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 for use in this project\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import requests\n",
    "import tweepy\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
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       },
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   },
   "outputs": [],
   "source": [
    "#Directly downloading the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)\n",
    "twitter_archive=pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url='https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "response=requests.get(url)\n",
    "try:\n",
    "    response.content\n",
    "    with open(os.path.join(url.split('/')[-1]), mode='wb') as file:\n",
    "        file.write(response.content)\n",
    "except requests.exceptions.ConnectionError as e:\n",
    "    response = \"No response\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions=pd.read_csv('image-predictions.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Use the Tweepy library to query additional data via the Twitter API (tweet_json.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumer_key = ''\n",
    "consumer_secret = ''\n",
    "access_token = ''\n",
    "access_secret = ''\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)"
   ]
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    }
   ],
   "source": [
    "#extracting each tweet status via their tweet_id in the twitter_archive data set\n",
    "tweet_list=[]\n",
    "tweet_list_err=[]\n",
    "for ids in twitter_archive.tweet_id:\n",
    "    try:\n",
    "        tweet_list.append(api.get_status(ids, tweet_mode= 'extended'))\n",
    "        print(ids,' : success')\n",
    "    except tweepy.TweepError as e:\n",
    "        tweet_list_err.append(ids)\n",
    "        print (ids,' : failed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of tweets that failed to download\n",
    "len(tweet_list_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[888202515573088257,\n",
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       " 802247111496568832,\n",
       " 801958328846974976,\n",
       " 779123168116150273,\n",
       " 775096608509886464,\n",
       " 771004394259247104,\n",
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       " 766864461642756096,\n",
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       " 759566828574212096,\n",
       " 754011816964026368,\n",
       " 680055455951884288]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list of tweet ids of tweets that failed to downloaded \n",
    "tweet_list_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2326"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of tweets that downloaded successfully\n",
    "len(tweet_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a list 'json_data'\n",
    "#storing the downloaded tweets in json_data\n",
    "#reference: https://stackoverflow.com/questions/47612822/how-to-create-pandas-dataframe-from-twitter-search-api\n",
    "json_data= []\n",
    "for ids in tweet_list:\n",
    "    json_data.append(ids._json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing the list in 'tweet_json.txt' file\n",
    "#reference: https://stackoverflow.com/questions/47612822/how-to-create-pandas-dataframe-from-twitter-search-api\n",
    "with open('tweet_json.txt', 'w') as file:\n",
    "        file.write(json.dumps(json_data, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a list 'retweet_fav_count'\n",
    "#extracting the retweet count, favourite count, and ids from the 'tweet_json.txt' file\n",
    "#appending the extracted data to the list \"retweet_fav_count\", and reading the data into a pandas dataframe 'json_data'\n",
    "#reference: https://stackoverflow.com/questions/47612822/how-to-create-pandas-dataframe-from-twitter-search-api\n",
    "retweet_fav_count = []\n",
    "with open('tweet_json.txt', encoding='utf-8') as json_file:  \n",
    "    json_tweet_data = json.load(json_file)\n",
    "    for individual_tweets in json_tweet_data:\n",
    "        tweet_id = individual_tweets['id']\n",
    "        favorite_count = individual_tweets['favorite_count']\n",
    "        retweet_count = individual_tweets['retweet_count']\n",
    "        retweet_fav_count.append({'tweet_id': str(tweet_id),'favorite_count': int(favorite_count),'retweet_count': int(retweet_count)})\n",
    "        json_data = pd.DataFrame(retweet_fav_count, columns = ['tweet_id', 'favorite_count','retweet_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing dataframe into csv file\n",
    "json_data.to_csv('json_data.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 28,
        "width": 4
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       "report_default": {
        "hidden": false
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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": [
    "### Visual and Programmatic Assessment of Individual Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visual assessment of twitter_archive dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>https://twitter.com/dog_rates/status/862457590...</td>\n",
       "      <td>13</td>\n",
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       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tucker. He is 100% ready for the sport...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/669006782...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Tucker</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>684567543613382656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-06 02:49:55 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Bobby. He doesn't give a damn about pe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/684567543...</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2186</th>\n",
       "      <td>668981893510119424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-24 02:38:07 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Unique dog here. Oddly shaped tail. Long pink ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668981893...</td>\n",
       "      <td>4</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>1494</th>\n",
       "      <td>692568918515392513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-28 04:44:32 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Chester. He's been guarding this pumpk...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/692568918...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Chester</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>708469915515297792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-12 01:49:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Bobble. He's a Croatian Galifianakis. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/708469915...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Bobble</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\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>868</th>\n",
       "      <td>761750502866649088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-06 02:27:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: \"Tristan do not speak to me wit...</td>\n",
       "      <td>6.853251e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-01-08 05:00:14 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/685325112...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>680934982542561280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-27 02:15:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Say hello to Sadie. She's a Tortellini Sidewin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/680934982...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Sadie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>838561493054533637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-03-06 01:26:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Walter. His owner has been watching al...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/838561493...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Walter</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>762464539388485633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-08 01:44:46 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Buddy. His father was a bear and his m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/762464539...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Buddy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>672609152938721280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-04 02:51:33 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Caryl. Likes to get in the microwave. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/672609152...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Caryl</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>666345417576210432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 20:01:42 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Look at this jokester thinking seat belt laws ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666345417...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>667524857454854144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 02:08:22 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>Another topnotch dog. His name is Big Jumpy Ra...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667524857...</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>227</th>\n",
       "      <td>848324959059550208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-02 00:03:26 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Odin. He's supposed to be giving directio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/848324959...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Odin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>813157409116065792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-25 23:00:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Layla. It is her first Christmas. She ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/813157409...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Layla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>707610948723478529</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-09 16:56:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Harper. She scraped her elbow attempti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/707610948...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Harper</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>680805554198020098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-26 17:41:07 +0000</td>\n",
       "      <td>&lt;a href=\"http://vine.co\" rel=\"nofollow\"&gt;Vine -...</td>\n",
       "      <td>This guy's dog broke. So sad. 9/10 would still...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://vine.co/v/iAP0Ugzi2PO</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>884441805382717440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-10 15:58:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>I present to you, Pup in Hat. Pup in Hat is gr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/884441805...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>718246886998687744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-08 01:19:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Alexanderson. He's got a weird ass bir...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/718246886...</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Alexanderson</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>673213039743795200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-05 18:51:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Dexter. He just got some big news. 10/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/673213039...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Dexter</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>710140971284037632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-16 16:29:35 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tater. His underbite is fierce af. Doe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/710140971...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Tater</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>744995568523612160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-20 20:49:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Abby. She got her face stuck in a glas...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/744995568...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Abby</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>759099523532779520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-29 18:53:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Toby. He has a drinking problem. Inflatab...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/759099523...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Toby</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>837471256429613056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-03-03 01:14:41 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Vincent. He's suave as h*ck. Will be y...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/837471256...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Vincent</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",
       "2136  670055038660800512                    NaN                  NaN   \n",
       "2263  667544320556335104                    NaN                  NaN   \n",
       "153   862457590147678208                    NaN                  NaN   \n",
       "2346  666058600524156928                    NaN                  NaN   \n",
       "1322  706291001778950144                    NaN                  NaN   \n",
       "1177  719704490224398336                    NaN                  NaN   \n",
       "2179  669006782128353280                    NaN                  NaN   \n",
       "1629  684567543613382656                    NaN                  NaN   \n",
       "2186  668981893510119424                    NaN                  NaN   \n",
       "1494  692568918515392513                    NaN                  NaN   \n",
       "1285  708469915515297792                    NaN                  NaN   \n",
       "0     892420643555336193                    NaN                  NaN   \n",
       "868   761750502866649088                    NaN                  NaN   \n",
       "1702  680934982542561280                    NaN                  NaN   \n",
       "287   838561493054533637                    NaN                  NaN   \n",
       "864   762464539388485633                    NaN                  NaN   \n",
       "1993  672609152938721280                    NaN                  NaN   \n",
       "2332  666345417576210432                    NaN                  NaN   \n",
       "2267  667524857454854144                    NaN                  NaN   \n",
       "227   848324959059550208                    NaN                  NaN   \n",
       "496   813157409116065792                    NaN                  NaN   \n",
       "1302  707610948723478529                    NaN                  NaN   \n",
       "1706  680805554198020098                    NaN                  NaN   \n",
       "41    884441805382717440                    NaN                  NaN   \n",
       "1189  718246886998687744                    NaN                  NaN   \n",
       "1973  673213039743795200                    NaN                  NaN   \n",
       "1262  710140971284037632                    NaN                  NaN   \n",
       "1035  744995568523612160                    NaN                  NaN   \n",
       "896   759099523532779520                    NaN                  NaN   \n",
       "295   837471256429613056                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "2136  2015-11-27 01:42:24 +0000   \n",
       "2263  2015-11-20 03:25:43 +0000   \n",
       "153   2017-05-11 00:01:27 +0000   \n",
       "2346  2015-11-16 01:01:59 +0000   \n",
       "1322  2016-03-06 01:31:11 +0000   \n",
       "1177  2016-04-12 01:51:36 +0000   \n",
       "2179  2015-11-24 04:17:01 +0000   \n",
       "1629  2016-01-06 02:49:55 +0000   \n",
       "2186  2015-11-24 02:38:07 +0000   \n",
       "1494  2016-01-28 04:44:32 +0000   \n",
       "1285  2016-03-12 01:49:25 +0000   \n",
       "0     2017-08-01 16:23:56 +0000   \n",
       "868   2016-08-06 02:27:27 +0000   \n",
       "1702  2015-12-27 02:15:25 +0000   \n",
       "287   2017-03-06 01:26:54 +0000   \n",
       "864   2016-08-08 01:44:46 +0000   \n",
       "1993  2015-12-04 02:51:33 +0000   \n",
       "2332  2015-11-16 20:01:42 +0000   \n",
       "2267  2015-11-20 02:08:22 +0000   \n",
       "227   2017-04-02 00:03:26 +0000   \n",
       "496   2016-12-25 23:00:08 +0000   \n",
       "1302  2016-03-09 16:56:11 +0000   \n",
       "1706  2015-12-26 17:41:07 +0000   \n",
       "41    2017-07-10 15:58:53 +0000   \n",
       "1189  2016-04-08 01:19:36 +0000   \n",
       "1973  2015-12-05 18:51:11 +0000   \n",
       "1262  2016-03-16 16:29:35 +0000   \n",
       "1035  2016-06-20 20:49:19 +0000   \n",
       "896   2016-07-29 18:53:24 +0000   \n",
       "295   2017-03-03 01:14:41 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "2136  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2263  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "153   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2346  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1322  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1177  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2179  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1629  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2186  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1494  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1285  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "0     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "868   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1702  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "287   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "864   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1993  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2332  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2267  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "227   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "496   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1302  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1706  <a href=\"http://vine.co\" rel=\"nofollow\">Vine -...   \n",
       "41    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1189  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1973  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1262  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1035  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "896   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "295   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "2136  This is Tommy. He's a cool dog. Hard not to st...                  NaN   \n",
       "2263  This is Kial. Kial is either wearing a cape, w...                  NaN   \n",
       "153   This is Jersey. He likes to watch movies, but ...                  NaN   \n",
       "2346  Here is the Rand Paul of retrievers folks! He'...                  NaN   \n",
       "1322  When you're just relaxin and having a swell ti...                  NaN   \n",
       "1177  This is Clyde. He's making sure you're having ...                  NaN   \n",
       "2179  This is Tucker. He is 100% ready for the sport...                  NaN   \n",
       "1629  This is Bobby. He doesn't give a damn about pe...                  NaN   \n",
       "2186  Unique dog here. Oddly shaped tail. Long pink ...                  NaN   \n",
       "1494  This is Chester. He's been guarding this pumpk...                  NaN   \n",
       "1285  This is Bobble. He's a Croatian Galifianakis. ...                  NaN   \n",
       "0     This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "868   RT @dog_rates: \"Tristan do not speak to me wit...         6.853251e+17   \n",
       "1702  Say hello to Sadie. She's a Tortellini Sidewin...                  NaN   \n",
       "287   This is Walter. His owner has been watching al...                  NaN   \n",
       "864   This is Buddy. His father was a bear and his m...                  NaN   \n",
       "1993  This is Caryl. Likes to get in the microwave. ...                  NaN   \n",
       "2332  Look at this jokester thinking seat belt laws ...                  NaN   \n",
       "2267  Another topnotch dog. His name is Big Jumpy Ra...                  NaN   \n",
       "227   Meet Odin. He's supposed to be giving directio...                  NaN   \n",
       "496   This is Layla. It is her first Christmas. She ...                  NaN   \n",
       "1302  This is Harper. She scraped her elbow attempti...                  NaN   \n",
       "1706  This guy's dog broke. So sad. 9/10 would still...                  NaN   \n",
       "41    I present to you, Pup in Hat. Pup in Hat is gr...                  NaN   \n",
       "1189  This is Alexanderson. He's got a weird ass bir...                  NaN   \n",
       "1973  This is Dexter. He just got some big news. 10/...                  NaN   \n",
       "1262  This is Tater. His underbite is fierce af. Doe...                  NaN   \n",
       "1035  This is Abby. She got her face stuck in a glas...                  NaN   \n",
       "896   Meet Toby. He has a drinking problem. Inflatab...                  NaN   \n",
       "295   This is Vincent. He's suave as h*ck. Will be y...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "2136                       NaN                        NaN   \n",
       "2263                       NaN                        NaN   \n",
       "153                        NaN                        NaN   \n",
       "2346                       NaN                        NaN   \n",
       "1322                       NaN                        NaN   \n",
       "1177                       NaN                        NaN   \n",
       "2179                       NaN                        NaN   \n",
       "1629                       NaN                        NaN   \n",
       "2186                       NaN                        NaN   \n",
       "1494                       NaN                        NaN   \n",
       "1285                       NaN                        NaN   \n",
       "0                          NaN                        NaN   \n",
       "868               4.196984e+09  2016-01-08 05:00:14 +0000   \n",
       "1702                       NaN                        NaN   \n",
       "287                        NaN                        NaN   \n",
       "864                        NaN                        NaN   \n",
       "1993                       NaN                        NaN   \n",
       "2332                       NaN                        NaN   \n",
       "2267                       NaN                        NaN   \n",
       "227                        NaN                        NaN   \n",
       "496                        NaN                        NaN   \n",
       "1302                       NaN                        NaN   \n",
       "1706                       NaN                        NaN   \n",
       "41                         NaN                        NaN   \n",
       "1189                       NaN                        NaN   \n",
       "1973                       NaN                        NaN   \n",
       "1262                       NaN                        NaN   \n",
       "1035                       NaN                        NaN   \n",
       "896                        NaN                        NaN   \n",
       "295                        NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "2136  https://twitter.com/dog_rates/status/670055038...                 3   \n",
       "2263  https://twitter.com/dog_rates/status/667544320...                10   \n",
       "153   https://twitter.com/dog_rates/status/862457590...                13   \n",
       "2346  https://twitter.com/dog_rates/status/666058600...                 8   \n",
       "1322  https://twitter.com/dog_rates/status/706291001...                11   \n",
       "1177  https://twitter.com/dog_rates/status/719704490...                12   \n",
       "2179  https://twitter.com/dog_rates/status/669006782...                12   \n",
       "1629  https://twitter.com/dog_rates/status/684567543...                 4   \n",
       "2186  https://twitter.com/dog_rates/status/668981893...                 4   \n",
       "1494  https://twitter.com/dog_rates/status/692568918...                12   \n",
       "1285  https://twitter.com/dog_rates/status/708469915...                11   \n",
       "0     https://twitter.com/dog_rates/status/892420643...                13   \n",
       "868   https://twitter.com/dog_rates/status/685325112...                10   \n",
       "1702  https://twitter.com/dog_rates/status/680934982...                10   \n",
       "287   https://twitter.com/dog_rates/status/838561493...                13   \n",
       "864   https://twitter.com/dog_rates/status/762464539...                12   \n",
       "1993  https://twitter.com/dog_rates/status/672609152...                 9   \n",
       "2332  https://twitter.com/dog_rates/status/666345417...                10   \n",
       "2267  https://twitter.com/dog_rates/status/667524857...                12   \n",
       "227   https://twitter.com/dog_rates/status/848324959...                12   \n",
       "496   https://twitter.com/dog_rates/status/813157409...                12   \n",
       "1302  https://twitter.com/dog_rates/status/707610948...                12   \n",
       "1706                      https://vine.co/v/iAP0Ugzi2PO                 9   \n",
       "41    https://twitter.com/dog_rates/status/884441805...                14   \n",
       "1189  https://twitter.com/dog_rates/status/718246886...                 3   \n",
       "1973  https://twitter.com/dog_rates/status/673213039...                10   \n",
       "1262  https://twitter.com/dog_rates/status/710140971...                 8   \n",
       "1035  https://twitter.com/dog_rates/status/744995568...                 9   \n",
       "896   https://twitter.com/dog_rates/status/759099523...                 7   \n",
       "295   https://twitter.com/dog_rates/status/837471256...                12   \n",
       "\n",
       "      rating_denominator          name doggo floofer  pupper  puppo  \n",
       "2136                  10         Tommy  None    None    None   None  \n",
       "2263                  10          Kial  None    None    None   None  \n",
       "153                   10        Jersey  None    None    None   None  \n",
       "2346                  10           the  None    None    None   None  \n",
       "1322                  10          None  None    None    None   None  \n",
       "1177                  10         Clyde  None    None  pupper   None  \n",
       "2179                  10        Tucker  None    None    None   None  \n",
       "1629                  10         Bobby  None    None    None   None  \n",
       "2186                  10          None  None    None    None   None  \n",
       "1494                  10       Chester  None    None    None   None  \n",
       "1285                  10        Bobble  None    None    None   None  \n",
       "0                     10       Phineas  None    None    None   None  \n",
       "868                   10          None  None    None    None   None  \n",
       "1702                  10         Sadie  None    None    None   None  \n",
       "287                   10        Walter  None    None    None   None  \n",
       "864                   10         Buddy  None    None    None   None  \n",
       "1993                  10         Caryl  None    None    None   None  \n",
       "2332                  10          None  None    None    None   None  \n",
       "2267                  10          None  None    None    None   None  \n",
       "227                   10          Odin  None    None    None   None  \n",
       "496                   10         Layla  None    None    None   None  \n",
       "1302                  10        Harper  None    None    None   None  \n",
       "1706                  10          None  None    None    None   None  \n",
       "41                    10          None  None    None    None   None  \n",
       "1189                  10  Alexanderson  None    None    None   None  \n",
       "1973                  10        Dexter  None    None    None   None  \n",
       "1262                  10         Tater  None    None    None   None  \n",
       "1035                  10          Abby  None    None    None  puppo  \n",
       "896                   10          Toby  None    None    None   None  \n",
       "295                   10       Vincent  None    None    None   None  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visual assessment of twitter_archive dataset\n",
    "twitter_archive.sample(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### programmatic assessment of twitter_archive dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "#programmatic assessment of twitter_archive dataset\n",
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>     2221\n",
       "<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>                          91\n",
       "<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>                       33\n",
       "<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>      11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Here we have Pancho and Peaches. Pancho is a Condoleezza Gryffindor, and Peaches is just an asshole. 10/10 &amp; 7/10 https://t.co/Lh1BsJrWPp                          1\n",
       "RT @dog_rates: Say hello to Maddie and Gunner. They are considerably pupset about bath time. Both 12/10 but Gunner needs your help\\n\\nhttps:/…                         1\n",
       "This is Margo. She just dug pup a massive hole. Can't wait for you to see it. H*ckin proud of herself. 12/10 would forgive then pet https://t.co/H38HB6rBTx            1\n",
       "RT @dog_rates: This is Bruce. He never backs down from a challenge. 11/10 you got this Bruce https://t.co/aI7umZHIq7                                                   1\n",
       "Here's a doggo fully pupared for a shower. H*ckin exquisite balance. Sneaky tongue slip too. 13/10 https://t.co/UtEVnQ1ZPg                                             1\n",
       "This is Burt. He thinks your thesis statement is comically underdeveloped. 12/10 intellectual af https://t.co/jH6EN9cEn6                                               1\n",
       "Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12                                                           1\n",
       "This is Jed. He may be the fanciest pupper in the game right now. Knows it too. 13/10 would sign modeling contract https://t.co/0YplNnSMEm                             1\n",
       "This is Cooper. He only wakes up to switch gears. 12/10 helpful af https://t.co/EEIkAGVY64                                                                             1\n",
       "This is the saddest/sweetest/best picture I've been sent. 12/10 😢🐶 https://t.co/vQ2Lw1BLBF                                                                             1\n",
       "This is Gabe. He is a southern Baklava. Gabe has always wanted to fit in with the other bananas. 10/10 fabulous https://t.co/3LZrJzg3BJ                                1\n",
       "We don't rate penguins, but if we did, this one would get 12/10 https://t.co/cEORXhwZ5K                                                                                1\n",
       "This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe                                                 1\n",
       "This is Tino. He really likes corndogs. 9/10 https://t.co/cUxGtnBfc2                                                                                                   1\n",
       "Vine will be deeply missed. This was by far my favorite one. 14/10 https://t.co/roqIxCvEB3                                                                             1\n",
       "Guys please stop sending pictures without any dogs in th- oh never mind hello excuse me sir. 12/10 stealthy as h*ck https://t.co/brCQoqc8AW                            1\n",
       "RT @dog_rates: We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10…                           1\n",
       "oh h*ck 10/10 https://t.co/bC69RrW559                                                                                                                                  1\n",
       "This is Skittles. I would kidnap Skittles. Pink dog in back hasn't moved in days. 12/10 https://t.co/2wm0POA9N2                                                        1\n",
       "This is Charlie. He pouts until he gets to go on the swing. 12/10 manipulative af https://t.co/ilwQqWFKCh                                                              1\n",
       "Meet Jeb &amp; Bush. Jeb is somehow stuck in that fence and Bush won't stop whispering sweet nothings in his ear. 9/10s https://t.co/NRNExUy9Hm                        1\n",
       "Unique dog here. Wrinkly as hell. Weird segmented neck. Finger on fire. Doesn't seem to notice. 5/10 might still pet https://t.co/Hy9La4xNX3                           1\n",
       "This is Reese. He likes holding hands. 12/10 https://t.co/cbLroGCbmh                                                                                                   1\n",
       "I've been informed by multiple sources that this is actually a dog elf who's tired from helping Santa all night. Pupgraded to 12/10                                    1\n",
       "This is Ester. He has a cocaine problem. This is an intervention Ester. We all care about you. 8/10 https://t.co/eCVj2xT59V                                            1\n",
       "Guys, we only rate dogs. This is quite clearly a bulbasaur. Please only send dogs. Thank you... 12/10 human used pet, it's super effective https://t.co/Xc7uj1C64x     1\n",
       "This is Ronnie. He hopes you're having a great day. Nifty tongue slip. 12/10 would pat head approvingly https://t.co/4fY2bsAm65                                        1\n",
       "This is Penny. She's an OU cheerleader. About to do a triple back handspring down the stairs. 11/10 hype af https://t.co/B2f3XkGU5c                                    1\n",
       "This is Charlie. He's a West Side Niddlewog. Mucho fluffy. 12/10 would pet so damn well https://t.co/B9dOrmnPVt                                                        1\n",
       "This is Oliver. Bath time is upon him. His fear of the wetness postpones his ultimate pupper destiny. 11/10 https://t.co/AFzzKqR4tT                                    1\n",
       "                                                                                                                                                                      ..\n",
       "Here is a heartbreaking scene of an incredible pupper being laid to rest. 10/10 RIP pupper https://t.co/81mvJ0rGRu                                                     1\n",
       "Sorry for the lack of posts today. I came home from school and had to spend quality time with my puppo. Her name is Zoey and she's 13/10 https://t.co/BArWupFAn0       1\n",
       "When you're given AUX cord privileges from the back seat and accidentally start blasting an audiobook... both 10/10 https://t.co/gCCrY8P0K9                            1\n",
       "This is Sansa. She's gotten too big for her chair. Not so smol anymore. 11/10 once a pupper, always a pupper https://t.co/IpAoztle2s                                   1\n",
       "Never seen a shark hold another shark like this before. Must be evolving. Both 10/10 please only send dogs though https://t.co/x4IUNKV79Y                              1\n",
       "This is Ava. She just blasted off. Streamline af. Aerodynamic as h*ck. One small step for pupper, one giant leap for pupkind. 12/10 https://t.co/W4KffrdX3Q            1\n",
       "Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S                            1\n",
       "This is Chet. He's having a hard time. Really struggling. 7/10 hang in there pupper https://t.co/eb4ta0xtnd                                                            1\n",
       "Ermergerd 12/10 https://t.co/PQni2sjPsm                                                                                                                                1\n",
       "This is Walter. His owner has been watching all the Iditarod coverage and is convinced Walter can be a sled dog. 13/10 Walter isn't so sure https://t.co/0av1PEehFI    1\n",
       "This is Ollie Vue. He was a 3 legged pupper on a mission to overcome everything. This is very hard to write. 14/10 we will miss you Ollie https://t.co/qTRY2qX9y4      1\n",
       "RT @dog_rates: This is Phil. He's an important dog. Can control the seasons. Magical as hell. 12/10 would let him sign my forehead https://…                           1\n",
       "When you see sophomores in high school driving. 11/10 https://t.co/m6aC8d1Kzp                                                                                          1\n",
       "Never doubt a doggo 14/10 https://t.co/AbBLh2FZCH                                                                                                                      1\n",
       "Instead of the usual nightly dog rate, I'm sharing this story with you. Meeko is 13/10 and would like your help \\n\\nhttps://t.co/Mj4j6QoIJk https://t.co/JdNE5oqYEV    1\n",
       "This is Bertson. He just wants to say hi. 11/10 would boop nose https://t.co/hwv7Wq6gDA                                                                                1\n",
       "This is Ivar. She is a badass Viking warrior. Will sack your village. 10/10 savage af https://t.co/Dz6MiVssVU                                                          1\n",
       "RT @dog_rates: This is Meyer. He has to hold somebody's hand during car rides. He's also wearing a seatbelt. 12/10 responsible af https://t…                           1\n",
       "This is Marvin. He can tie a bow tie better than me. 11/10 https://t.co/81kzPgqjQ3                                                                                     1\n",
       "Remarkable dog here. Walks on back legs really well. Looks extra soft. 8/10 would cuddle with https://t.co/gpWLdbposg                                                  1\n",
       "Meet Goliath. He's an example of irony. Head is phenomenally round. Wants to be an ornament. 12/10 would hug gently https://t.co/72Dil0Aktw                            1\n",
       "This is Koda. He is a South Australian deckshark. Deceptively deadly. Frighteningly majestic. 13/10 would risk a petting #BarkWeek https://t.co/dVPW0B0Mme             1\n",
       "This is Eevee. She wants to see how you're doing. Just checkin pup on you. She hopes you're doing okay. 12/10 extremely good girl https://t.co/nqAJGCHKEt              1\n",
       "Meet Grady. He's very hungry. Too bad no one can find his food bowl. 9/10 poor pupper https://t.co/oToIkYnEGn                                                          1\n",
       "Meet Tripp. He's being eaten by a sherk and doesn't even care. Unfazed af. 11/10 keep doin you Tripp https://t.co/gGxjthmG1c                                           1\n",
       "Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl                                                                                      1\n",
       "Say hello to Kenneth. He likes Reese's Puffs. 10/10 https://t.co/6RHNRsByOY                                                                                            1\n",
       "This is Rocco. He's doing his best. 13/10 someone help him (IG: rocco_roni) https://t.co/qFsl1nnXMv                                                                    1\n",
       "Everybody needs to read this. Jack is our first 14/10. Truly heroic pupper https://t.co/3m6bNGXWnM                                                                     1\n",
       "Here's a sleepy Christmas pupper 11/10 https://t.co/KXg0f8GNQ9                                                                                                         1\n",
       "Name: text, Length: 2356, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.text.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      558\n",
       "11      464\n",
       "10      461\n",
       "13      351\n",
       "9       158\n",
       "8       102\n",
       "7        55\n",
       "14       54\n",
       "5        37\n",
       "6        32\n",
       "3        19\n",
       "4        17\n",
       "1         9\n",
       "2         9\n",
       "420       2\n",
       "0         2\n",
       "15        2\n",
       "75        2\n",
       "80        1\n",
       "20        1\n",
       "24        1\n",
       "26        1\n",
       "44        1\n",
       "50        1\n",
       "60        1\n",
       "165       1\n",
       "84        1\n",
       "88        1\n",
       "144       1\n",
       "182       1\n",
       "143       1\n",
       "666       1\n",
       "960       1\n",
       "1776      1\n",
       "17        1\n",
       "27        1\n",
       "45        1\n",
       "99        1\n",
       "121       1\n",
       "204       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Phineas', 'Tilly', 'Archie', 'Darla', 'Franklin', 'None', 'Jax',\n",
       "       'Zoey', 'Cassie', 'Koda', 'Bruno', 'Ted', 'Stuart', 'Oliver', 'Jim',\n",
       "       'Zeke', 'Ralphus', 'Canela', 'Gerald', 'Jeffrey', 'such', 'Maya',\n",
       "       'Mingus', 'Derek', 'Roscoe', 'Waffles', 'Jimbo', 'Maisey', 'Lilly',\n",
       "       'Earl', 'Lola', 'Kevin', 'Yogi', 'Noah', 'Bella', 'Grizzwald',\n",
       "       'Rusty', 'Gus', 'Stanley', 'Alfy', 'Koko', 'Rey', 'Gary', 'a',\n",
       "       'Elliot', 'Louis', 'Jesse', 'Romeo', 'Bailey', 'Duddles', 'Jack',\n",
       "       'Emmy', 'Steven', 'Beau', 'Snoopy', 'Shadow', 'Terrance', 'Aja',\n",
       "       'Penny', 'Dante', 'Nelly', 'Ginger', 'Benedict', 'Venti', 'Goose',\n",
       "       'Nugget', 'Cash', 'Coco', 'Jed', 'Sebastian', 'Walter', 'Sierra',\n",
       "       'Monkey', 'Harry', 'Kody', 'Lassie', 'Rover', 'Napolean', 'Dawn',\n",
       "       'Boomer', 'Cody', 'Rumble', 'Clifford', 'quite', 'Dewey', 'Scout',\n",
       "       'Gizmo', 'Cooper', 'Harold', 'Shikha', 'Jamesy', 'Lili', 'Sammy',\n",
       "       'Meatball', 'Paisley', 'Albus', 'Neptune', 'Quinn', 'Belle',\n",
       "       'Zooey', 'Dave', 'Jersey', 'Hobbes', 'Burt', 'Lorenzo', 'Carl',\n",
       "       'Jordy', 'Milky', 'Trooper', 'Winston', 'Sophie', 'Wyatt', 'Rosie',\n",
       "       'Thor', 'Oscar', 'Luna', 'Callie', 'Cermet', 'George', 'Marlee',\n",
       "       'Arya', 'Einstein', 'Alice', 'Rumpole', 'Benny', 'Aspen', 'Jarod',\n",
       "       'Wiggles', 'General', 'Sailor', 'Astrid', 'Iggy', 'Snoop', 'Kyle',\n",
       "       'Leo', 'Riley', 'Gidget', 'Noosh', 'Odin', 'Jerry', 'Charlie',\n",
       "       'Georgie', 'Rontu', 'Cannon', 'Furzey', 'Daisy', 'Tuck', 'Barney',\n",
       "       'Vixen', 'Jarvis', 'Mimosa', 'Pickles', 'Bungalo', 'Brady', 'Margo',\n",
       "       'Sadie', 'Hank', 'Tycho', 'Stephan', 'Indie', 'Winnie', 'Bentley',\n",
       "       'Ken', 'Max', 'Maddie', 'Pipsy', 'Monty', 'Sojourner', 'Odie',\n",
       "       'Arlo', 'Sunny', 'Vincent', 'Lucy', 'Clark', 'Mookie', 'Meera',\n",
       "       'Buddy', 'Ava', 'Rory', 'Eli', 'Ash', 'Tucker', 'Tobi', 'Chester',\n",
       "       'Wilson', 'Sunshine', 'Lipton', 'Gabby', 'Bronte', 'Poppy', 'Rhino',\n",
       "       'Willow', 'not', 'Orion', 'Eevee', 'Smiley', 'Logan', 'Moreton',\n",
       "       'Klein', 'Miguel', 'Emanuel', 'Kuyu', 'Dutch', 'Pete', 'Scooter',\n",
       "       'Reggie', 'Kyro', 'Samson', 'Loki', 'Mia', 'Malcolm', 'Dexter',\n",
       "       'Alfie', 'Fiona', 'one', 'Mutt', 'Bear', 'Doobert', 'Beebop',\n",
       "       'Alexander', 'Sailer', 'Brutus', 'Kona', 'Boots', 'Ralphie', 'Phil',\n",
       "       'Cupid', 'Pawnd', 'Pilot', 'Ike', 'Mo', 'Toby', 'Sweet', 'Pablo',\n",
       "       'Nala', 'Balto', 'Crawford', 'Gabe', 'Mattie', 'Jimison',\n",
       "       'Hercules', 'Duchess', 'Harlso', 'Sampson', 'Sundance', 'Luca',\n",
       "       'Flash', 'Finn', 'Peaches', 'Howie', 'Jazzy', 'Anna', 'Bo',\n",
       "       'Seamus', 'Wafer', 'Chelsea', 'Tom', 'Moose', 'Florence', 'Autumn',\n",
       "       'Dido', 'Eugene', 'Herschel', 'Strudel', 'Tebow', 'Chloe', 'Betty',\n",
       "       'Timber', 'Binky', 'Dudley', 'Comet', 'Larry', 'Levi', 'Akumi',\n",
       "       'Titan', 'Olivia', 'Alf', 'Oshie', 'Bruce', 'Chubbs', 'Sky',\n",
       "       'Atlas', 'Eleanor', 'Layla', 'Rocky', 'Baron', 'Tyr', 'Bauer',\n",
       "       'Swagger', 'Brandi', 'Mary', 'Moe', 'Halo', 'Augie', 'Craig', 'Sam',\n",
       "       'Hunter', 'Pavlov', 'Maximus', 'Wallace', 'Ito', 'Milo', 'Ollie',\n",
       "       'Cali', 'Lennon', 'incredibly', 'Major', 'Duke', 'Reginald',\n",
       "       'Sansa', 'Shooter', 'Django', 'Diogi', 'Sonny', 'Philbert',\n",
       "       'Marley', 'Severus', 'Ronnie', 'Anakin', 'Bones', 'Mauve', 'Chef',\n",
       "       'Doc', 'Sobe', 'Longfellow', 'Mister', 'Iroh', 'Baloo', 'Stubert',\n",
       "       'Paull', 'Timison', 'Davey', 'Pancake', 'Tyrone', 'Snicku', 'Ruby',\n",
       "       'Brody', 'Rizzy', 'Mack', 'Butter', 'Nimbus', 'Laika', 'Dobby',\n",
       "       'Juno', 'Maude', 'Lily', 'Newt', 'Benji', 'Nida', 'Robin',\n",
       "       'Monster', 'BeBe', 'Remus', 'Mabel', 'Misty', 'Happy', 'Mosby',\n",
       "       'Maggie', 'Leela', 'Ralphy', 'Brownie', 'Meyer', 'Stella', 'mad',\n",
       "       'Frank', 'Tonks', 'Lincoln', 'Oakley', 'Dale', 'Rizzo', 'Arnie',\n",
       "       'Pinot', 'Dallas', 'Hero', 'Frankie', 'Stormy', 'Mairi', 'Loomis',\n",
       "       'Godi', 'Kenny', 'Deacon', 'Timmy', 'Harper', 'Chipson', 'Combo',\n",
       "       'Dash', 'Bell', 'Hurley', 'Jay', 'Mya', 'Strider', 'an', 'Wesley',\n",
       "       'Solomon', 'Huck', 'very', 'O', 'Blue', 'Finley', 'Sprinkles',\n",
       "       'Heinrich', 'Shakespeare', 'Fizz', 'Chip', 'Grey', 'Roosevelt',\n",
       "       'Gromit', 'Willem', 'Dakota', 'Dixie', 'Al', 'Jackson', 'just',\n",
       "       'Carbon', 'DonDon', 'Kirby', 'Lou', 'Nollie', 'Chevy', 'Tito',\n",
       "       'Louie', 'Rupert', 'Rufus', 'Brudge', 'Shadoe', 'Colby', 'Angel',\n",
       "       'Brat', 'Tove', 'my', 'Aubie', 'Kota', 'Eve', 'Glenn', 'Shelby',\n",
       "       'Sephie', 'Bonaparte', 'Albert', 'Wishes', 'Rose', 'Theo', 'Rocco',\n",
       "       'Fido', 'Emma', 'Spencer', 'Lilli', 'Boston', 'Brandonald', 'Corey',\n",
       "       'Leonard', 'Chompsky', 'Beckham', 'Devón', 'Gert', 'Watson',\n",
       "       'Rubio', 'Keith', 'Dex', 'Carly', 'Ace', 'Tayzie', 'Grizzie',\n",
       "       'Fred', 'Gilbert', 'Zoe', 'Stewie', 'Calvin', 'Lilah', 'Spanky',\n",
       "       'Jameson', 'Piper', 'Atticus', 'Blu', 'Dietrich', 'Divine', 'Tripp',\n",
       "       'his', 'Cora', 'Huxley', 'Keurig', 'Bookstore', 'Linus', 'Abby',\n",
       "       'Shaggy', 'Shiloh', 'Gustav', 'Arlen', 'Percy', 'Lenox', 'Sugar',\n",
       "       'Harvey', 'Blanket', 'actually', 'Geno', 'Stark', 'Beya', 'Kilo',\n",
       "       'Kayla', 'Maxaroni', 'Doug', 'Edmund', 'Aqua', 'Theodore', 'Chase',\n",
       "       'getting', 'Rorie', 'Simba', 'Charles', 'Bayley', 'Axel',\n",
       "       'Storkson', 'Remy', 'Chadrick', 'Kellogg', 'Buckley', 'Livvie',\n",
       "       'Terry', 'Hermione', 'Ralpher', 'Aldrick', 'this', 'unacceptable',\n",
       "       'Rooney', 'Crystal', 'Ziva', 'Stefan', 'Pupcasso', 'Puff',\n",
       "       'Flurpson', 'Coleman', 'Enchilada', 'Raymond', 'all', 'Rueben',\n",
       "       'Cilantro', 'Karll', 'Sprout', 'Blitz', 'Bloop', 'Lillie',\n",
       "       'Ashleigh', 'Kreggory', 'Sarge', 'Luther', 'Ivar', 'Jangle',\n",
       "       'Schnitzel', 'Panda', 'Berkeley', 'Ralphé', 'Charleson', 'Clyde',\n",
       "       'Harnold', 'Sid', 'Pippa', 'Otis', 'Carper', 'Bowie',\n",
       "       'Alexanderson', 'Suki', 'Barclay', 'Skittle', 'Ebby', 'Flávio',\n",
       "       'Smokey', 'Link', 'Jennifur', 'Ozzy', 'Bluebert', 'Stephanus',\n",
       "       'Bubbles', 'old', 'Zeus', 'Bertson', 'Nico', 'Michelangelope',\n",
       "       'Siba', 'Calbert', 'Curtis', 'Travis', 'Thumas', 'Kanu', 'Lance',\n",
       "       'Opie', 'Kane', 'Olive', 'Chuckles', 'Staniel', 'Sora', 'Beemo',\n",
       "       'Gunner', 'infuriating', 'Lacy', 'Tater', 'Olaf', 'Cecil', 'Vince',\n",
       "       'Karma', 'Billy', 'Walker', 'Rodney', 'Klevin', 'Malikai', 'Bobble',\n",
       "       'River', 'Jebberson', 'Remington', 'Farfle', 'Jiminus', 'Clarkus',\n",
       "       'Finnegus', 'Cupcake', 'Kathmandu', 'Ellie', 'Katie', 'Kara',\n",
       "       'Adele', 'Zara', 'Ambrose', 'Jimothy', 'Bode', 'Terrenth', 'Reese',\n",
       "       'Chesterson', 'Lucia', 'Bisquick', 'Ralphson', 'Socks', 'Rambo',\n",
       "       'Rudy', 'Fiji', 'Rilo', 'Bilbo', 'Coopson', 'Yoda', 'Millie',\n",
       "       'Chet', 'Crouton', 'Daniel', 'Kaia', 'Murphy', 'Dotsy', 'Eazy',\n",
       "       'Coops', 'Fillup', 'Miley', 'Charl', 'Reagan', 'Yukon', 'CeCe',\n",
       "       'Cuddles', 'Claude', 'Jessiga', 'Carter', 'Ole', 'Pherb', 'Blipson',\n",
       "       'Reptar', 'Trevith', 'Berb', 'Bob', 'Colin', 'Brian', 'Oliviér',\n",
       "       'Grady', 'Kobe', 'Freddery', 'Bodie', 'Dunkin', 'Wally', 'Tupawc',\n",
       "       'Amber', 'Edgar', 'Teddy', 'Kingsley', 'Brockly', 'Richie', 'Molly',\n",
       "       'Vinscent', 'Cedrick', 'Hazel', 'Lolo', 'Eriq', 'Phred', 'the',\n",
       "       'Oddie', 'Maxwell', 'Geoff', 'Covach', 'Durg', 'Fynn', 'Ricky',\n",
       "       'Herald', 'Lucky', 'Ferg', 'Trip', 'Clarence', 'Hamrick', 'Brad',\n",
       "       'Pubert', 'Frönq', 'Derby', 'Lizzie', 'Ember', 'Blakely', 'Opal',\n",
       "       'Marq', 'Kramer', 'Barry', 'Gordon', 'Baxter', 'Mona', 'Horace',\n",
       "       'Crimson', 'Birf', 'Hammond', 'Lorelei', 'Marty', 'Brooks',\n",
       "       'Petrick', 'Hubertson', 'Gerbald', 'Oreo', 'Bruiser', 'Perry',\n",
       "       'Bobby', 'Jeph', 'Obi', 'Tino', 'Kulet', 'Sweets', 'Lupe', 'Tiger',\n",
       "       'Jiminy', 'Griffin', 'Banjo', 'Brandy', 'Lulu', 'Darrel', 'Taco',\n",
       "       'Joey', 'Patrick', 'Kreg', 'Todo', 'Tess', 'Ulysses', 'Toffee',\n",
       "       'Apollo', 'Asher', 'Glacier', 'Chuck', 'Champ', 'Ozzie', 'Griswold',\n",
       "       'Cheesy', 'Moofasa', 'Hector', 'Goliath', 'Kawhi', 'by', 'Emmie',\n",
       "       'Penelope', 'Willie', 'Rinna', 'Mike', 'William', 'Dwight', 'Evy',\n",
       "       'officially', 'Rascal', 'Linda', 'Tug', 'Tango', 'Grizz', 'Jerome',\n",
       "       'Crumpet', 'Jessifer', 'Izzy', 'Ralph', 'Sandy', 'Humphrey',\n",
       "       'Tassy', 'Juckson', 'Chuq', 'Tyrus', 'Karl', 'Godzilla', 'Vinnie',\n",
       "       'Kenneth', 'Herm', 'Bert', 'Striker', 'Donny', 'Pepper', 'Bernie',\n",
       "       'Buddah', 'Lenny', 'Arnold', 'Zuzu', 'Mollie', 'Laela', 'Tedders',\n",
       "       'Superpup', 'Rufio', 'Jeb', 'Rodman', 'Jonah', 'Chesney', 'life',\n",
       "       'Henry', 'Bobbay', 'Mitch', 'Kaiya', 'Acro', 'Aiden', 'Obie', 'Dot',\n",
       "       'Shnuggles', 'Kendall', 'Jeffri', 'Steve', 'Mac', 'Fletcher',\n",
       "       'Kenzie', 'Pumpkin', 'Schnozz', 'Gustaf', 'Cheryl', 'Ed',\n",
       "       'Leonidas', 'Norman', 'Caryl', 'Scott', 'Taz', 'Darby', 'Jackie',\n",
       "       'light', 'Jazz', 'Franq', 'Pippin', 'Rolf', 'Snickers', 'Ridley',\n",
       "       'Cal', 'Bradley', 'Bubba', 'Tuco', 'Patch', 'Mojo', 'Batdog',\n",
       "       'Dylan', 'space', 'Mark', 'JD', 'Alejandro', 'Scruffers', 'Pip',\n",
       "       'Julius', 'Tanner', 'Sparky', 'Anthony', 'Holly', 'Jett', 'Amy',\n",
       "       'Sage', 'Andy', 'Mason', 'Trigger', 'Antony', 'Creg', 'Traviss',\n",
       "       'Gin', 'Jeffrie', 'Danny', 'Ester', 'Pluto', 'Bloo', 'Edd', 'Willy',\n",
       "       'Herb', 'Damon', 'Peanut', 'Nigel', 'Butters', 'Sandra', 'Fabio',\n",
       "       'Randall', 'Liam', 'Tommy', 'Ben', 'Raphael', 'Julio', 'Andru',\n",
       "       'Kloey', 'Shawwn', 'Skye', 'Kollin', 'Ronduh', 'Billl', 'Saydee',\n",
       "       'Dug', 'Tessa', 'Sully', 'Kirk', 'Ralf', 'Clarq', 'Jaspers',\n",
       "       'Samsom', 'Harrison', 'Chaz', 'Jeremy', 'Jaycob', 'Lambeau',\n",
       "       'Ruffles', 'Amélie', 'Bobb', 'Banditt', 'Kevon', 'Winifred', 'Hanz',\n",
       "       'Churlie', 'Zeek', 'Timofy', 'Maks', 'Jomathan', 'Kallie', 'Marvin',\n",
       "       'Spark', 'Gòrdón', 'Jo', 'DayZ', 'Jareld', 'Torque', 'Ron',\n",
       "       'Skittles', 'Cleopatricia', 'Erik', 'Stu', 'Tedrick', 'Filup',\n",
       "       'Kial', 'Naphaniel', 'Dook', 'Hall', 'Philippe', 'Biden', 'Fwed',\n",
       "       'Genevieve', 'Joshwa', 'Bradlay', 'Clybe', 'Keet', 'Carll',\n",
       "       'Jockson', 'Josep', 'Lugan', 'Christoper'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2259\n",
       "doggo      97\n",
       "Name: doggo, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.doggo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       2346\n",
       "floofer      10\n",
       "Name: floofer, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.floofer.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None      2099\n",
       "pupper     257\n",
       "Name: pupper, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.pupper.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2326\n",
       "puppo      30\n",
       "Name: puppo, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.puppo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2278\n",
       "in_reply_to_user_id           2278\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           2175\n",
       "retweeted_status_user_id      2175\n",
       "retweeted_status_timestamp    2175\n",
       "expanded_urls                   59\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                             0\n",
       "doggo                            0\n",
       "floofer                          0\n",
       "pupper                           0\n",
       "puppo                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issues in The Twitter Archive Dataset\n",
    "1. Some dog names are not actual names\n",
    "2. full html link should be replaced with the actual source in the source column\n",
    "3. remove retweets by dropping rows with values in the retweeted_status_id column\n",
    "4. drop the following columns: in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id column, retweeted_status_user_id, retweeted_status_timestamp, text.\n",
    "5. after spliting timestamp column, drop the timestamp column \n",
    "6. drop pupper, doggo, puppo and floofer columns after merging into one \"dog_stage\"\n",
    "7. drop expanded_urls column\n",
    "\n",
    "### Tidiness Issues in The Twitter Archive Dataset\n",
    "1. The timestamp column should be split into date and time columns, and dtype of the date column should be changed to datetime\n",
    "2. doggo, floofer, pupper, and puppo columns should be merged into one (dog_stage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visual assessment of image_predictions dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
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       "      <th>p2_conf</th>\n",
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       "      <th>1948</th>\n",
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       "      <td>https://pbs.twimg.com/media/C_lkieeVwAAm0L4.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Australian_terrier</td>\n",
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       "    <tr>\n",
       "      <th>1354</th>\n",
       "      <td>760252756032651264</td>\n",
       "      <td>https://pbs.twimg.com/media/Coz12OLWgAADdys.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>radio_telescope</td>\n",
       "      <td>0.155279</td>\n",
       "      <td>False</td>\n",
       "      <td>dam</td>\n",
       "      <td>0.154515</td>\n",
       "      <td>False</td>\n",
       "      <td>crane</td>\n",
       "      <td>0.098040</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>707969809498152960</td>\n",
       "      <td>https://pbs.twimg.com/media/CdM2xRpXEAUsR4k.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.908491</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.082652</td>\n",
       "      <td>True</td>\n",
       "      <td>teddy</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>760656994973933572</td>\n",
       "      <td>https://pbs.twimg.com/media/Co5lf-KW8AAIwJw.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.760546</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.232079</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1816</th>\n",
       "      <td>833863086058651648</td>\n",
       "      <td>https://pbs.twimg.com/media/C5J6DIpWQAEosSz.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.494969</td>\n",
       "      <td>True</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.312632</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.141736</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>677530072887205888</td>\n",
       "      <td>https://pbs.twimg.com/media/CWcSAI-WUAAOB9W.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.689259</td>\n",
       "      <td>True</td>\n",
       "      <td>Norwegian_elkhound</td>\n",
       "      <td>0.026121</td>\n",
       "      <td>True</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.023075</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>842535590457499648</td>\n",
       "      <td>https://pbs.twimg.com/media/C7FJpgVW4AIDzi6.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.685084</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.314608</td>\n",
       "      <td>True</td>\n",
       "      <td>basenji</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>698703483621523456</td>\n",
       "      <td>https://pbs.twimg.com/media/CbJLG0HWwAAV-ug.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Brittany_spaniel</td>\n",
       "      <td>0.931963</td>\n",
       "      <td>True</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.030695</td>\n",
       "      <td>True</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.012896</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>875</th>\n",
       "      <td>698195409219559425</td>\n",
       "      <td>https://pbs.twimg.com/media/CbB9BTqW8AEVc2A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.643690</td>\n",
       "      <td>True</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.102684</td>\n",
       "      <td>True</td>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.050008</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>707776935007539200</td>\n",
       "      <td>https://pbs.twimg.com/media/CdKHWimWoAABs08.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.890426</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.051335</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.018015</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>684800227459624960</td>\n",
       "      <td>https://pbs.twimg.com/media/CYDmK7ZVAAI_ylL.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_schnauzer</td>\n",
       "      <td>0.294457</td>\n",
       "      <td>True</td>\n",
       "      <td>Norfolk_terrier</td>\n",
       "      <td>0.161885</td>\n",
       "      <td>True</td>\n",
       "      <td>West_Highland_white_terrier</td>\n",
       "      <td>0.120992</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>674764817387900928</td>\n",
       "      <td>https://pbs.twimg.com/media/CV0_BSuWIAIvE9k.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.634695</td>\n",
       "      <td>True</td>\n",
       "      <td>Arctic_fox</td>\n",
       "      <td>0.309853</td>\n",
       "      <td>False</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.019641</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>708810915978854401</td>\n",
       "      <td>https://pbs.twimg.com/media/CdYzwuYUIAAHPkB.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.976139</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.016301</td>\n",
       "      <td>True</td>\n",
       "      <td>Norfolk_terrier</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>668975677807423489</td>\n",
       "      <td>https://pbs.twimg.com/media/CUit1O1WoAEBHjj.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.605437</td>\n",
       "      <td>True</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.184783</td>\n",
       "      <td>True</td>\n",
       "      <td>Saint_Bernard</td>\n",
       "      <td>0.116299</td>\n",
       "      <td>True</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>0.000201</td>\n",
       "      <td>False</td>\n",
       "      <td>oscilloscope</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>711732680602345472</td>\n",
       "      <td>https://pbs.twimg.com/media/CeCVGEbUYAASeY4.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>dingo</td>\n",
       "      <td>0.366875</td>\n",
       "      <td>False</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.334929</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.073876</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>870308999962521604</td>\n",
       "      <td>https://pbs.twimg.com/media/DBP1asiUAAEKZI5.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Greater_Swiss_Mountain_dog</td>\n",
       "      <td>0.622752</td>\n",
       "      <td>True</td>\n",
       "      <td>Appenzeller</td>\n",
       "      <td>0.158463</td>\n",
       "      <td>True</td>\n",
       "      <td>EntleBucher</td>\n",
       "      <td>0.148115</td>\n",
       "      <td>True</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>0.258858</td>\n",
       "      <td>False</td>\n",
       "      <td>panpipe</td>\n",
       "      <td>0.021563</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>672082170312290304</td>\n",
       "      <td>https://pbs.twimg.com/media/CVO3KodXAAAj1de.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>hamster</td>\n",
       "      <td>0.132440</td>\n",
       "      <td>False</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.123962</td>\n",
       "      <td>True</td>\n",
       "      <td>bubble</td>\n",
       "      <td>0.056212</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>821407182352777218</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/82140...</td>\n",
       "      <td>1</td>\n",
       "      <td>Irish_setter</td>\n",
       "      <td>0.505496</td>\n",
       "      <td>True</td>\n",
       "      <td>vizsla</td>\n",
       "      <td>0.168747</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.111311</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>872620804844003328</td>\n",
       "      <td>https://pbs.twimg.com/media/DBwr_hzXkAEnZBW.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.513191</td>\n",
       "      <td>True</td>\n",
       "      <td>Sussex_spaniel</td>\n",
       "      <td>0.159088</td>\n",
       "      <td>True</td>\n",
       "      <td>standard_poodle</td>\n",
       "      <td>0.149509</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>766078092750233600</td>\n",
       "      <td>https://pbs.twimg.com/media/ChK1tdBWwAQ1flD.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.420463</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.132640</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.121523</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>671882082306625538</td>\n",
       "      <td>https://pbs.twimg.com/media/CVMBL_LWUAAsvrL.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>ski_mask</td>\n",
       "      <td>0.968325</td>\n",
       "      <td>False</td>\n",
       "      <td>mask</td>\n",
       "      <td>0.021863</td>\n",
       "      <td>False</td>\n",
       "      <td>abaya</td>\n",
       "      <td>0.005479</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>668248472370458624</td>\n",
       "      <td>https://pbs.twimg.com/media/CUYYcMfXAAAixe7.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.734547</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.068294</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.046367</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>685321586178670592</td>\n",
       "      <td>https://pbs.twimg.com/media/CYLAWFMWMAEcRzb.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Boston_bull</td>\n",
       "      <td>0.972483</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.025469</td>\n",
       "      <td>True</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>668542336805281792</td>\n",
       "      <td>https://pbs.twimg.com/media/CUcjtL8WUAAAJoz.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.267695</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.254050</td>\n",
       "      <td>True</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.212381</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>690360449368465409</td>\n",
       "      <td>https://pbs.twimg.com/media/CZSnKw8WwAAAN7q.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.686933</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.076359</td>\n",
       "      <td>True</td>\n",
       "      <td>Brabancon_griffon</td>\n",
       "      <td>0.035007</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>845397057150107648</td>\n",
       "      <td>https://pbs.twimg.com/media/C7t0IzLWkAINoft.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Dandie_Dinmont</td>\n",
       "      <td>0.394404</td>\n",
       "      <td>True</td>\n",
       "      <td>Maltese_dog</td>\n",
       "      <td>0.186537</td>\n",
       "      <td>True</td>\n",
       "      <td>West_Highland_white_terrier</td>\n",
       "      <td>0.181985</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                            jpg_url  \\\n",
       "1948  862831371563274240    https://pbs.twimg.com/media/C_lkieeVwAAm0L4.jpg   \n",
       "1354  760252756032651264    https://pbs.twimg.com/media/Coz12OLWgAADdys.jpg   \n",
       "989   707969809498152960    https://pbs.twimg.com/media/CdM2xRpXEAUsR4k.jpg   \n",
       "1358  760656994973933572    https://pbs.twimg.com/media/Co5lf-KW8AAIwJw.jpg   \n",
       "1816  833863086058651648    https://pbs.twimg.com/media/C5J6DIpWQAEosSz.jpg   \n",
       "2073  892177421306343426    https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "549   677530072887205888    https://pbs.twimg.com/media/CWcSAI-WUAAOB9W.jpg   \n",
       "1861  842535590457499648    https://pbs.twimg.com/media/C7FJpgVW4AIDzi6.jpg   \n",
       "881   698703483621523456    https://pbs.twimg.com/media/CbJLG0HWwAAV-ug.jpg   \n",
       "3     666044226329800704    https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "875   698195409219559425    https://pbs.twimg.com/media/CbB9BTqW8AEVc2A.jpg   \n",
       "988   707776935007539200    https://pbs.twimg.com/media/CdKHWimWoAABs08.jpg   \n",
       "700   684800227459624960    https://pbs.twimg.com/media/CYDmK7ZVAAI_ylL.jpg   \n",
       "454   674764817387900928    https://pbs.twimg.com/media/CV0_BSuWIAIvE9k.jpg   \n",
       "1002  708810915978854401    https://pbs.twimg.com/media/CdYzwuYUIAAHPkB.jpg   \n",
       "164   668975677807423489    https://pbs.twimg.com/media/CUit1O1WoAEBHjj.jpg   \n",
       "94    667550882905632768    https://pbs.twimg.com/media/CUObvUJVEAAnYPF.jpg   \n",
       "1034  711732680602345472    https://pbs.twimg.com/media/CeCVGEbUYAASeY4.jpg   \n",
       "1976  870308999962521604    https://pbs.twimg.com/media/DBP1asiUAAEKZI5.jpg   \n",
       "140   668544745690562560    https://pbs.twimg.com/media/CUcl5jeWsAA6ufS.jpg   \n",
       "329   672082170312290304    https://pbs.twimg.com/media/CVO3KodXAAAj1de.jpg   \n",
       "1733  821407182352777218  https://pbs.twimg.com/ext_tw_video_thumb/82140...   \n",
       "1987  872620804844003328    https://pbs.twimg.com/media/DBwr_hzXkAEnZBW.jpg   \n",
       "1387  766078092750233600    https://pbs.twimg.com/media/ChK1tdBWwAQ1flD.jpg   \n",
       "325   671882082306625538    https://pbs.twimg.com/media/CVMBL_LWUAAsvrL.jpg   \n",
       "125   668248472370458624    https://pbs.twimg.com/media/CUYYcMfXAAAixe7.jpg   \n",
       "712   685321586178670592    https://pbs.twimg.com/media/CYLAWFMWMAEcRzb.jpg   \n",
       "139   668542336805281792    https://pbs.twimg.com/media/CUcjtL8WUAAAJoz.jpg   \n",
       "786   690360449368465409    https://pbs.twimg.com/media/CZSnKw8WwAAAN7q.jpg   \n",
       "1874  845397057150107648    https://pbs.twimg.com/media/C7t0IzLWkAINoft.jpg   \n",
       "\n",
       "      img_num                              p1   p1_conf  p1_dog  \\\n",
       "1948        2              Australian_terrier  0.207281    True   \n",
       "1354        1                 radio_telescope  0.155279   False   \n",
       "989         1                      toy_poodle  0.908491    True   \n",
       "1358        1                golden_retriever  0.760546    True   \n",
       "1816        1                          kuvasz  0.494969    True   \n",
       "2073        1                       Chihuahua  0.323581    True   \n",
       "549         1       Staffordshire_bullterrier  0.689259    True   \n",
       "1861        1                        Pembroke  0.685084    True   \n",
       "881         1                Brittany_spaniel  0.931963    True   \n",
       "3           1             Rhodesian_ridgeback  0.408143    True   \n",
       "875         1              Labrador_retriever  0.643690    True   \n",
       "988         1              miniature_pinscher  0.890426    True   \n",
       "700         1             miniature_schnauzer  0.294457    True   \n",
       "454         2                         Samoyed  0.634695    True   \n",
       "1002        2                golden_retriever  0.976139    True   \n",
       "164         1                          basset  0.605437    True   \n",
       "94          1                        web_site  0.998258   False   \n",
       "1034        3                           dingo  0.366875   False   \n",
       "1976        2      Greater_Swiss_Mountain_dog  0.622752    True   \n",
       "140         1                        bearskin  0.427870   False   \n",
       "329         1                         hamster  0.132440   False   \n",
       "1733        1                    Irish_setter  0.505496    True   \n",
       "1987        1                  cocker_spaniel  0.513191    True   \n",
       "1387        1                      toy_poodle  0.420463    True   \n",
       "325         1                        ski_mask  0.968325   False   \n",
       "125         1                       Chihuahua  0.734547    True   \n",
       "712         1                     Boston_bull  0.972483    True   \n",
       "139         1  American_Staffordshire_terrier  0.267695    True   \n",
       "786         1                             pug  0.686933    True   \n",
       "1874        1                  Dandie_Dinmont  0.394404    True   \n",
       "\n",
       "                                  p2   p2_conf  p2_dog  \\\n",
       "1948                   Irish_terrier  0.156296    True   \n",
       "1354                             dam  0.154515   False   \n",
       "989                 miniature_poodle  0.082652    True   \n",
       "1358              Labrador_retriever  0.232079    True   \n",
       "1816                  Great_Pyrenees  0.312632    True   \n",
       "2073                        Pekinese  0.090647    True   \n",
       "549               Norwegian_elkhound  0.026121    True   \n",
       "1861                        Cardigan  0.314608    True   \n",
       "881           Welsh_springer_spaniel  0.030695    True   \n",
       "3                            redbone  0.360687    True   \n",
       "875   American_Staffordshire_terrier  0.102684    True   \n",
       "988                      toy_terrier  0.051335    True   \n",
       "700                  Norfolk_terrier  0.161885    True   \n",
       "454                       Arctic_fox  0.309853   False   \n",
       "1002              Labrador_retriever  0.016301    True   \n",
       "164           Welsh_springer_spaniel  0.184783    True   \n",
       "94                        dishwasher  0.000201   False   \n",
       "1034                    Ibizan_hound  0.334929    True   \n",
       "1976                     Appenzeller  0.158463    True   \n",
       "140                              bow  0.258858   False   \n",
       "329                       toy_poodle  0.123962    True   \n",
       "1733                          vizsla  0.168747    True   \n",
       "1987                  Sussex_spaniel  0.159088    True   \n",
       "1387                miniature_poodle  0.132640    True   \n",
       "325                             mask  0.021863   False   \n",
       "125               miniature_pinscher  0.068294    True   \n",
       "712                   French_bulldog  0.025469    True   \n",
       "139                   French_bulldog  0.254050    True   \n",
       "786                   French_bulldog  0.076359    True   \n",
       "1874                     Maltese_dog  0.186537    True   \n",
       "\n",
       "                                  p3   p3_conf  p3_dog  \n",
       "1948                 German_shepherd  0.123536    True  \n",
       "1354                           crane  0.098040   False  \n",
       "989                            teddy  0.005786   False  \n",
       "1358                         redbone  0.002874    True  \n",
       "1816                golden_retriever  0.141736    True  \n",
       "2073                        papillon  0.068957    True  \n",
       "549   American_Staffordshire_terrier  0.023075    True  \n",
       "1861                         basenji  0.000160    True  \n",
       "881                           beagle  0.012896    True  \n",
       "3                 miniature_pinscher  0.222752    True  \n",
       "875                        dalmatian  0.050008    True  \n",
       "988                        Chihuahua  0.018015    True  \n",
       "700      West_Highland_white_terrier  0.120992    True  \n",
       "454                           kuvasz  0.019641    True  \n",
       "1002                 Norfolk_terrier  0.001871    True  \n",
       "164                    Saint_Bernard  0.116299    True  \n",
       "94                      oscilloscope  0.000142   False  \n",
       "1034                      Eskimo_dog  0.073876    True  \n",
       "1976                     EntleBucher  0.148115    True  \n",
       "140                          panpipe  0.021563   False  \n",
       "329                           bubble  0.056212   False  \n",
       "1733        Chesapeake_Bay_retriever  0.111311    True  \n",
       "1987                 standard_poodle  0.149509    True  \n",
       "1387        Chesapeake_Bay_retriever  0.121523    True  \n",
       "325                            abaya  0.005479   False  \n",
       "125                      toy_terrier  0.046367    True  \n",
       "712                            boxer  0.000458    True  \n",
       "139        Staffordshire_bullterrier  0.212381    True  \n",
       "786                Brabancon_griffon  0.035007    True  \n",
       "1874     West_Highland_white_terrier  0.181985    True  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visual assessment of image_predictions dataset\n",
    "image_predictions.sample(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### programmatic assessment of image_predictions dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "image_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1780\n",
       "2     198\n",
       "3      66\n",
       "4      31\n",
       "Name: img_num, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.img_num.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id    0\n",
       "jpg_url     0\n",
       "img_num     0\n",
       "p1          0\n",
       "p1_conf     0\n",
       "p1_dog      0\n",
       "p2          0\n",
       "p2_conf     0\n",
       "p2_dog      0\n",
       "p3          0\n",
       "p3_conf     0\n",
       "p3_dog      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever             150\n",
       "Labrador_retriever           100\n",
       "Pembroke                      89\n",
       "Chihuahua                     83\n",
       "pug                           57\n",
       "chow                          44\n",
       "Samoyed                       43\n",
       "toy_poodle                    39\n",
       "Pomeranian                    38\n",
       "malamute                      30\n",
       "cocker_spaniel                30\n",
       "French_bulldog                26\n",
       "miniature_pinscher            23\n",
       "Chesapeake_Bay_retriever      23\n",
       "seat_belt                     22\n",
       "Siberian_husky                20\n",
       "Staffordshire_bullterrier     20\n",
       "German_shepherd               20\n",
       "web_site                      19\n",
       "Cardigan                      19\n",
       "Eskimo_dog                    18\n",
       "Maltese_dog                   18\n",
       "Shetland_sheepdog             18\n",
       "teddy                         18\n",
       "beagle                        18\n",
       "Rottweiler                    17\n",
       "Shih-Tzu                      17\n",
       "Lakeland_terrier              17\n",
       "kuvasz                        16\n",
       "Italian_greyhound             16\n",
       "                            ... \n",
       "African_hunting_dog            1\n",
       "bighorn                        1\n",
       "bonnet                         1\n",
       "military_uniform               1\n",
       "cheeseburger                   1\n",
       "cowboy_boot                    1\n",
       "book_jacket                    1\n",
       "maillot                        1\n",
       "nail                           1\n",
       "tailed_frog                    1\n",
       "Egyptian_cat                   1\n",
       "trombone                       1\n",
       "black-footed_ferret            1\n",
       "otter                          1\n",
       "pole                           1\n",
       "crane                          1\n",
       "silky_terrier                  1\n",
       "shopping_basket                1\n",
       "minibus                        1\n",
       "alp                            1\n",
       "banana                         1\n",
       "china_cabinet                  1\n",
       "fountain                       1\n",
       "washer                         1\n",
       "leopard                        1\n",
       "sandbar                        1\n",
       "bib                            1\n",
       "sulphur-crested_cockatoo       1\n",
       "microwave                      1\n",
       "water_bottle                   1\n",
       "Name: p1, Length: 378, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.p1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     1532\n",
       "False     543\n",
       "Name: p1_dog, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.p1_dog.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Labrador_retriever                104\n",
       "golden_retriever                   92\n",
       "Cardigan                           73\n",
       "Chihuahua                          44\n",
       "Pomeranian                         42\n",
       "French_bulldog                     41\n",
       "Chesapeake_Bay_retriever           41\n",
       "toy_poodle                         37\n",
       "cocker_spaniel                     34\n",
       "miniature_poodle                   33\n",
       "Siberian_husky                     33\n",
       "beagle                             28\n",
       "Eskimo_dog                         27\n",
       "collie                             27\n",
       "Pembroke                           27\n",
       "kuvasz                             26\n",
       "Italian_greyhound                  22\n",
       "American_Staffordshire_terrier     21\n",
       "Pekinese                           21\n",
       "chow                               20\n",
       "miniature_pinscher                 20\n",
       "toy_terrier                        20\n",
       "Samoyed                            20\n",
       "malinois                           20\n",
       "Norwegian_elkhound                 19\n",
       "Boston_bull                        19\n",
       "Staffordshire_bullterrier          18\n",
       "Irish_terrier                      17\n",
       "pug                                17\n",
       "kelpie                             16\n",
       "                                 ... \n",
       "hamper                              1\n",
       "lampshade                           1\n",
       "peacock                             1\n",
       "bib                                 1\n",
       "cowboy_hat                          1\n",
       "Madagascar_cat                      1\n",
       "lifeboat                            1\n",
       "tree_frog                           1\n",
       "snorkel                             1\n",
       "chain_mail                          1\n",
       "African_hunting_dog                 1\n",
       "cloak                               1\n",
       "spatula                             1\n",
       "trench_coat                         1\n",
       "komondor                            1\n",
       "academic_gown                       1\n",
       "pier                                1\n",
       "lesser_panda                        1\n",
       "paper_towel                         1\n",
       "stove                               1\n",
       "dam                                 1\n",
       "hay                                 1\n",
       "junco                               1\n",
       "spotted_salamander                  1\n",
       "cardigan                            1\n",
       "bagel                               1\n",
       "shower_cap                          1\n",
       "armadillo                           1\n",
       "hummingbird                         1\n",
       "stingray                            1\n",
       "Name: p2, Length: 405, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.p2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     1553\n",
       "False     522\n",
       "Name: p2_dog, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.p2_dog.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Labrador_retriever                79\n",
       "Chihuahua                         58\n",
       "golden_retriever                  48\n",
       "Eskimo_dog                        38\n",
       "kelpie                            35\n",
       "kuvasz                            34\n",
       "Staffordshire_bullterrier         32\n",
       "chow                              32\n",
       "beagle                            31\n",
       "cocker_spaniel                    31\n",
       "toy_poodle                        29\n",
       "Pekinese                          29\n",
       "Pomeranian                        29\n",
       "Pembroke                          27\n",
       "Great_Pyrenees                    27\n",
       "Chesapeake_Bay_retriever          27\n",
       "malamute                          26\n",
       "French_bulldog                    26\n",
       "American_Staffordshire_terrier    24\n",
       "Cardigan                          23\n",
       "pug                               23\n",
       "basenji                           21\n",
       "bull_mastiff                      20\n",
       "toy_terrier                       20\n",
       "Siberian_husky                    19\n",
       "Shetland_sheepdog                 17\n",
       "Boston_bull                       17\n",
       "doormat                           16\n",
       "boxer                             16\n",
       "Lakeland_terrier                  16\n",
       "                                  ..\n",
       "traffic_light                      1\n",
       "grand_piano                        1\n",
       "snail                              1\n",
       "space_shuttle                      1\n",
       "broccoli                           1\n",
       "toyshop                            1\n",
       "cardoon                            1\n",
       "conch                              1\n",
       "paintbrush                         1\n",
       "oscilloscope                       1\n",
       "park_bench                         1\n",
       "Sussex_spaniel                     1\n",
       "red_wolf                           1\n",
       "drumstick                          1\n",
       "ibex                               1\n",
       "wombat                             1\n",
       "French_horn                        1\n",
       "wok                                1\n",
       "mushroom                           1\n",
       "cuirass                            1\n",
       "bow                                1\n",
       "plunger                            1\n",
       "can_opener                         1\n",
       "lampshade                          1\n",
       "axolotl                            1\n",
       "banana                             1\n",
       "neck_brace                         1\n",
       "valley                             1\n",
       "electric_fan                       1\n",
       "Band_Aid                           1\n",
       "Name: p3, Length: 408, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.p3.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     1499\n",
       "False     576\n",
       "Name: p3_dog, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.p3_dog.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issues in The Image Predictions Dataset\n",
    "1. some dog breeds are not actual dog breeds. remove p1_dog, p2_dog, p3_dog values set as 'False: as they are not dogs of any breed\n",
    "\n",
    "\n",
    "### Tidiness Issues in The Image Predictions Dataset\n",
    "1. For each row, generate each maximum p_conf value and the corresponding p and p_dog values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visual assessment of json_data dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>1580</th>\n",
       "      <td>685547936038666240</td>\n",
       "      <td>30308</td>\n",
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       "      <th>1309</th>\n",
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       "      <td>2012</td>\n",
       "      <td>556</td>\n",
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       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>674262580978937856</td>\n",
       "      <td>1177</td>\n",
       "      <td>382</td>\n",
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       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>682429480204398592</td>\n",
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      "text/plain": [
       "                tweet_id  favorite_count  retweet_count\n",
       "1706  679722016581222400            1535            425\n",
       "1110  728015554473250816            3784            989\n",
       "2204  668190681446379520             575            171\n",
       "388   823719002937630720               0          10312\n",
       "1228  710296729921429505            2197            668\n",
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       "1383  698953797952008193            2477            824\n",
       "432   818646164899774465               0          19361\n",
       "187   854732716440526848           20626           5327\n",
       "2067  670733412878163972             848            446\n",
       "2271  667044094246576128             163             43\n",
       "1472  692142790915014657            1510            354\n",
       "1947  672988786805112832             871            253\n",
       "1593  684914660081053696            3213           1320\n",
       "1580  685547936038666240           30308          14080\n",
       "1309  705066031337840642            2012            556\n",
       "1891  674262580978937856            1177            382\n",
       "1640  682429480204398592            3175           1042\n",
       "546   802265048156610565            6601           1288\n",
       "2186  668537837512433665             226             61\n",
       "1783  676776431406465024            4509           1797\n",
       "1075  735137028879360001            2874            877\n",
       "945   750056684286914561            2946            817\n",
       "931   751205363882532864            5887           1659\n",
       "1744  678023323247357953            1739            340\n",
       "1342  702276748847800320            2277            694\n",
       "769   773670353721753600            5044           1220\n",
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       "1092  730924654643314689            5628           1781\n",
       "1140  720785406564900865            2860            698"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data.sample(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### programmatic assessment of json_data dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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 object\n",
      "favorite_count    2326 non-null int64\n",
      "retweet_count     2326 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 54.6+ KB\n"
     ]
    }
   ],
   "source": [
    "json_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id          0\n",
       "favorite_count    0\n",
       "retweet_count     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Quality Issues \n",
    "1. change data type of tweet_id column in all three datasets to string object before merging\n",
    "\n",
    "\n",
    "### Additional Tidiness Issues \n",
    "1. Merge all three datasets"
   ]
  },
  {
   "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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of original pieces of data\n",
    "t_archives=twitter_archive.copy()\n",
    "predictions=image_predictions.copy()\n",
    "json_copy=json_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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",
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       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
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       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
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       "      <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",
       "  </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",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <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",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  \n",
       "0                  10  Phineas  None    None   None  None  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archives.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog      p2   p2_conf  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True  collie  0.156665   \n",
       "\n",
       "   p2_dog                 p3   p3_conf  p3_dog  \n",
       "0    True  Shetland_sheepdog  0.061428    True  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>33697</td>\n",
       "      <td>6970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  favorite_count  retweet_count\n",
       "0  892420643555336193           33697           6970"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_copy.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 1: some names are not actual names, and they begin with lowercase characters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define: extract names beginning with uppercase characters, remove NaN and 'None' entries**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Phineas\n",
       "1          Tilly\n",
       "2         Archie\n",
       "3          Darla\n",
       "4       Franklin\n",
       "5           None\n",
       "6            Jax\n",
       "7           None\n",
       "8           Zoey\n",
       "9         Cassie\n",
       "10          Koda\n",
       "11         Bruno\n",
       "12          None\n",
       "13           Ted\n",
       "14        Stuart\n",
       "15        Oliver\n",
       "16           Jim\n",
       "17          Zeke\n",
       "18       Ralphus\n",
       "19        Canela\n",
       "20        Gerald\n",
       "21       Jeffrey\n",
       "22           NaN\n",
       "23        Canela\n",
       "24          None\n",
       "25          None\n",
       "26          Maya\n",
       "27        Mingus\n",
       "28         Derek\n",
       "29        Roscoe\n",
       "          ...   \n",
       "2326         NaN\n",
       "2327         NaN\n",
       "2328        None\n",
       "2329        None\n",
       "2330        None\n",
       "2331        None\n",
       "2332        None\n",
       "2333         NaN\n",
       "2334         NaN\n",
       "2335         NaN\n",
       "2336        None\n",
       "2337        None\n",
       "2338        None\n",
       "2339        None\n",
       "2340        None\n",
       "2341        None\n",
       "2342        None\n",
       "2343        None\n",
       "2344        None\n",
       "2345         NaN\n",
       "2346         NaN\n",
       "2347         NaN\n",
       "2348         NaN\n",
       "2349         NaN\n",
       "2350         NaN\n",
       "2351        None\n",
       "2352         NaN\n",
       "2353         NaN\n",
       "2354         NaN\n",
       "2355        None\n",
       "Name: name, Length: 2356, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract names beginning with uppercase characters\n",
    "t_archives.name = t_archives.name.str.extract('\\\\b([A-Z]\\\\S*)\\\\b')\n",
    "t_archives.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Phineas\n",
       "1            Tilly\n",
       "2           Archie\n",
       "3            Darla\n",
       "4         Franklin\n",
       "5             None\n",
       "6              Jax\n",
       "7             None\n",
       "8             Zoey\n",
       "9           Cassie\n",
       "10            Koda\n",
       "11           Bruno\n",
       "12            None\n",
       "13             Ted\n",
       "14          Stuart\n",
       "15          Oliver\n",
       "16             Jim\n",
       "17            Zeke\n",
       "18         Ralphus\n",
       "19          Canela\n",
       "20          Gerald\n",
       "21         Jeffrey\n",
       "23          Canela\n",
       "24            None\n",
       "25            None\n",
       "26            Maya\n",
       "27          Mingus\n",
       "28           Derek\n",
       "29          Roscoe\n",
       "30            None\n",
       "           ...    \n",
       "2310          None\n",
       "2312         Josep\n",
       "2313         Lugan\n",
       "2315    Christoper\n",
       "2316          None\n",
       "2317       Jimothy\n",
       "2318      Kreggory\n",
       "2319         Scout\n",
       "2320          None\n",
       "2321          None\n",
       "2322          None\n",
       "2323          None\n",
       "2324          None\n",
       "2325        Walter\n",
       "2328          None\n",
       "2329          None\n",
       "2330          None\n",
       "2331          None\n",
       "2332          None\n",
       "2336          None\n",
       "2337          None\n",
       "2338          None\n",
       "2339          None\n",
       "2340          None\n",
       "2341          None\n",
       "2342          None\n",
       "2343          None\n",
       "2344          None\n",
       "2351          None\n",
       "2355          None\n",
       "Name: name, Length: 2247, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop NaN values \n",
    "t_archives=t_archives.dropna(subset=['name'])\n",
    "t_archives.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Phineas\n",
       "1            Tilly\n",
       "2           Archie\n",
       "3            Darla\n",
       "4         Franklin\n",
       "6              Jax\n",
       "8             Zoey\n",
       "9           Cassie\n",
       "10            Koda\n",
       "11           Bruno\n",
       "13             Ted\n",
       "14          Stuart\n",
       "15          Oliver\n",
       "16             Jim\n",
       "17            Zeke\n",
       "18         Ralphus\n",
       "19          Canela\n",
       "20          Gerald\n",
       "21         Jeffrey\n",
       "23          Canela\n",
       "26            Maya\n",
       "27          Mingus\n",
       "28           Derek\n",
       "29          Roscoe\n",
       "31         Waffles\n",
       "33           Jimbo\n",
       "34          Maisey\n",
       "36           Lilly\n",
       "38            Earl\n",
       "39            Lola\n",
       "           ...    \n",
       "2268          Dook\n",
       "2270          Hall\n",
       "2271      Philippe\n",
       "2274         Reese\n",
       "2275       Cupcake\n",
       "2279         Biden\n",
       "2280          Fwed\n",
       "2282     Genevieve\n",
       "2283        Joshwa\n",
       "2286       Timison\n",
       "2289      Clarence\n",
       "2290       Kenneth\n",
       "2291       Churlie\n",
       "2292       Bradlay\n",
       "2293         Pipsy\n",
       "2295          Gabe\n",
       "2296         Clybe\n",
       "2297          Dave\n",
       "2300          Keet\n",
       "2302        Klevin\n",
       "2303         Carll\n",
       "2308          Jeph\n",
       "2309       Jockson\n",
       "2312         Josep\n",
       "2313         Lugan\n",
       "2315    Christoper\n",
       "2317       Jimothy\n",
       "2318      Kreggory\n",
       "2319         Scout\n",
       "2325        Walter\n",
       "Name: name, Length: 1502, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove name entries with 'None' as values\n",
    "t_archives=t_archives.query('name !=\"None\"')\n",
    "t_archives.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Charlie      12\n",
       "Lucy         11\n",
       "Cooper       11\n",
       "Oliver       11\n",
       "Tucker       10\n",
       "Penny        10\n",
       "Lola         10\n",
       "Winston       9\n",
       "Bo            9\n",
       "Sadie         8\n",
       "Buddy         7\n",
       "Toby          7\n",
       "Daisy         7\n",
       "Bailey        7\n",
       "Bella         6\n",
       "Scout         6\n",
       "Rusty         6\n",
       "Stanley       6\n",
       "Leo           6\n",
       "Koda          6\n",
       "Oscar         6\n",
       "Jax           6\n",
       "Milo          6\n",
       "Jack          6\n",
       "Dave          6\n",
       "Phil          5\n",
       "Sammy         5\n",
       "Larry         5\n",
       "George        5\n",
       "Oakley        5\n",
       "             ..\n",
       "Barry         1\n",
       "Halo          1\n",
       "Derby         1\n",
       "Remy          1\n",
       "Kawhi         1\n",
       "Grizzwald     1\n",
       "Barney        1\n",
       "Crimson       1\n",
       "Dot           1\n",
       "Binky         1\n",
       "William       1\n",
       "Simba         1\n",
       "Howie         1\n",
       "Ashleigh      1\n",
       "Dook          1\n",
       "Fillup        1\n",
       "Vinscent      1\n",
       "Carbon        1\n",
       "Bode          1\n",
       "Travis        1\n",
       "Leonard       1\n",
       "Ember         1\n",
       "Dallas        1\n",
       "Sojourner     1\n",
       "Rumble        1\n",
       "Berkeley      1\n",
       "Coleman       1\n",
       "Antony        1\n",
       "Comet         1\n",
       "Bookstore     1\n",
       "Name: name, Length: 931, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archives.name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 2: full html link should be replaced with the actual source in the source column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: replace html links with actual source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>     1436\n",
       "<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>                          41\n",
       "<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>                       16\n",
       "<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>       9\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archives.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives.source=t_archives.source.replace(['<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>'], 'Twitter for Iphone')\n",
    "t_archives.source=t_archives.source.replace(['<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>'], 'Vine- Make a Scene')\n",
    "t_archives.source=t_archives.source.replace(['<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>'], 'TweetDeck')\n",
    "t_archives.source=t_archives.source.replace(['<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>'], 'Twitter Web Client')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for Iphone    1436\n",
       "Vine- Make a Scene      41\n",
       "Twitter Web Client      16\n",
       "TweetDeck                9\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archives.source.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #3: remove retweets "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "#### Define: remove retweets by dropping rows with values in the retweeted_status_id column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives=t_archives.query('retweeted_status_id == \"NaN\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1391 entries, 0 to 2325\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      1391 non-null int64\n",
      "in_reply_to_status_id         1 non-null float64\n",
      "in_reply_to_user_id           1 non-null float64\n",
      "timestamp                     1391 non-null object\n",
      "source                        1391 non-null object\n",
      "text                          1391 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                 1391 non-null object\n",
      "rating_numerator              1391 non-null int64\n",
      "rating_denominator            1391 non-null int64\n",
      "name                          1391 non-null object\n",
      "doggo                         1391 non-null object\n",
      "floofer                       1391 non-null object\n",
      "pupper                        1391 non-null object\n",
      "puppo                         1391 non-null object\n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 195.6+ KB\n"
     ]
    }
   ],
   "source": [
    "t_archives.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Issue 4: drop the following columns: in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, text)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives=t_archives.drop(columns=['in_reply_to_status_id', 'in_reply_to_user_id', 'retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp','text'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1391 entries, 0 to 2325\n",
      "Data columns (total 11 columns):\n",
      "tweet_id              1391 non-null int64\n",
      "timestamp             1391 non-null object\n",
      "source                1391 non-null object\n",
      "expanded_urls         1391 non-null object\n",
      "rating_numerator      1391 non-null int64\n",
      "rating_denominator    1391 non-null int64\n",
      "name                  1391 non-null object\n",
      "doggo                 1391 non-null object\n",
      "floofer               1391 non-null object\n",
      "pupper                1391 non-null object\n",
      "puppo                 1391 non-null object\n",
      "dtypes: int64(3), object(8)\n",
      "memory usage: 130.4+ KB\n"
     ]
    }
   ],
   "source": [
    "t_archives.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 5: The timestamp column should be split into two separate columns (date, and time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create empty columns for date and time\n",
    "t_archives['date']=\" \"\n",
    "t_archives['time']=\" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1391 entries, 0 to 2325\n",
      "Data columns (total 13 columns):\n",
      "tweet_id              1391 non-null int64\n",
      "timestamp             1391 non-null object\n",
      "source                1391 non-null object\n",
      "expanded_urls         1391 non-null object\n",
      "rating_numerator      1391 non-null int64\n",
      "rating_denominator    1391 non-null int64\n",
      "name                  1391 non-null object\n",
      "doggo                 1391 non-null object\n",
      "floofer               1391 non-null object\n",
      "pupper                1391 non-null object\n",
      "puppo                 1391 non-null object\n",
      "date                  1391 non-null object\n",
      "time                  1391 non-null object\n",
      "dtypes: int64(3), object(10)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "t_archives.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives['date']=pd.to_datetime(t_archives.timestamp).dt.date.astype('datetime64[ns]')\n",
    "t_archives['time']=pd.to_datetime(t_archives.timestamp).dt.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1391 entries, 0 to 2325\n",
      "Data columns (total 13 columns):\n",
      "tweet_id              1391 non-null int64\n",
      "timestamp             1391 non-null object\n",
      "source                1391 non-null object\n",
      "expanded_urls         1391 non-null object\n",
      "rating_numerator      1391 non-null int64\n",
      "rating_denominator    1391 non-null int64\n",
      "name                  1391 non-null object\n",
      "doggo                 1391 non-null object\n",
      "floofer               1391 non-null object\n",
      "pupper                1391 non-null object\n",
      "puppo                 1391 non-null object\n",
      "date                  1391 non-null datetime64[ns]\n",
      "time                  1391 non-null object\n",
      "dtypes: datetime64[ns](1), int64(3), object(9)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "t_archives.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 6: Drop the timestamp column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives=t_archives.drop(columns=['timestamp'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1391 entries, 0 to 2325\n",
      "Data columns (total 12 columns):\n",
      "tweet_id              1391 non-null int64\n",
      "source                1391 non-null object\n",
      "expanded_urls         1391 non-null object\n",
      "rating_numerator      1391 non-null int64\n",
      "rating_denominator    1391 non-null int64\n",
      "name                  1391 non-null object\n",
      "doggo                 1391 non-null object\n",
      "floofer               1391 non-null object\n",
      "pupper                1391 non-null object\n",
      "puppo                 1391 non-null object\n",
      "date                  1391 non-null datetime64[ns]\n",
      "time                  1391 non-null object\n",
      "dtypes: datetime64[ns](1), int64(3), object(8)\n",
      "memory usage: 141.3+ KB\n"
     ]
    }
   ],
   "source": [
    "t_archives.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 7: doggo, floofer, pupper, and puppo columns should be merged into one column (dog_stage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create new column 'dog stage'\n",
    "t_archives['dog_stage']=\" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   9,   43,  108,  121,  240,  248,  300,  331,  339,  344,  345,\n",
       "             351,  363,  372,  384,  385,  389,  423,  426,  448,  449,  460,\n",
       "             489,  501,  575,  587,  698,  705,  724,  727,  731,  780,  782,\n",
       "             835,  877,  889,  977,  985,  989, 1030, 1103, 1117],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_doggo=t_archives.query('doggo==\"doggo\"').index\n",
    "t_doggo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([46, 582, 984, 1110, 1614], dtype='int64')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_floofer=t_archives.query('floofer==\"floofer\"').index\n",
    "t_floofer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29, 49, 82, 92, 98, 107, 135, 220, 249, 297, 304, 330, 378, 418, 460, 483, 515, 533, 575, 631, 672, 705, 763, 772, 776, 789, 793, 802, 853, 889, 917, 951, 962, 964, 966, 981, 1057, 1059, 1067, 1076, 1096, 1104, 1122, 1139, 1152, 1157, 1160, 1168, 1173, 1177, 1197, 1201, 1243, 1250, 1307, 1327, 1334, 1348, 1355, 1367, 1379, 1395, 1441, 1444, 1458, 1467, 1478, 1487, 1489, 1498, 1536, 1541, 1551, 1555, 1563, 1571, 1575, 1589, 1590, 1594, 1606, 1608, 1615, 1617, 1640, 1652, 1657, 1658, 1670, 1682, 1683, 1685, 1705, 1713, 1720, 1733, 1740, 1768, 1817, 1845, 1875, 1880, 1889, 1897, 1915, 1921, 1930, 1937, 1945, 1948, 1954, 1956, 1967, 1970, 1974, 1977, 1980, 1981, 1985, 1992, 1995, 2002, 2009, 2015, 2017\n"
     ]
    }
   ],
   "source": [
    "t_pupper=t_archives.query('pupper== \"pupper\"').index\n",
    "print (* t_pupper, sep=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([14, 71, 94, 129, 398, 439, 554, 567, 643, 663, 689, 713, 961, 1035,\n",
       "            1048, 1083],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_puppo=t_archives.query('puppo==\"puppo\"').index\n",
    "t_puppo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives.at[[9,   43,  108,  121,  240,  248,  300,  331,  339,  344,  345,\n",
    "             351,  363,  372,  384,  385,  389,  423,  426,  448,  449,  460,\n",
    "             489,  501,  575,  587,  698,  705,  724,  727,  731,  780,  782,\n",
    "             835,  877,  889,  977,  985,  989, 1030, 1103, 1117], 'dog_stage']= 'doggo'\n",
    "t_archives.at[[46, 582, 984, 1110, 1614], 'dog_stage']='floofer'\n",
    "t_archives.at[[29, 49, 82, 92, 98, 107, 135, 220, 249, 297, 304, 330, 378, 418, \n",
    "               460, 483, 515, 533, 575, 631, 672, 705, 763, 772, 776, 789, 793, \n",
    "               802, 853, 889, 917, 951, 962, 964, 966, 981, 1057, 1059, 1067, 1076, \n",
    "               1096, 1104, 1122, 1139, 1152, 1157, 1160, 1168, 1173, 1177, 1197, 1201, \n",
    "               1243, 1250, 1307, 1327, 1334, 1348, 1355, 1367, 1379, 1395, 1441, 1444, \n",
    "               1458, 1467, 1478, 1487, 1489, 1498, 1536, 1541, 1551, 1555, 1563, 1571, \n",
    "               1575, 1589, 1590, 1594, 1606, 1608, 1615, 1617, 1640, 1652, 1657, 1658, \n",
    "               1670, 1682, 1683, 1685, 1705, 1713, 1720, 1733, 1740, 1768, 1817, 1845, \n",
    "               1875, 1880, 1889, 1897, 1915, 1921, 1930, 1937, 1945, 1948, 1954, 1956, \n",
    "               1967, 1970, 1974, 1977, 1980, 1981, 1985, 1992, 1995, 2002, 2009, 2015, 2017], 'dog_stage']='pupper'\n",
    "t_archives.at[[14, 71, 94, 129, 398, 439, 554, 567, 643, 663, 689, 713, 961, 1035,\n",
    "            1048, 1083], 'dog_stage']='puppo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, 1, 2, 3, 4, 6, 8, 10, 11, 13, 15, 16, 17, 18, 20, 21, 23, 26, 27, 28, 31, 33, 34, 38, 39, 40, 44, 45, 48, 50, 51, 52, 53, 54, 57, 58, 60, 61, 63, 65, 66, 67, 69, 70, 75, 76, 77, 79, 80, 81, 84, 85, 86, 87, 90, 102, 104, 105, 106, 111, 114, 115, 116, 117, 119, 120, 122, 123, 126, 128, 134, 136, 138, 139, 140, 142, 143, 144, 145, 147, 150, 151, 152, 153, 156, 158, 161, 162, 163, 166, 167, 173, 174, 175, 176, 178, 181, 183, 187, 196, 197, 198, 201, 203, 205, 206, 207, 208, 209, 210, 214, 215, 216, 217, 219, 223, 225, 227, 229, 232, 233, 235, 236, 237, 239, 242, 244, 252, 253, 254, 255, 257, 258, 259, 261, 262, 263, 264, 265, 267, 270, 271, 276, 278, 279, 280, 282, 284, 287, 288, 292, 294, 295, 296, 299, 301, 305, 308, 311, 312, 316, 317, 320, 321, 322, 324, 325, 326, 328, 329, 333, 336, 337, 338, 347, 348, 350, 353, 354, 355, 356, 358, 360, 361, 364, 365, 367, 368, 370, 371, 373, 374, 376, 377, 379, 380, 381, 383, 388, 392, 393, 394, 396, 400, 401, 403, 405, 407, 408, 410, 412, 414, 417, 421, 424, 428, 430, 432, 441, 442, 443, 445, 451, 452, 454, 456, 457, 459, 461, 463, 464, 466, 468, 470, 471, 472, 473, 474, 477, 480, 481, 484, 486, 487, 490, 491, 492, 495, 496, 500, 502, 503, 504, 505, 507, 509, 510, 511, 512, 514, 516, 517, 518, 519, 520, 524, 525, 526, 528, 529, 532, 534, 536, 540, 544, 545, 547, 548, 549, 550, 553, 557, 559, 560, 562, 563, 564, 569, 571, 573, 578, 579, 584, 588, 590, 591, 592, 593, 607, 609, 610, 613, 614, 617, 619, 622, 623, 625, 626, 628, 630, 632, 633, 635, 636, 637, 638, 640, 641, 642, 644, 645, 646, 648, 650, 651, 652, 653, 659, 660, 662, 665, 666, 667, 668, 670, 673, 674, 675, 678, 679, 680, 681, 683, 685, 688, 690, 691, 693, 695, 697, 699, 700, 703, 704, 706, 708, 709, 710, 711, 712, 714, 715, 716, 717, 718, 719, 721, 722, 723, 725, 726, 729, 734, 737, 738, 739, 743, 747, 750, 752, 755, 756, 757, 760, 761, 765, 768, 769, 771, 775, 779, 783, 785, 786, 787, 790, 791, 792, 795, 796, 797, 799, 804, 805, 806, 808, 809, 810, 812, 813, 816, 817, 820, 821, 823, 824, 825, 827, 828, 830, 831, 832, 834, 836, 837, 838, 840, 842, 844, 845, 846, 848, 849, 850, 851, 854, 855, 856, 858, 859, 861, 862, 863, 864, 865, 867, 871, 873, 874, 875, 876, 878, 879, 880, 882, 883, 884, 888, 891, 892, 894, 896, 897, 898, 900, 901, 903, 904, 907, 909, 910, 915, 916, 918, 925, 927, 928, 929, 930, 932, 935, 936, 938, 940, 941, 942, 950, 952, 953, 954, 955, 957, 959, 963, 965, 969, 970, 971, 972, 974, 975, 976, 978, 979, 980, 983, 986, 987, 991, 994, 997, 1003, 1006, 1007, 1011, 1013, 1014, 1020, 1021, 1024, 1026, 1027, 1028, 1029, 1032, 1033, 1034, 1036, 1037, 1038, 1041, 1044, 1045, 1046, 1047, 1050, 1052, 1054, 1055, 1056, 1060, 1061, 1062, 1064, 1066, 1070, 1072, 1073, 1074, 1078, 1081, 1086, 1087, 1088, 1089, 1092, 1094, 1099, 1101, 1105, 1107, 1109, 1112, 1114, 1115, 1118, 1119, 1123, 1124, 1125, 1126, 1128, 1129, 1131, 1133, 1134, 1135, 1136, 1137, 1140, 1145, 1147, 1148, 1149, 1151, 1153, 1155, 1158, 1159, 1161, 1162, 1163, 1164, 1166, 1167, 1169, 1170, 1172, 1174, 1175, 1178, 1179, 1180, 1181, 1182, 1183, 1185, 1187, 1189, 1190, 1191, 1194, 1195, 1196, 1198, 1199, 1202, 1203, 1205, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1218, 1219, 1221, 1222, 1223, 1226, 1227, 1229, 1231, 1232, 1233, 1235, 1236, 1239, 1240, 1241, 1244, 1247, 1251, 1252, 1253, 1256, 1257, 1258, 1261, 1262, 1264, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1275, 1276, 1277, 1278, 1280, 1281, 1282, 1283, 1284, 1285, 1287, 1288, 1289, 1291, 1292, 1294, 1296, 1297, 1300, 1302, 1303, 1305, 1308, 1309, 1310, 1311, 1312, 1314, 1315, 1316, 1318, 1320, 1323, 1325, 1326, 1328, 1329, 1332, 1333, 1335, 1336, 1337, 1338, 1342, 1346, 1347, 1350, 1354, 1358, 1359, 1363, 1364, 1365, 1366, 1369, 1373, 1374, 1375, 1377, 1378, 1381, 1383, 1384, 1386, 1387, 1388, 1389, 1392, 1393, 1394, 1396, 1398, 1399, 1400, 1403, 1404, 1406, 1407, 1409, 1411, 1412, 1414, 1415, 1417, 1418, 1419, 1420, 1421, 1422, 1424, 1425, 1426, 1430, 1431, 1432, 1434, 1436, 1437, 1438, 1442, 1443, 1448, 1451, 1453, 1455, 1456, 1460, 1462, 1463, 1465, 1469, 1470, 1472, 1475, 1480, 1481, 1482, 1484, 1485, 1491, 1494, 1495, 1496, 1500, 1502, 1503, 1504, 1507, 1509, 1510, 1512, 1518, 1519, 1520, 1521, 1522, 1524, 1525, 1526, 1528, 1529, 1530, 1533, 1537, 1538, 1539, 1540, 1544, 1546, 1547, 1548, 1549, 1553, 1556, 1558, 1559, 1561, 1562, 1565, 1567, 1569, 1570, 1572, 1573, 1576, 1577, 1578, 1580, 1581, 1582, 1585, 1591, 1593, 1597, 1601, 1602, 1604, 1607, 1612, 1616, 1619, 1620, 1621, 1623, 1626, 1629, 1631, 1632, 1633, 1637, 1639, 1642, 1643, 1644, 1645, 1648, 1649, 1651, 1655, 1661, 1662, 1665, 1667, 1671, 1672, 1673, 1674, 1675, 1677, 1679, 1681, 1687, 1688, 1690, 1691, 1692, 1694, 1695, 1696, 1698, 1700, 1701, 1702, 1703, 1704, 1708, 1709, 1710, 1711, 1715, 1716, 1719, 1721, 1722, 1725, 1726, 1727, 1728, 1730, 1731, 1735, 1736, 1739, 1741, 1742, 1744, 1746, 1748, 1750, 1752, 1754, 1755, 1756, 1757, 1758, 1759, 1762, 1764, 1765, 1769, 1771, 1773, 1775, 1776, 1780, 1781, 1786, 1790, 1793, 1794, 1795, 1796, 1798, 1800, 1802, 1803, 1809, 1812, 1820, 1821, 1824, 1827, 1829, 1830, 1831, 1833, 1835, 1839, 1848, 1849, 1851, 1855, 1856, 1859, 1864, 1868, 1872, 1874, 1876, 1879, 1881, 1884, 1887, 1894, 1898, 1899, 1900, 1906, 1908, 1909, 1910, 1911, 1912, 1913, 1917, 1919, 1920, 1922, 1924, 1925, 1926, 1927, 1931, 1933, 1934, 1944, 1946, 1950, 1951, 1952, 1953, 1957, 1961, 1962, 1964, 1965, 1968, 1971, 1973, 1975, 1976, 1982, 1983, 1984, 1987, 1988, 1989, 1990, 1993, 1996, 1998, 2000, 2003, 2004, 2005, 2007, 2011, 2012, 2014, 2016, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2032, 2035, 2039, 2041, 2042, 2043, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2055, 2057, 2058, 2059, 2061, 2063, 2064, 2069, 2071, 2072, 2075, 2077, 2078, 2080, 2082, 2083, 2084, 2085, 2087, 2088, 2090, 2093, 2095, 2096, 2097, 2098, 2100, 2101, 2102, 2103, 2104, 2106, 2110, 2111, 2113, 2114, 2115, 2117, 2118, 2120, 2121, 2122, 2123, 2124, 2126, 2129, 2130, 2132, 2133, 2134, 2135, 2136, 2137, 2140, 2141, 2143, 2144, 2147, 2148, 2150, 2151, 2152, 2154, 2156, 2158, 2159, 2160, 2162, 2163, 2164, 2165, 2167, 2168, 2169, 2170, 2171, 2173, 2174, 2175, 2176, 2179, 2180, 2182, 2183, 2185, 2187, 2188, 2190, 2192, 2193, 2194, 2195, 2196, 2197, 2199, 2200, 2203, 2205, 2206, 2207, 2208, 2209, 2210, 2213, 2216, 2217, 2219, 2221, 2225, 2226, 2228, 2229, 2230, 2233, 2234, 2238, 2240, 2241, 2243, 2244, 2245, 2246, 2248, 2251, 2256, 2257, 2262, 2263, 2265, 2266, 2268, 2270, 2271, 2274, 2275, 2279, 2280, 2282, 2283, 2286, 2289, 2290, 2291, 2292, 2293, 2295, 2296, 2297, 2300, 2302, 2303, 2308, 2309, 2312, 2313, 2315, 2317, 2318, 2319, 2325\n"
     ]
    }
   ],
   "source": [
    "dog_index=t_archives.query('dog_stage==\" \"').index\n",
    "print(* dog_index, sep=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives.at[[0, 1, 2, 3, 4, 6, 8, 10, 11, 13, 15, 16, 17, 18, 20, 21, 23, 26, 27, 28, 31, 33, 34, 38, 39, 40, 44, 45, 48, 50, 51, 52, 53, 54, 57, 58, 60, 61, 63, 65, 66, 67, 69, 70, 75, 76, 77, 79, 80, 81, 84, 85, 86, 87, 90, 102, 104, 105, 106, 111, 114, 115, 116, 117, 119, 120, 122, 123, 126, 128, 134, 136, 138, 139, 140, 142, 143, 144, 145, 147, 150, 151, 152, 153, 156, 158, 161, 162, 163, 166, 167, 173, 174, 175, 176, 178, 181, 183, 187, 196, 197, 198, 201, 203, 205, 206, 207, 208, 209, 210, 214, 215, 216, 217, 219, 223, 225, 227, 229, 232, 233, 235, 236, 237, 239, 242, 244, 252, 253, 254, 255, 257, 258, 259, 261, 262, 263, 264, 265, 267, 270, 271, 276, 278, 279, 280, 282, 284, 287, 288, 292, 294, 295, 296, 299, 301, 305, 308, 311, 312, 316, 317, 320, 321, 322, 324, 325, 326, 328, 329, 333, 336, 337, 338, 347, 348, 350, 353, 354, 355, 356, 358, 360, 361, 364, 365, 367, 368, 370, 371, 373, 374, 376, 377, 379, 380, 381, 383, 388, 392, 393, 394, 396, 400, 401, 403, 405, 407, 408, 410, 412, 414, 417, 421, 424, 428, 430, 432, 441, 442, 443, 445, 451, 452, 454, 456, 457, 459, 461, 463, 464, 466, 468, 470, 471, 472, 473, 474, 477, 480, 481, 484, 486, 487, 490, 491, 492, 495, 496, 500, 502, 503, 504, 505, 507, 509, 510, 511, 512, 514, 516, 517, 518, 519, 520, 524, 525, 526, 528, 529, 532, 534, 536, 540, 544, 545, 547, 548, 549, 550, 553, 557, 559, 560, 562, 563, 564, 569, 571, 573, 578, 579, 584, 588, 590, 591, 592, 593, 607, 609, 610, 613, 614, 617, 619, 622, 623, 625, 626, 628, 630, 632, 633, 635, 636, 637, 638, 640, 641, 642, 644, 645, 646, 648, 650, 651, 652, 653, 659, 660, 662, 665, 666, 667, 668, 670, 673, 674, 675, 678, 679, 680, 681, 683, 685, 688, 690, 691, 693, 695, 697, 699, 700, 703, 704, 706, 708, 709, 710, 711, 712, 714, 715, 716, 717, 718, 719, 721, 722, 723, 725, 726, 729, 734, 737, 738, 739, 743, 747, 750, 752, 755, 756, 757, 760, 761, 765, 768, 769, 771, 775, 779, 783, 785, 786, 787, 790, 791, 792, 795, 796, 797, 799, 804, 805, 806, 808, 809, 810, 812, 813, 816, 817, 820, 821, 823, 824, 825, 827, 828, 830, 831, 832, 834, 836, 837, 838, 840, 842, 844, 845, 846, 848, 849, 850, 851, 854, 855, 856, 858, 859, 861, 862, 863, 864, 865, 867, 871, 873, 874, 875, 876, 878, 879, 880, 882, 883, 884, 888, 891, 892, 894, 896, 897, 898, 900, 901, 903, 904, 907, 909, 910, 915, 916, 918, 925, 927, 928, 929, 930, 932, 935, 936, 938, 940, 941, 942, 950, 952, 953, 954, 955, 957, 959, 963, 965, 969, 970, 971, 972, 974, 975, 976, 978, 979, 980, 983, 986, 987, 991, 994, 997, 1003, 1006, 1007, 1011, 1013, 1014, 1020, 1021, 1024, 1026, 1027, 1028, 1029, 1032, 1033, 1034, 1036, 1037, 1038, 1041, 1044, 1045, 1046, 1047, 1050, 1052, 1054, 1055, 1056, 1060, 1061, 1062, 1064, 1066, 1070, 1072, 1073, 1074, 1078, 1081, 1086, 1087, 1088, 1089, 1092, 1094, 1099, 1101, 1105, 1107, 1109, 1112, 1114, 1115, 1118, 1119, 1123, 1124, 1125, 1126, 1128, 1129, 1131, 1133, 1134, 1135, 1136, 1137, 1140, 1145, 1147, 1148, 1149, 1151, 1153, 1155, 1158, 1159, 1161, 1162, 1163, 1164, 1166, 1167, 1169, 1170, 1172, 1174, 1175, 1178, 1179, 1180, 1181, 1182, 1183, 1185, 1187, 1189, 1190, 1191, 1194, 1195, 1196, 1198, 1199, 1202, 1203, 1205, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1218, 1219, 1221, 1222, 1223, 1226, 1227, 1229, 1231, 1232, 1233, 1235, 1236, 1239, 1240, 1241, 1244, 1247, 1251, 1252, 1253, 1256, 1257, 1258, 1261, 1262, 1264, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1275, 1276, 1277, 1278, 1280, 1281, 1282, 1283, 1284, 1285, 1287, 1288, 1289, 1291, 1292, 1294, 1296, 1297, 1300, 1302, 1303, 1305, 1308, 1309, 1310, 1311, 1312, 1314, 1315, 1316, 1318, 1320, 1323, 1325, 1326, 1328, 1329, 1332, 1333, 1335, 1336, 1337, 1338, 1342, 1346, 1347, 1350, 1354, 1358, 1359, 1363, 1364, 1365, 1366, 1369, 1373, 1374, 1375, 1377, 1378, 1381, 1383, 1384, 1386, 1387, 1388, 1389, 1392, 1393, 1394, 1396, 1398, 1399, 1400, 1403, 1404, 1406, 1407, 1409, 1411, 1412, 1414, 1415, 1417, 1418, 1419, 1420, 1421, 1422, 1424, 1425, 1426, 1430, 1431, 1432, 1434, 1436, 1437, 1438, 1442, 1443, 1448, 1451, 1453, 1455, 1456, 1460, 1462, 1463, 1465, 1469, 1470, 1472, 1475, 1480, 1481, 1482, 1484, 1485, 1491, 1494, 1495, 1496, 1500, 1502, 1503, 1504, 1507, 1509, 1510, 1512, 1518, 1519, 1520, 1521, 1522, 1524, 1525, 1526, 1528, 1529, 1530, 1533, 1537, 1538, 1539, 1540, 1544, 1546, 1547, 1548, 1549, 1553, 1556, 1558, 1559, 1561, 1562, 1565, 1567, 1569, 1570, 1572, 1573, 1576, 1577, 1578, 1580, 1581, 1582, 1585, 1591, 1593, 1597, 1601, 1602, 1604, 1607, 1612, 1616, 1619, 1620, 1621, 1623, 1626, 1629, 1631, 1632, 1633, 1637, 1639, 1642, 1643, 1644, 1645, 1648, 1649, 1651, 1655, 1661, 1662, 1665, 1667, 1671, 1672, 1673, 1674, 1675, 1677, 1679, 1681, 1687, 1688, 1690, 1691, 1692, 1694, 1695, 1696, 1698, 1700, 1701, 1702, 1703, 1704, 1708, 1709, 1710, 1711, 1715, 1716, 1719, 1721, 1722, 1725, 1726, 1727, 1728, 1730, 1731, 1735, 1736, 1739, 1741, 1742, 1744, 1746, 1748, 1750, 1752, 1754, 1755, 1756, 1757, 1758, 1759, 1762, 1764, 1765, 1769, 1771, 1773, 1775, 1776, 1780, 1781, 1786, 1790, 1793, 1794, 1795, 1796, 1798, 1800, 1802, 1803, 1809, 1812, 1820, 1821, 1824, 1827, 1829, 1830, 1831, 1833, 1835, 1839, 1848, 1849, 1851, 1855, 1856, 1859, 1864, 1868, 1872, 1874, 1876, 1879, 1881, 1884, 1887, 1894, 1898, 1899, 1900, 1906, 1908, 1909, 1910, 1911, 1912, 1913, 1917, 1919, 1920, 1922, 1924, 1925, 1926, 1927, 1931, 1933, 1934, 1944, 1946, 1950, 1951, 1952, 1953, 1957, 1961, 1962, 1964, 1965, 1968, 1971, 1973, 1975, 1976, 1982, 1983, 1984, 1987, 1988, 1989, 1990, 1993, 1996, 1998, 2000, 2003, 2004, 2005, 2007, 2011, 2012, 2014, 2016, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2032, 2035, 2039, 2041, 2042, 2043, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2055, 2057, 2058, 2059, 2061, 2063, 2064, 2069, 2071, 2072, 2075, 2077, 2078, 2080, 2082, 2083, 2084, 2085, 2087, 2088, 2090, 2093, 2095, 2096, 2097, 2098, 2100, 2101, 2102, 2103, 2104, 2106, 2110, 2111, 2113, 2114, 2115, 2117, 2118, 2120, 2121, 2122, 2123, 2124, 2126, 2129, 2130, 2132, 2133, 2134, 2135, 2136, 2137, 2140, 2141, 2143, 2144, 2147, 2148, 2150, 2151, 2152, 2154, 2156, 2158, 2159, 2160, 2162, 2163, 2164, 2165, 2167, 2168, 2169, 2170, 2171, 2173, 2174, 2175, 2176, 2179, 2180, 2182, 2183, 2185, 2187, 2188, 2190, 2192, 2193, 2194, 2195, 2196, 2197, 2199, 2200, 2203, 2205, 2206, 2207, 2208, 2209, 2210, 2213, 2216, 2217, 2219, 2221, 2225, 2226, 2228, 2229, 2230, 2233, 2234, 2238, 2240, 2241, 2243, 2244, 2245, 2246, 2248, 2251, 2256, 2257, 2262, 2263, 2265, 2266, 2268, 2270, 2271, 2274, 2275, 2279, 2280, 2282, 2283, 2286, 2289, 2290, 2291, 2292, 2293, 2295, 2296, 2297, 2300, 2302, 2303, 2308, 2309, 2312, 2313, 2315, 2317, 2318, 2319, 2325], 'dog_stage']= 'None'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       1207\n",
       "pupper      125\n",
       "doggo        38\n",
       "puppo        16\n",
       "floofer       5\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archives.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 8: drop pupper, doggo, puppo and floofer columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives.drop(columns=['pupper', 'doggo', 'puppo', 'floofer'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>source</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>Twitter for Iphone</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>2017-08-01</td>\n",
       "      <td>16:23:56</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>Twitter for Iphone</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>2017-08-01</td>\n",
       "      <td>00:17:27</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>Twitter for Iphone</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>2017-07-31</td>\n",
       "      <td>00:18:03</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>Twitter for Iphone</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>2017-07-30</td>\n",
       "      <td>15:58:51</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>Twitter for Iphone</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>2017-07-29</td>\n",
       "      <td>16:00:24</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>890971913173991426</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://gofundme.com/ydvmve-surgery-for-jax,ht...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jax</td>\n",
       "      <td>2017-07-28</td>\n",
       "      <td>16:27:12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>890609185150312448</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890609185...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Zoey</td>\n",
       "      <td>2017-07-27</td>\n",
       "      <td>16:25:51</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890240255...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Cassie</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>15:59:51</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>890006608113172480</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890006608...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Koda</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>00:31:25</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>889880896479866881</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889880896...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Bruno</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>16:11:53</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>889638837579907072</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889638837...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Ted</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>00:10:02</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889531135...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Stuart</td>\n",
       "      <td>2017-07-24</td>\n",
       "      <td>17:02:04</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889278841...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>2017-07-24</td>\n",
       "      <td>00:19:32</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>888917238123831296</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888917238...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Jim</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>00:22:39</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>888804989199671297</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888804989...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Zeke</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>16:56:37</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>888554962724278272</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888554962...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Ralphus</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>00:23:06</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>888078434458587136</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888078434...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>2017-07-20</td>\n",
       "      <td>16:49:33</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>887705289381826560</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887705289...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jeffrey</td>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>16:06:48</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>887473957103951883</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887473957...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Canela</td>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>00:47:34</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>886983233522544640</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886983233...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Maya</td>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>16:17:36</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>886736880519319552</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://www.gofundme.com/mingusneedsus,https:/...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Mingus</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>23:58:41</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886680336...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Derek</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>20:14:00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886366144...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Roscoe</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>23:25:31</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>886258384151887873</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886258384...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Waffles</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>16:17:19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>885984800019947520</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/885984800...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Jimbo</td>\n",
       "      <td>2017-07-14</td>\n",
       "      <td>22:10:11</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>885528943205470208</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/885528943...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Maisey</td>\n",
       "      <td>2017-07-13</td>\n",
       "      <td>15:58:47</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>884925521741709313</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/884925521...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Earl</td>\n",
       "      <td>2017-07-12</td>\n",
       "      <td>00:01:00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>884876753390489601</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/884876753...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Lola</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>20:47:12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>884562892145688576</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/884562892...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Kevin</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>00:00:02</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>884162670584377345</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/884162670...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Yogi</td>\n",
       "      <td>2017-07-09</td>\n",
       "      <td>21:29:42</td>\n",
       "      <td>doggo</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2268</th>\n",
       "      <td>667517642048163840</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667517642...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Dook</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>01:39:42</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>667502640335572993</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667502640...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Hall</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>00:40:05</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>667495797102141441</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667495797...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Philippe</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>00:12:54</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>667455448082227200</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667455448...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Reese</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>21:32:34</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>667453023279554560</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667453023...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Cupcake</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>21:22:56</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>667405339315146752</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667405339...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Biden</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>18:13:27</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2280</th>\n",
       "      <td>667393430834667520</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667393430...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Fwed</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>17:26:08</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2282</th>\n",
       "      <td>667211855547486208</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667211855...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Genevieve</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>05:24:37</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>667200525029539841</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667200525...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Joshwa</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>04:39:35</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>667182792070062081</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667182792...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Timison</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>03:29:07</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>667174963120574464</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667174963...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Clarence</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>02:58:01</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>667171260800061440</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667171260...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>02:43:18</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>667165590075940865</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667165590...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Churlie</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>02:20:46</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>667160273090932737</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667160273...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Bradlay</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>01:59:39</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>667152164079423490</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667152164...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Pipsy</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>01:27:25</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>667119796878725120</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667119796...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Gabe</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>23:18:48</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2296</th>\n",
       "      <td>667090893657276420</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667090893...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Clybe</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>21:23:57</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2297</th>\n",
       "      <td>667073648344346624</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667073648...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Dave</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>20:15:26</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>667062181243039745</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667062181...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Keet</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>19:29:52</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>667012601033924608</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667012601...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Klevin</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>16:12:51</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>666996132027977728</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666996132...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Carll</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>15:07:24</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2308</th>\n",
       "      <td>666817836334096384</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666817836...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Jeph</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>03:18:55</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2309</th>\n",
       "      <td>666804364988780544</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666804364...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Jockson</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>02:25:23</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312</th>\n",
       "      <td>666776908487630848</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666776908...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>Josep</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>00:36:17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2313</th>\n",
       "      <td>666739327293083650</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666739327...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Lugan</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>22:06:57</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>666691418707132416</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666691418...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Christoper</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>18:56:35</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2317</th>\n",
       "      <td>666644823164719104</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666644823...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Jimothy</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>15:51:26</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2318</th>\n",
       "      <td>666454714377183233</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666454714...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Kreggory</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>03:16:00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2319</th>\n",
       "      <td>666447344410484738</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666447344...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Scout</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>02:46:43</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>666418789513326592</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666418789...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Walter</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>00:53:15</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1391 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id              source  \\\n",
       "0     892420643555336193  Twitter for Iphone   \n",
       "1     892177421306343426  Twitter for Iphone   \n",
       "2     891815181378084864  Twitter for Iphone   \n",
       "3     891689557279858688  Twitter for Iphone   \n",
       "4     891327558926688256  Twitter for Iphone   \n",
       "6     890971913173991426  Twitter for Iphone   \n",
       "8     890609185150312448  Twitter for Iphone   \n",
       "9     890240255349198849  Twitter for Iphone   \n",
       "10    890006608113172480  Twitter for Iphone   \n",
       "11    889880896479866881  Twitter for Iphone   \n",
       "13    889638837579907072  Twitter for Iphone   \n",
       "14    889531135344209921  Twitter for Iphone   \n",
       "15    889278841981685760  Twitter for Iphone   \n",
       "16    888917238123831296  Twitter for Iphone   \n",
       "17    888804989199671297  Twitter for Iphone   \n",
       "18    888554962724278272  Twitter for Iphone   \n",
       "20    888078434458587136  Twitter for Iphone   \n",
       "21    887705289381826560  Twitter for Iphone   \n",
       "23    887473957103951883  Twitter for Iphone   \n",
       "26    886983233522544640  Twitter for Iphone   \n",
       "27    886736880519319552  Twitter for Iphone   \n",
       "28    886680336477933568  Twitter for Iphone   \n",
       "29    886366144734445568  Twitter for Iphone   \n",
       "31    886258384151887873  Twitter for Iphone   \n",
       "33    885984800019947520  Twitter for Iphone   \n",
       "34    885528943205470208  Twitter for Iphone   \n",
       "38    884925521741709313  Twitter for Iphone   \n",
       "39    884876753390489601  Twitter for Iphone   \n",
       "40    884562892145688576  Twitter for Iphone   \n",
       "43    884162670584377345  Twitter for Iphone   \n",
       "...                  ...                 ...   \n",
       "2268  667517642048163840  Twitter Web Client   \n",
       "2270  667502640335572993  Twitter Web Client   \n",
       "2271  667495797102141441  Twitter Web Client   \n",
       "2274  667455448082227200  Twitter Web Client   \n",
       "2275  667453023279554560  Twitter Web Client   \n",
       "2279  667405339315146752  Twitter for Iphone   \n",
       "2280  667393430834667520  Twitter for Iphone   \n",
       "2282  667211855547486208  Twitter for Iphone   \n",
       "2283  667200525029539841  Twitter for Iphone   \n",
       "2286  667182792070062081  Twitter for Iphone   \n",
       "2289  667174963120574464  Twitter for Iphone   \n",
       "2290  667171260800061440  Twitter for Iphone   \n",
       "2291  667165590075940865  Twitter for Iphone   \n",
       "2292  667160273090932737  Twitter for Iphone   \n",
       "2293  667152164079423490  Twitter for Iphone   \n",
       "2295  667119796878725120  Twitter for Iphone   \n",
       "2296  667090893657276420  Twitter for Iphone   \n",
       "2297  667073648344346624  Twitter for Iphone   \n",
       "2300  667062181243039745  Twitter for Iphone   \n",
       "2302  667012601033924608  Twitter for Iphone   \n",
       "2303  666996132027977728  Twitter for Iphone   \n",
       "2308  666817836334096384  Twitter for Iphone   \n",
       "2309  666804364988780544  Twitter for Iphone   \n",
       "2312  666776908487630848  Twitter for Iphone   \n",
       "2313  666739327293083650  Twitter for Iphone   \n",
       "2315  666691418707132416  Twitter for Iphone   \n",
       "2317  666644823164719104  Twitter for Iphone   \n",
       "2318  666454714377183233  Twitter for Iphone   \n",
       "2319  666447344410484738  Twitter for Iphone   \n",
       "2325  666418789513326592  Twitter for Iphone   \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",
       "6     https://gofundme.com/ydvmve-surgery-for-jax,ht...                13   \n",
       "8     https://twitter.com/dog_rates/status/890609185...                13   \n",
       "9     https://twitter.com/dog_rates/status/890240255...                14   \n",
       "10    https://twitter.com/dog_rates/status/890006608...                13   \n",
       "11    https://twitter.com/dog_rates/status/889880896...                13   \n",
       "13    https://twitter.com/dog_rates/status/889638837...                12   \n",
       "14    https://twitter.com/dog_rates/status/889531135...                13   \n",
       "15    https://twitter.com/dog_rates/status/889278841...                13   \n",
       "16    https://twitter.com/dog_rates/status/888917238...                12   \n",
       "17    https://twitter.com/dog_rates/status/888804989...                13   \n",
       "18    https://twitter.com/dog_rates/status/888554962...                13   \n",
       "20    https://twitter.com/dog_rates/status/888078434...                12   \n",
       "21    https://twitter.com/dog_rates/status/887705289...                13   \n",
       "23    https://twitter.com/dog_rates/status/887473957...                13   \n",
       "26    https://twitter.com/dog_rates/status/886983233...                13   \n",
       "27    https://www.gofundme.com/mingusneedsus,https:/...                13   \n",
       "28    https://twitter.com/dog_rates/status/886680336...                13   \n",
       "29    https://twitter.com/dog_rates/status/886366144...                12   \n",
       "31    https://twitter.com/dog_rates/status/886258384...                13   \n",
       "33    https://twitter.com/dog_rates/status/885984800...                12   \n",
       "34    https://twitter.com/dog_rates/status/885528943...                13   \n",
       "38    https://twitter.com/dog_rates/status/884925521...                12   \n",
       "39    https://twitter.com/dog_rates/status/884876753...                13   \n",
       "40    https://twitter.com/dog_rates/status/884562892...                13   \n",
       "43    https://twitter.com/dog_rates/status/884162670...                12   \n",
       "...                                                 ...               ...   \n",
       "2268  https://twitter.com/dog_rates/status/667517642...                 8   \n",
       "2270  https://twitter.com/dog_rates/status/667502640...                11   \n",
       "2271  https://twitter.com/dog_rates/status/667495797...                 9   \n",
       "2274  https://twitter.com/dog_rates/status/667455448...                 7   \n",
       "2275  https://twitter.com/dog_rates/status/667453023...                11   \n",
       "2279  https://twitter.com/dog_rates/status/667405339...                 7   \n",
       "2280  https://twitter.com/dog_rates/status/667393430...                 8   \n",
       "2282  https://twitter.com/dog_rates/status/667211855...                 9   \n",
       "2283  https://twitter.com/dog_rates/status/667200525...                11   \n",
       "2286  https://twitter.com/dog_rates/status/667182792...                10   \n",
       "2289  https://twitter.com/dog_rates/status/667174963...                 9   \n",
       "2290  https://twitter.com/dog_rates/status/667171260...                10   \n",
       "2291  https://twitter.com/dog_rates/status/667165590...                10   \n",
       "2292  https://twitter.com/dog_rates/status/667160273...                11   \n",
       "2293  https://twitter.com/dog_rates/status/667152164...                12   \n",
       "2295  https://twitter.com/dog_rates/status/667119796...                10   \n",
       "2296  https://twitter.com/dog_rates/status/667090893...                 7   \n",
       "2297  https://twitter.com/dog_rates/status/667073648...                10   \n",
       "2300  https://twitter.com/dog_rates/status/667062181...                10   \n",
       "2302  https://twitter.com/dog_rates/status/667012601...                 9   \n",
       "2303  https://twitter.com/dog_rates/status/666996132...                10   \n",
       "2308  https://twitter.com/dog_rates/status/666817836...                 9   \n",
       "2309  https://twitter.com/dog_rates/status/666804364...                 8   \n",
       "2312  https://twitter.com/dog_rates/status/666776908...                 5   \n",
       "2313  https://twitter.com/dog_rates/status/666739327...                10   \n",
       "2315  https://twitter.com/dog_rates/status/666691418...                 8   \n",
       "2317  https://twitter.com/dog_rates/status/666644823...                 9   \n",
       "2318  https://twitter.com/dog_rates/status/666454714...                10   \n",
       "2319  https://twitter.com/dog_rates/status/666447344...                 9   \n",
       "2325  https://twitter.com/dog_rates/status/666418789...                10   \n",
       "\n",
       "      rating_denominator        name       date      time dog_stage  \n",
       "0                     10     Phineas 2017-08-01  16:23:56      None  \n",
       "1                     10       Tilly 2017-08-01  00:17:27      None  \n",
       "2                     10      Archie 2017-07-31  00:18:03      None  \n",
       "3                     10       Darla 2017-07-30  15:58:51      None  \n",
       "4                     10    Franklin 2017-07-29  16:00:24      None  \n",
       "6                     10         Jax 2017-07-28  16:27:12      None  \n",
       "8                     10        Zoey 2017-07-27  16:25:51      None  \n",
       "9                     10      Cassie 2017-07-26  15:59:51     doggo  \n",
       "10                    10        Koda 2017-07-26  00:31:25      None  \n",
       "11                    10       Bruno 2017-07-25  16:11:53      None  \n",
       "13                    10         Ted 2017-07-25  00:10:02      None  \n",
       "14                    10      Stuart 2017-07-24  17:02:04     puppo  \n",
       "15                    10      Oliver 2017-07-24  00:19:32      None  \n",
       "16                    10         Jim 2017-07-23  00:22:39      None  \n",
       "17                    10        Zeke 2017-07-22  16:56:37      None  \n",
       "18                    10     Ralphus 2017-07-22  00:23:06      None  \n",
       "20                    10      Gerald 2017-07-20  16:49:33      None  \n",
       "21                    10     Jeffrey 2017-07-19  16:06:48      None  \n",
       "23                    10      Canela 2017-07-19  00:47:34      None  \n",
       "26                    10        Maya 2017-07-17  16:17:36      None  \n",
       "27                    10      Mingus 2017-07-16  23:58:41      None  \n",
       "28                    10       Derek 2017-07-16  20:14:00      None  \n",
       "29                    10      Roscoe 2017-07-15  23:25:31    pupper  \n",
       "31                    10     Waffles 2017-07-15  16:17:19      None  \n",
       "33                    10       Jimbo 2017-07-14  22:10:11      None  \n",
       "34                    10      Maisey 2017-07-13  15:58:47      None  \n",
       "38                    10        Earl 2017-07-12  00:01:00      None  \n",
       "39                    10        Lola 2017-07-11  20:47:12      None  \n",
       "40                    10       Kevin 2017-07-11  00:00:02      None  \n",
       "43                    10        Yogi 2017-07-09  21:29:42     doggo  \n",
       "...                  ...         ...        ...       ...       ...  \n",
       "2268                  10        Dook 2015-11-20  01:39:42      None  \n",
       "2270                  10        Hall 2015-11-20  00:40:05      None  \n",
       "2271                  10    Philippe 2015-11-20  00:12:54      None  \n",
       "2274                  10       Reese 2015-11-19  21:32:34      None  \n",
       "2275                  10     Cupcake 2015-11-19  21:22:56      None  \n",
       "2279                  10       Biden 2015-11-19  18:13:27      None  \n",
       "2280                  10        Fwed 2015-11-19  17:26:08      None  \n",
       "2282                  10   Genevieve 2015-11-19  05:24:37      None  \n",
       "2283                  10      Joshwa 2015-11-19  04:39:35      None  \n",
       "2286                  10     Timison 2015-11-19  03:29:07      None  \n",
       "2289                  10    Clarence 2015-11-19  02:58:01      None  \n",
       "2290                  10     Kenneth 2015-11-19  02:43:18      None  \n",
       "2291                  10     Churlie 2015-11-19  02:20:46      None  \n",
       "2292                  10     Bradlay 2015-11-19  01:59:39      None  \n",
       "2293                  10       Pipsy 2015-11-19  01:27:25      None  \n",
       "2295                  10        Gabe 2015-11-18  23:18:48      None  \n",
       "2296                  10       Clybe 2015-11-18  21:23:57      None  \n",
       "2297                  10        Dave 2015-11-18  20:15:26      None  \n",
       "2300                  10        Keet 2015-11-18  19:29:52      None  \n",
       "2302                  10      Klevin 2015-11-18  16:12:51      None  \n",
       "2303                  10       Carll 2015-11-18  15:07:24      None  \n",
       "2308                  10        Jeph 2015-11-18  03:18:55      None  \n",
       "2309                  10     Jockson 2015-11-18  02:25:23      None  \n",
       "2312                  10       Josep 2015-11-18  00:36:17      None  \n",
       "2313                  10       Lugan 2015-11-17  22:06:57      None  \n",
       "2315                  10  Christoper 2015-11-17  18:56:35      None  \n",
       "2317                  10     Jimothy 2015-11-17  15:51:26      None  \n",
       "2318                  10    Kreggory 2015-11-17  03:16:00      None  \n",
       "2319                  10       Scout 2015-11-17  02:46:43      None  \n",
       "2325                  10      Walter 2015-11-17  00:53:15      None  \n",
       "\n",
       "[1391 rows x 9 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 9: Remove expanded_urls column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives.drop(columns=['expanded_urls'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>source</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>691820333922455552</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Brockly</td>\n",
       "      <td>2016-01-26</td>\n",
       "      <td>03:09:55</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>669324657376567296</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Ralf</td>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>01:20:08</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>673342308415348736</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Django</td>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>03:24:51</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>766793450729734144</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Rufus</td>\n",
       "      <td>2016-08-20</td>\n",
       "      <td>00:26:19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>735256018284875776</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Kellogg</td>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>23:47:49</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>687704180304273409</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Blakely</td>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>18:33:48</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>667182792070062081</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Timison</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>03:29:07</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>669571471778410496</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Keith</td>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>17:40:53</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>817536400337801217</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Eugene</td>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>01:00:41</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>672902681409806336</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Jimison</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>22:17:55</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>737800304142471168</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Bella</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>00:17:54</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>712092745624633345</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Steven</td>\n",
       "      <td>2016-03-22</td>\n",
       "      <td>01:45:15</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>672082170312290304</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Patch</td>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>15:57:30</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>739485634323156992</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>Kyle</td>\n",
       "      <td>2016-06-05</td>\n",
       "      <td>15:54:48</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>815736392542261248</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Akumi</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>01:48:06</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1558</th>\n",
       "      <td>688789766343622656</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Pubert</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>18:27:32</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>693486665285931008</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>2016-01-30</td>\n",
       "      <td>17:31:20</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>759197388317847553</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Luna</td>\n",
       "      <td>2016-07-30</td>\n",
       "      <td>01:22:17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>710140971284037632</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Tater</td>\n",
       "      <td>2016-03-16</td>\n",
       "      <td>16:29:35</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>816091915477250048</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Comet</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>01:20:49</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id              source  rating_numerator  \\\n",
       "1504  691820333922455552  Twitter for Iphone                 8   \n",
       "2173  669324657376567296  Twitter for Iphone                11   \n",
       "1967  673342308415348736  Twitter for Iphone                10   \n",
       "842   766793450729734144  Twitter for Iphone                10   \n",
       "1103  735256018284875776  Twitter for Iphone                 8   \n",
       "1571  687704180304273409  Twitter for Iphone                 9   \n",
       "2286  667182792070062081  Twitter for Iphone                10   \n",
       "2159  669571471778410496  Twitter for Iphone                 7   \n",
       "461   817536400337801217  Twitter for Iphone                12   \n",
       "1984  672902681409806336  Twitter for Iphone                 9   \n",
       "1089  737800304142471168  Twitter for Iphone                10   \n",
       "1239  712092745624633345  Twitter for Iphone                 7   \n",
       "2021  672082170312290304  Twitter for Iphone                11   \n",
       "1078  739485634323156992  Twitter for Iphone                 6   \n",
       "480   815736392542261248  Twitter for Iphone                11   \n",
       "1558  688789766343622656  Twitter for Iphone                 8   \n",
       "1480  693486665285931008  Twitter for Iphone                11   \n",
       "894   759197388317847553  Twitter for Iphone                12   \n",
       "1262  710140971284037632  Twitter for Iphone                 8   \n",
       "474   816091915477250048  Twitter for Iphone                12   \n",
       "\n",
       "      rating_denominator     name       date      time dog_stage  \n",
       "1504                  10  Brockly 2016-01-26  03:09:55      None  \n",
       "2173                  10     Ralf 2015-11-25  01:20:08      None  \n",
       "1967                  10   Django 2015-12-06  03:24:51    pupper  \n",
       "842                   10    Rufus 2016-08-20  00:26:19      None  \n",
       "1103                  10  Kellogg 2016-05-24  23:47:49     doggo  \n",
       "1571                  10  Blakely 2016-01-14  18:33:48    pupper  \n",
       "2286                  10  Timison 2015-11-19  03:29:07      None  \n",
       "2159                  10    Keith 2015-11-25  17:40:53      None  \n",
       "461                   10   Eugene 2017-01-07  01:00:41      None  \n",
       "1984                  10  Jimison 2015-12-04  22:17:55      None  \n",
       "1089                  10    Bella 2016-06-01  00:17:54      None  \n",
       "1239                  10   Steven 2016-03-22  01:45:15      None  \n",
       "2021                  10    Patch 2015-12-02  15:57:30      None  \n",
       "1078                  10     Kyle 2016-06-05  15:54:48      None  \n",
       "480                   10    Akumi 2017-01-02  01:48:06      None  \n",
       "1558                  10   Pubert 2016-01-17  18:27:32      None  \n",
       "1480                  10  Lincoln 2016-01-30  17:31:20      None  \n",
       "894                   10     Luna 2016-07-30  01:22:17      None  \n",
       "1262                  10    Tater 2016-03-16  16:29:35      None  \n",
       "474                   10    Comet 2017-01-03  01:20:49      None  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archives.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 10: some dog breeds are not actual dog breeds. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define:** some dog breeds are not actual dog breeds. drop entries with 'False: as values in the p1_dog, p2_dog, p3_dog columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog      p2   p2_conf  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True  collie  0.156665   \n",
       "\n",
       "   p2_dog                 p3   p3_conf  p3_dog  \n",
       "0    True  Shetland_sheepdog  0.061428    True  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.head(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = predictions[((predictions['p1_dog'] == True) & (predictions['p2_dog'] == True) & (predictions['p3_dog'] == True))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    1243\n",
       "Name: p1_dog, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.p1_dog.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    1243\n",
       "Name: p2_dog, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.p2_dog.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    1243\n",
       "Name: p3_dog, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.p3_dog.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 11: change data type of tweet_id column in all three datasets to string object before merging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archives.tweet_id = t_archives.tweet_id.astype(str)\n",
    "predictions.tweet_id = predictions.tweet_id.astype(str)\n",
    "json_copy.tweet_id = json_copy.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1391 entries, 0 to 2325\n",
      "Data columns (total 8 columns):\n",
      "tweet_id              1391 non-null object\n",
      "source                1391 non-null object\n",
      "rating_numerator      1391 non-null int64\n",
      "rating_denominator    1391 non-null int64\n",
      "name                  1391 non-null object\n",
      "date                  1391 non-null datetime64[ns]\n",
      "time                  1391 non-null object\n",
      "dog_stage             1391 non-null object\n",
      "dtypes: datetime64[ns](1), int64(2), object(5)\n",
      "memory usage: 137.8+ KB\n"
     ]
    }
   ],
   "source": [
    "t_archives.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1243 entries, 0 to 2073\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    1243 non-null object\n",
      "jpg_url     1243 non-null object\n",
      "img_num     1243 non-null int64\n",
      "p1          1243 non-null object\n",
      "p1_conf     1243 non-null float64\n",
      "p1_dog      1243 non-null bool\n",
      "p2          1243 non-null object\n",
      "p2_conf     1243 non-null float64\n",
      "p2_dog      1243 non-null bool\n",
      "p3          1243 non-null object\n",
      "p3_conf     1243 non-null float64\n",
      "p3_dog      1243 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(1), object(5)\n",
      "memory usage: 100.8+ KB\n"
     ]
    }
   ],
   "source": [
    "predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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 object\n",
      "favorite_count    2326 non-null int64\n",
      "retweet_count     2326 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 54.6+ KB\n"
     ]
    }
   ],
   "source": [
    "json_copy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 12: merge all three datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge the t_archives and predictions datasets\n",
    "twitter_archive_master = pd.merge(t_archives, predictions, on='tweet_id')\n",
    "# Merge the json_copy dataset into the twitter_archive_master dataset\n",
    "twitter_archive_master = pd.merge(twitter_archive_master, json_copy, on='tweet_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 844 entries, 0 to 843\n",
      "Data columns (total 21 columns):\n",
      "tweet_id              844 non-null object\n",
      "source                844 non-null object\n",
      "rating_numerator      844 non-null int64\n",
      "rating_denominator    844 non-null int64\n",
      "name                  844 non-null object\n",
      "date                  844 non-null datetime64[ns]\n",
      "time                  844 non-null object\n",
      "dog_stage             844 non-null object\n",
      "jpg_url               844 non-null object\n",
      "img_num               844 non-null int64\n",
      "p1                    844 non-null object\n",
      "p1_conf               844 non-null float64\n",
      "p1_dog                844 non-null bool\n",
      "p2                    844 non-null object\n",
      "p2_conf               844 non-null float64\n",
      "p2_dog                844 non-null bool\n",
      "p3                    844 non-null object\n",
      "p3_conf               844 non-null float64\n",
      "p3_dog                844 non-null bool\n",
      "favorite_count        844 non-null int64\n",
      "retweet_count         844 non-null int64\n",
      "dtypes: bool(3), datetime64[ns](1), float64(3), int64(5), object(9)\n",
      "memory usage: 127.8+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive_master.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue 13: For each row, generate each maximum p_conf value and the corresponding p and p_dog values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.487574</td>\n",
       "      <td>Irish_setter</td>\n",
       "      <td>0.193054</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.118184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.511319</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.451038</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.957979</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.013884</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.008167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.991650</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.002129</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.001498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.953442</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.013834</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.007958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>whippet</td>\n",
       "      <td>0.626152</td>\n",
       "      <td>borzoi</td>\n",
       "      <td>0.194742</td>\n",
       "      <td>Saluki</td>\n",
       "      <td>0.027351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.714719</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.120184</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.105506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.469760</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.184172</td>\n",
       "      <td>English_setter</td>\n",
       "      <td>0.073482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.700377</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.166511</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.111411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.995026</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>bull_mastiff</td>\n",
       "      <td>0.000903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>basset</td>\n",
       "      <td>0.821664</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.087582</td>\n",
       "      <td>Weimaraner</td>\n",
       "      <td>0.026236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.809197</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.054950</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.038915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.309706</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.186136</td>\n",
       "      <td>Dandie_Dinmont</td>\n",
       "      <td>0.086346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.999201</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.000361</td>\n",
       "      <td>Boston_bull</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Blenheim_spaniel</td>\n",
       "      <td>0.972494</td>\n",
       "      <td>Shih-Tzu</td>\n",
       "      <td>0.006630</td>\n",
       "      <td>Bernese_mountain_dog</td>\n",
       "      <td>0.006239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pug</td>\n",
       "      <td>0.369275</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.265835</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.134697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.259916</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.198451</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.127725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>chow</td>\n",
       "      <td>0.822103</td>\n",
       "      <td>Norwich_terrier</td>\n",
       "      <td>0.106075</td>\n",
       "      <td>Norfolk_terrier</td>\n",
       "      <td>0.037348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>pug</td>\n",
       "      <td>0.546406</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.404291</td>\n",
       "      <td>Brabancon_griffon</td>\n",
       "      <td>0.044002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.707046</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.199396</td>\n",
       "      <td>Norwegian_elkhound</td>\n",
       "      <td>0.049148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.610946</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.299603</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.063020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.943082</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.032409</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.005501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>chow</td>\n",
       "      <td>0.987997</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.007099</td>\n",
       "      <td>Newfoundland</td>\n",
       "      <td>0.002140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.850050</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.074257</td>\n",
       "      <td>flat-coated_retriever</td>\n",
       "      <td>0.015579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.542982</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.251988</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.107699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.762211</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.098985</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.017199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Saluki</td>\n",
       "      <td>0.529012</td>\n",
       "      <td>Afghan_hound</td>\n",
       "      <td>0.250003</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.160739</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.360159</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.293744</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.270673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.412893</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.312958</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.071960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.435254</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.307407</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.033158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.633037</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.146391</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.046184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.125176</td>\n",
       "      <td>standard_poodle</td>\n",
       "      <td>0.084572</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.081347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.996709</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.000712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>Tibetan_terrier</td>\n",
       "      <td>0.676376</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.054933</td>\n",
       "      <td>Yorkshire_terrier</td>\n",
       "      <td>0.040576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.825670</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.056639</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.054018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>Saint_Bernard</td>\n",
       "      <td>0.381377</td>\n",
       "      <td>Leonberg</td>\n",
       "      <td>0.127998</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.069357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>papillon</td>\n",
       "      <td>0.557009</td>\n",
       "      <td>Border_collie</td>\n",
       "      <td>0.271963</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.073473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.462556</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.454937</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.024193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.694904</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.232006</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.050635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.949892</td>\n",
       "      <td>Irish_setter</td>\n",
       "      <td>0.010564</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.005821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.266437</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.243223</td>\n",
       "      <td>bluetick</td>\n",
       "      <td>0.072806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>giant_schnauzer</td>\n",
       "      <td>0.841265</td>\n",
       "      <td>Lakeland_terrier</td>\n",
       "      <td>0.052744</td>\n",
       "      <td>Irish_water_spaniel</td>\n",
       "      <td>0.034402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.140173</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.134094</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.081900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.471351</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.091992</td>\n",
       "      <td>standard_poodle</td>\n",
       "      <td>0.087385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.535411</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.087544</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.062050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.741563</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.057866</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.039125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.959514</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.005370</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.002641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.483682</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.092494</td>\n",
       "      <td>Brabancon_griffon</td>\n",
       "      <td>0.057495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.825678</td>\n",
       "      <td>vizsla</td>\n",
       "      <td>0.090998</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.022956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>miniature_schnauzer</td>\n",
       "      <td>0.496953</td>\n",
       "      <td>standard_schnauzer</td>\n",
       "      <td>0.285276</td>\n",
       "      <td>giant_schnauzer</td>\n",
       "      <td>0.073764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>English_setter</td>\n",
       "      <td>0.328792</td>\n",
       "      <td>Brittany_spaniel</td>\n",
       "      <td>0.283545</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.057461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.546933</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.165255</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.095959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.975401</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.008687</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.005394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.043209</td>\n",
       "      <td>West_Highland_white_terrier</td>\n",
       "      <td>0.038906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.278954</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.237612</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.171106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>curly-coated_retriever</td>\n",
       "      <td>0.322084</td>\n",
       "      <td>giant_schnauzer</td>\n",
       "      <td>0.287955</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.166331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.149680</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.148258</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.142860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>844 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           p1   p1_conf                              p2  \\\n",
       "0                   Chihuahua  0.323581                        Pekinese   \n",
       "1                   Chihuahua  0.716012                        malamute   \n",
       "2                      basset  0.555712                English_springer   \n",
       "3               Irish_terrier  0.487574                    Irish_setter   \n",
       "4                    Pembroke  0.511319                        Cardigan   \n",
       "5                     Samoyed  0.957979                      Pomeranian   \n",
       "6              French_bulldog  0.991650                           boxer   \n",
       "7            golden_retriever  0.953442              Labrador_retriever   \n",
       "8                     whippet  0.626152                          borzoi   \n",
       "9            golden_retriever  0.714719                 Tibetan_mastiff   \n",
       "10           golden_retriever  0.469760              Labrador_retriever   \n",
       "11             Siberian_husky  0.700377                      Eskimo_dog   \n",
       "12             French_bulldog  0.995026                             pug   \n",
       "13                     basset  0.821664                         redbone   \n",
       "14                   Pembroke  0.809197             Rhodesian_ridgeback   \n",
       "15                     kuvasz  0.309706                  Great_Pyrenees   \n",
       "16             French_bulldog  0.999201                       Chihuahua   \n",
       "17           Blenheim_spaniel  0.972494                        Shih-Tzu   \n",
       "18                        pug  0.369275              Labrador_retriever   \n",
       "19          Italian_greyhound  0.259916  American_Staffordshire_terrier   \n",
       "20                       chow  0.822103                 Norwich_terrier   \n",
       "21                        pug  0.546406                  French_bulldog   \n",
       "22            German_shepherd  0.707046                        malinois   \n",
       "23                   Doberman  0.610946              miniature_pinscher   \n",
       "24           golden_retriever  0.943082              Labrador_retriever   \n",
       "25                       chow  0.987997                 Tibetan_mastiff   \n",
       "26         Labrador_retriever  0.850050        Chesapeake_Bay_retriever   \n",
       "27                   Pembroke  0.542982                       Chihuahua   \n",
       "28           golden_retriever  0.762211              Labrador_retriever   \n",
       "29                     Saluki  0.529012                    Afghan_hound   \n",
       "..                        ...       ...                             ...   \n",
       "814                    kuvasz  0.360159                golden_retriever   \n",
       "815                Pomeranian  0.412893                        Pembroke   \n",
       "816                  Pembroke  0.435254                        Cardigan   \n",
       "817          golden_retriever  0.633037                          kuvasz   \n",
       "818         Italian_greyhound  0.125176                 standard_poodle   \n",
       "819        Labrador_retriever  0.996709                golden_retriever   \n",
       "820           Tibetan_terrier  0.676376                   Irish_terrier   \n",
       "821        Labrador_retriever  0.825670                  French_bulldog   \n",
       "822             Saint_Bernard  0.381377                        Leonberg   \n",
       "823                  papillon  0.557009                   Border_collie   \n",
       "824          golden_retriever  0.462556              Labrador_retriever   \n",
       "825            Siberian_husky  0.694904                        malamute   \n",
       "826          golden_retriever  0.949892                    Irish_setter   \n",
       "827                toy_poodle  0.266437                       Chihuahua   \n",
       "828           giant_schnauzer  0.841265                Lakeland_terrier   \n",
       "829        miniature_pinscher  0.140173                      Rottweiler   \n",
       "830          golden_retriever  0.471351                miniature_poodle   \n",
       "831                toy_poodle  0.535411                      Pomeranian   \n",
       "832                  Pembroke  0.741563                       Chihuahua   \n",
       "833                 Chihuahua  0.959514               Italian_greyhound   \n",
       "834                 Chihuahua  0.483682                             pug   \n",
       "835  Chesapeake_Bay_retriever  0.825678                          vizsla   \n",
       "836       miniature_schnauzer  0.496953              standard_schnauzer   \n",
       "837            English_setter  0.328792                Brittany_spaniel   \n",
       "838          miniature_poodle  0.546933                  cocker_spaniel   \n",
       "839           German_shepherd  0.975401                          beagle   \n",
       "840              Ibizan_hound  0.044333                        Pembroke   \n",
       "841                 dalmatian  0.278954              Labrador_retriever   \n",
       "842    curly-coated_retriever  0.322084                 giant_schnauzer   \n",
       "843               toy_terrier  0.149680                        papillon   \n",
       "\n",
       "      p2_conf                           p3   p3_conf  \n",
       "0    0.090647                     papillon  0.068957  \n",
       "1    0.078253                       kelpie  0.031379  \n",
       "2    0.225770  German_short-haired_pointer  0.175219  \n",
       "3    0.193054     Chesapeake_Bay_retriever  0.118184  \n",
       "4    0.451038                    Chihuahua  0.029248  \n",
       "5    0.013884                         chow  0.008167  \n",
       "6    0.002129    Staffordshire_bullterrier  0.001498  \n",
       "7    0.013834                      redbone  0.007958  \n",
       "8    0.194742                       Saluki  0.027351  \n",
       "9    0.120184           Labrador_retriever  0.105506  \n",
       "10   0.184172               English_setter  0.073482  \n",
       "11   0.166511                     malamute  0.111411  \n",
       "12   0.000932                 bull_mastiff  0.000903  \n",
       "13   0.087582                   Weimaraner  0.026236  \n",
       "14   0.054950                       beagle  0.038915  \n",
       "15   0.186136               Dandie_Dinmont  0.086346  \n",
       "16   0.000361                  Boston_bull  0.000076  \n",
       "17   0.006630         Bernese_mountain_dog  0.006239  \n",
       "18   0.265835                       kuvasz  0.134697  \n",
       "19   0.198451    Staffordshire_bullterrier  0.127725  \n",
       "20   0.106075              Norfolk_terrier  0.037348  \n",
       "21   0.404291            Brabancon_griffon  0.044002  \n",
       "22   0.199396           Norwegian_elkhound  0.049148  \n",
       "23   0.299603                       kelpie  0.063020  \n",
       "24   0.032409                       kuvasz  0.005501  \n",
       "25   0.007099                 Newfoundland  0.002140  \n",
       "26   0.074257        flat-coated_retriever  0.015579  \n",
       "27   0.251988                     Cardigan  0.107699  \n",
       "28   0.098985               cocker_spaniel  0.017199  \n",
       "29   0.250003             golden_retriever  0.160739  \n",
       "..        ...                          ...       ...  \n",
       "814  0.293744           Labrador_retriever  0.270673  \n",
       "815  0.312958                    Chihuahua  0.071960  \n",
       "816  0.307407               cocker_spaniel  0.033158  \n",
       "817  0.146391           Labrador_retriever  0.046184  \n",
       "818  0.084572               cocker_spaniel  0.081347  \n",
       "819  0.001688                       beagle  0.000712  \n",
       "820  0.054933            Yorkshire_terrier  0.040576  \n",
       "821  0.056639    Staffordshire_bullterrier  0.054018  \n",
       "822  0.127998             golden_retriever  0.069357  \n",
       "823  0.271963                       collie  0.073473  \n",
       "824  0.454937                       kuvasz  0.024193  \n",
       "825  0.232006                   Eskimo_dog  0.050635  \n",
       "826  0.010564     Chesapeake_Bay_retriever  0.005821  \n",
       "827  0.243223                     bluetick  0.072806  \n",
       "828  0.052744          Irish_water_spaniel  0.034402  \n",
       "829  0.134094                       beagle  0.081900  \n",
       "830  0.091992              standard_poodle  0.087385  \n",
       "831  0.087544             miniature_poodle  0.062050  \n",
       "832  0.057866                   toy_poodle  0.039125  \n",
       "833  0.005370                   Pomeranian  0.002641  \n",
       "834  0.092494            Brabancon_griffon  0.057495  \n",
       "835  0.090998                       kelpie  0.022956  \n",
       "836  0.285276              giant_schnauzer  0.073764  \n",
       "837  0.283545                 Ibizan_hound  0.057461  \n",
       "838  0.165255                   toy_poodle  0.095959  \n",
       "839  0.008687                   bloodhound  0.005394  \n",
       "840  0.043209  West_Highland_white_terrier  0.038906  \n",
       "841  0.237612               Great_Pyrenees  0.171106  \n",
       "842  0.287955           Labrador_retriever  0.166331  \n",
       "843  0.148258                    Chihuahua  0.142860  \n",
       "\n",
       "[844 rows x 6 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#separate the p and p_conf columns from the twitter_archive_master dataset \n",
    "twitter_archive_master_1=twitter_archive_master[['tweet_id','source','rating_numerator','rating_denominator', 'name','date','time','dog_stage','jpg_url','img_num','favorite_count','retweet_count']]\n",
    "twitter_archive_master_2=twitter_archive_master[['p1', 'p1_conf','p2','p2_conf','p3','p3_conf']]\n",
    "twitter_archive_master_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find the maximum p_conf values\n",
    "#reference: https://stackoverflow.com/questions/66772187/python-pandas-find-cell-next-to-a-known-cell\n",
    "max_p_conf=twitter_archive_master_2[['p1_conf','p2_conf','p3_conf']].max(axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/66772187/python-pandas-find-cell-next-to-a-known-cell\n",
    "#print maximum p_conf values and corresponding p values renamed as \"dog_breed\"\n",
    "max_colums = twitter_archive_master_2.loc[:, twitter_archive_master_2.dtypes == float].idxmax(axis=1)\n",
    "max_labels = max_colums.apply(twitter_archive_master_2.columns.get_loc) - 1\n",
    "\n",
    "max_colums = iter(max_colums.values)\n",
    "max_labels = iter(max_labels.values)\n",
    "dog_predictions= pd.DataFrame(\n",
    "        twitter_archive_master_2.apply(\n",
    "            lambda x: {\n",
    "                \"dog_breed\": x[next(max_labels)],\n",
    "                \"p_conf\": x[next(max_colums)],\n",
    "            },\n",
    "            axis=1,\n",
    "        ).tolist()\n",
    "    )\n",
    "dog_predictions.to_csv('dog_predictions.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>dog_breed</th>\n",
       "      <th>p_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.487574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.511319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.957979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.991650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.953442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>whippet</td>\n",
       "      <td>0.626152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.714719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.469760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.700377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.995026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>basset</td>\n",
       "      <td>0.821664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.809197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.309706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.999201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Blenheim_spaniel</td>\n",
       "      <td>0.972494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pug</td>\n",
       "      <td>0.369275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.259916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>chow</td>\n",
       "      <td>0.822103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>pug</td>\n",
       "      <td>0.546406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.707046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.610946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.943082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>chow</td>\n",
       "      <td>0.987997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.850050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.542982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.762211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Saluki</td>\n",
       "      <td>0.529012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.360159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.412893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.435254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.633037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.125176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.996709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>Tibetan_terrier</td>\n",
       "      <td>0.676376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.825670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>Saint_Bernard</td>\n",
       "      <td>0.381377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>papillon</td>\n",
       "      <td>0.557009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.462556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.694904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.949892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.266437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>giant_schnauzer</td>\n",
       "      <td>0.841265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.140173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.471351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.535411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.741563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.959514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.483682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.825678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>miniature_schnauzer</td>\n",
       "      <td>0.496953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>English_setter</td>\n",
       "      <td>0.328792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.546933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.975401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.044333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.278954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>curly-coated_retriever</td>\n",
       "      <td>0.322084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.149680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>844 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    dog_breed    p_conf\n",
       "0                   Chihuahua  0.323581\n",
       "1                   Chihuahua  0.716012\n",
       "2                      basset  0.555712\n",
       "3               Irish_terrier  0.487574\n",
       "4                    Pembroke  0.511319\n",
       "5                     Samoyed  0.957979\n",
       "6              French_bulldog  0.991650\n",
       "7            golden_retriever  0.953442\n",
       "8                     whippet  0.626152\n",
       "9            golden_retriever  0.714719\n",
       "10           golden_retriever  0.469760\n",
       "11             Siberian_husky  0.700377\n",
       "12             French_bulldog  0.995026\n",
       "13                     basset  0.821664\n",
       "14                   Pembroke  0.809197\n",
       "15                     kuvasz  0.309706\n",
       "16             French_bulldog  0.999201\n",
       "17           Blenheim_spaniel  0.972494\n",
       "18                        pug  0.369275\n",
       "19          Italian_greyhound  0.259916\n",
       "20                       chow  0.822103\n",
       "21                        pug  0.546406\n",
       "22            German_shepherd  0.707046\n",
       "23                   Doberman  0.610946\n",
       "24           golden_retriever  0.943082\n",
       "25                       chow  0.987997\n",
       "26         Labrador_retriever  0.850050\n",
       "27                   Pembroke  0.542982\n",
       "28           golden_retriever  0.762211\n",
       "29                     Saluki  0.529012\n",
       "..                        ...       ...\n",
       "814                    kuvasz  0.360159\n",
       "815                Pomeranian  0.412893\n",
       "816                  Pembroke  0.435254\n",
       "817          golden_retriever  0.633037\n",
       "818         Italian_greyhound  0.125176\n",
       "819        Labrador_retriever  0.996709\n",
       "820           Tibetan_terrier  0.676376\n",
       "821        Labrador_retriever  0.825670\n",
       "822             Saint_Bernard  0.381377\n",
       "823                  papillon  0.557009\n",
       "824          golden_retriever  0.462556\n",
       "825            Siberian_husky  0.694904\n",
       "826          golden_retriever  0.949892\n",
       "827                toy_poodle  0.266437\n",
       "828           giant_schnauzer  0.841265\n",
       "829        miniature_pinscher  0.140173\n",
       "830          golden_retriever  0.471351\n",
       "831                toy_poodle  0.535411\n",
       "832                  Pembroke  0.741563\n",
       "833                 Chihuahua  0.959514\n",
       "834                 Chihuahua  0.483682\n",
       "835  Chesapeake_Bay_retriever  0.825678\n",
       "836       miniature_schnauzer  0.496953\n",
       "837            English_setter  0.328792\n",
       "838          miniature_poodle  0.546933\n",
       "839           German_shepherd  0.975401\n",
       "840              Ibizan_hound  0.044333\n",
       "841                 dalmatian  0.278954\n",
       "842    curly-coated_retriever  0.322084\n",
       "843               toy_terrier  0.149680\n",
       "\n",
       "[844 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_predictions=pd.read_csv('dog_predictions.csv')\n",
    "dog_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 844 entries, 0 to 843\n",
      "Data columns (total 14 columns):\n",
      "tweet_id              844 non-null object\n",
      "source                844 non-null object\n",
      "rating_numerator      844 non-null int64\n",
      "rating_denominator    844 non-null int64\n",
      "name                  844 non-null object\n",
      "date                  844 non-null datetime64[ns]\n",
      "time                  844 non-null object\n",
      "dog_stage             844 non-null object\n",
      "jpg_url               844 non-null object\n",
      "img_num               844 non-null int64\n",
      "favorite_count        844 non-null int64\n",
      "retweet_count         844 non-null int64\n",
      "dog_breed             844 non-null object\n",
      "p_conf                844 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(5), object(7)\n",
      "memory usage: 138.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#Merge dog_predictions dataset with twitter_archive_master_1 dataset\n",
    "t_archive_master=pd.merge(twitter_archive_master_1, dog_predictions, left_index=True, right_index=True)\n",
    "t_archive_master.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Data\n",
    "Save gathered, assessed, and cleaned master dataset to a CSV file named \"twitter_archive_master.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_archive_master.to_csv('twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>source</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>dog_breed</th>\n",
       "      <th>p_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>00:17:27</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>29228</td>\n",
       "      <td>5273</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>00:18:03</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>21976</td>\n",
       "      <td>3464</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>2017-07-29</td>\n",
       "      <td>16:00:24</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>35183</td>\n",
       "      <td>7718</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>890609185150312448</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Zoey</td>\n",
       "      <td>2017-07-27</td>\n",
       "      <td>16:25:51</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>24432</td>\n",
       "      <td>3604</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.487574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Cassie</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>15:59:51</td>\n",
       "      <td>doggo</td>\n",
       "      <td>https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>27851</td>\n",
       "      <td>6070</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.511319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>890006608113172480</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Koda</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>00:31:25</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>26943</td>\n",
       "      <td>6114</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.957979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>889638837579907072</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Ted</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>00:10:02</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFihzFfXsAYGDPR.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>23580</td>\n",
       "      <td>3700</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.991650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Stuart</td>\n",
       "      <td>2017-07-24</td>\n",
       "      <td>17:02:04</td>\n",
       "      <td>puppo</td>\n",
       "      <td>https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>13312</td>\n",
       "      <td>1874</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.953442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>2017-07-24</td>\n",
       "      <td>00:19:32</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/88927...</td>\n",
       "      <td>1</td>\n",
       "      <td>22030</td>\n",
       "      <td>4420</td>\n",
       "      <td>whippet</td>\n",
       "      <td>0.626152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>888917238123831296</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Jim</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>00:22:39</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>25533</td>\n",
       "      <td>3742</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.714719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>888804989199671297</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Zeke</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>16:56:37</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFWra-3VYAA2piG.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>22395</td>\n",
       "      <td>3514</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.469760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>888554962724278272</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Ralphus</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>00:23:06</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFTH_O-UQAACu20.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>17254</td>\n",
       "      <td>2863</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.700377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>888078434458587136</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>2017-07-20</td>\n",
       "      <td>16:49:33</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFMWn56WsAAkA7B.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>19104</td>\n",
       "      <td>2885</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.995026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>887705289381826560</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jeffrey</td>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>16:06:48</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFHDQBbXgAEqY7t.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>26532</td>\n",
       "      <td>4519</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.821664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>887473957103951883</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Canela</td>\n",
       "      <td>2017-07-19</td>\n",
       "      <td>00:47:34</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>59980</td>\n",
       "      <td>14953</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.809197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>886736880519319552</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Mingus</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>23:58:41</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DE5Se8FXcAAJFx4.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>10454</td>\n",
       "      <td>2616</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.309706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Roscoe</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>23:25:31</td>\n",
       "      <td>pupper</td>\n",
       "      <td>https://pbs.twimg.com/media/DE0BTnQUwAApKEH.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>18488</td>\n",
       "      <td>2613</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.999201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>885984800019947520</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Jimbo</td>\n",
       "      <td>2017-07-14</td>\n",
       "      <td>22:10:11</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DEumeWWV0AA-Z61.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>28498</td>\n",
       "      <td>5590</td>\n",
       "      <td>Blenheim_spaniel</td>\n",
       "      <td>0.972494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>885528943205470208</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Maisey</td>\n",
       "      <td>2017-07-13</td>\n",
       "      <td>15:58:47</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DEoH3yvXgAAzQtS.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>31487</td>\n",
       "      <td>5306</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.369275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>884925521741709313</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Earl</td>\n",
       "      <td>2017-07-12</td>\n",
       "      <td>00:01:00</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DEfjEaNXkAAtPlj.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>67029</td>\n",
       "      <td>14945</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.259916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>884876753390489601</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Lola</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>20:47:12</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DEe2tZXXkAAwyX3.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>24302</td>\n",
       "      <td>4639</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.822103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>884562892145688576</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Kevin</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>00:00:02</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DEaZQkfXUAEC7qB.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>21242</td>\n",
       "      <td>3896</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.546406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>884162670584377345</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Yogi</td>\n",
       "      <td>2017-07-09</td>\n",
       "      <td>21:29:42</td>\n",
       "      <td>doggo</td>\n",
       "      <td>https://pbs.twimg.com/media/DEUtQbzW0AUTv_o.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>17880</td>\n",
       "      <td>2493</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.707046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>883838122936631299</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Noah</td>\n",
       "      <td>2017-07-09</td>\n",
       "      <td>00:00:04</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DEQGFgAXUAAEvfi.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>19093</td>\n",
       "      <td>2850</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.610946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>883482846933004288</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>Bella</td>\n",
       "      <td>2017-07-08</td>\n",
       "      <td>00:28:19</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DELC9dZXUAADqUk.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>40051</td>\n",
       "      <td>8223</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.943082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>883360690899218434</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Grizzwald</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>16:22:55</td>\n",
       "      <td>floofer</td>\n",
       "      <td>https://pbs.twimg.com/media/DEJT3FeXoAAtwUy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>19880</td>\n",
       "      <td>3061</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.987997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>882762694511734784</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Gus</td>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>00:46:41</td>\n",
       "      <td>pupper</td>\n",
       "      <td>https://pbs.twimg.com/media/DEAz_HHXsAA-p_z.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>24699</td>\n",
       "      <td>4078</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.850050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>882627270321602560</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Stanley</td>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>15:48:34</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DD-40X3WAAAJPU5.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>24531</td>\n",
       "      <td>5044</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.542982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>882268110199369728</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Alfy</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>16:01:23</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DD5yKdPW0AArzX8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>39049</td>\n",
       "      <td>9584</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.762211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>881666595344535552</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Gary</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>00:11:11</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/DDxPFwbWAAEbVVR.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>44398</td>\n",
       "      <td>8913</td>\n",
       "      <td>Saluki</td>\n",
       "      <td>0.529012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>667728196545200128</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Olive</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>15:36:22</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUQ_QahUAAAVQjn.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>323</td>\n",
       "      <td>126</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.360159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>667544320556335104</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Kial</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>03:25:43</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUOYBbbWIAAXQGU.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>760</td>\n",
       "      <td>456</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.412893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>667534815156183040</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Frank</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>02:47:56</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUOPYI5UcAAj_nO.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>716</td>\n",
       "      <td>443</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.435254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>667530908589760512</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Naphaniel</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>02:32:25</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUOL0uGUkAAx7yh.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>409</td>\n",
       "      <td>206</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.633037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>667517642048163840</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Dook</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>01:39:42</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUN_wiBUkAAakT0.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>165</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.125176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>667502640335572993</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Hall</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>00:40:05</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUNyHTMUYAAQVch.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>485</td>\n",
       "      <td>193</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.996709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>667455448082227200</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Reese</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>21:32:34</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUNHMXTU8AAS3HH.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>53</td>\n",
       "      <td>Tibetan_terrier</td>\n",
       "      <td>0.676376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>667453023279554560</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Cupcake</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>21:22:56</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUNE_OSUwAAdHhX.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>279</td>\n",
       "      <td>73</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.825670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>667405339315146752</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Biden</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>18:13:27</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUMZnmhUEAEbtis.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>410</td>\n",
       "      <td>194</td>\n",
       "      <td>Saint_Bernard</td>\n",
       "      <td>0.381377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>667393430834667520</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Fwed</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>17:26:08</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUMOyd3XIAAl13H.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>50</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.557009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>667211855547486208</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Genevieve</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>05:24:37</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUJppKJWoAA75NP.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>425</td>\n",
       "      <td>209</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.462556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>667200525029539841</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Joshwa</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>04:39:35</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUJfVMPXIAAgbue.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>544</td>\n",
       "      <td>227</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.694904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>667182792070062081</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Timison</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>03:29:07</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUJPNjOWsAAZRqP.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>12495</td>\n",
       "      <td>5250</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.949892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>667174963120574464</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Clarence</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>02:58:01</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUJIFoJWsAAL3Dc.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>218</td>\n",
       "      <td>69</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.266437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>667171260800061440</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>02:43:18</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUJEuRIXIAAPDLt.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>186</td>\n",
       "      <td>71</td>\n",
       "      <td>giant_schnauzer</td>\n",
       "      <td>0.841265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>667165590075940865</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Churlie</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>02:20:46</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUI_kHBWsAAAef5.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>2376</td>\n",
       "      <td>964</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.140173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>667160273090932737</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Bradlay</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>01:59:39</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUI6uuaW4AAvCIs.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>219</td>\n",
       "      <td>50</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.471351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>667152164079423490</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Pipsy</td>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>01:27:25</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUIzWk_UwAAfUNq.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>42142</td>\n",
       "      <td>14563</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.535411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>667119796878725120</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Gabe</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>23:18:48</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUIV6F7XIAA1tAM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>284</td>\n",
       "      <td>105</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.741563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>667090893657276420</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Clybe</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>21:23:57</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUH7oLuUsAELWib.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>286</td>\n",
       "      <td>105</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.959514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>667073648344346624</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Dave</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>20:15:26</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUHr8WbWEAEBPgf.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>103</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.483682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>667062181243039745</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Keet</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>19:29:52</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUHhgvHUAAA4aB0.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>45</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.825678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>666817836334096384</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Jeph</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>03:18:55</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUEDSMEWEAAuXVZ.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>442</td>\n",
       "      <td>209</td>\n",
       "      <td>miniature_schnauzer</td>\n",
       "      <td>0.496953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>666804364988780544</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Jockson</td>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>02:25:23</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUD3A7YWoAA82N0.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>78</td>\n",
       "      <td>English_setter</td>\n",
       "      <td>0.328792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>666739327293083650</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Lugan</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>22:06:57</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUC74aTWoAInZey.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>59</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.546933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>666691418707132416</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Christoper</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>18:56:35</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUCQTpEWEAA7EDz.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>41</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.975401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>666644823164719104</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Jimothy</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>15:51:26</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CUBl6IwVAAA9_zT.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "      <td>68</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.044333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>666454714377183233</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Kreggory</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>03:16:00</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CT-5Bs-WUAA2JeC.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>447</td>\n",
       "      <td>179</td>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.278954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>666447344410484738</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Scout</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>02:46:43</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CT-yU5QWwAEjLX5.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>curly-coated_retriever</td>\n",
       "      <td>0.322084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>666418789513326592</td>\n",
       "      <td>Twitter for Iphone</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Walter</td>\n",
       "      <td>2015-11-17</td>\n",
       "      <td>00:53:15</td>\n",
       "      <td>None</td>\n",
       "      <td>https://pbs.twimg.com/media/CT-YWb7U8AA7QnN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>39</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.149680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>844 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id              source  rating_numerator  \\\n",
       "0    892177421306343426  Twitter for Iphone                13   \n",
       "1    891815181378084864  Twitter for Iphone                12   \n",
       "2    891327558926688256  Twitter for Iphone                12   \n",
       "3    890609185150312448  Twitter for Iphone                13   \n",
       "4    890240255349198849  Twitter for Iphone                14   \n",
       "5    890006608113172480  Twitter for Iphone                13   \n",
       "6    889638837579907072  Twitter for Iphone                12   \n",
       "7    889531135344209921  Twitter for Iphone                13   \n",
       "8    889278841981685760  Twitter for Iphone                13   \n",
       "9    888917238123831296  Twitter for Iphone                12   \n",
       "10   888804989199671297  Twitter for Iphone                13   \n",
       "11   888554962724278272  Twitter for Iphone                13   \n",
       "12   888078434458587136  Twitter for Iphone                12   \n",
       "13   887705289381826560  Twitter for Iphone                13   \n",
       "14   887473957103951883  Twitter for Iphone                13   \n",
       "15   886736880519319552  Twitter for Iphone                13   \n",
       "16   886366144734445568  Twitter for Iphone                12   \n",
       "17   885984800019947520  Twitter for Iphone                12   \n",
       "18   885528943205470208  Twitter for Iphone                13   \n",
       "19   884925521741709313  Twitter for Iphone                12   \n",
       "20   884876753390489601  Twitter for Iphone                13   \n",
       "21   884562892145688576  Twitter for Iphone                13   \n",
       "22   884162670584377345  Twitter for Iphone                12   \n",
       "23   883838122936631299  Twitter for Iphone                12   \n",
       "24   883482846933004288  Twitter for Iphone                 5   \n",
       "25   883360690899218434  Twitter for Iphone                13   \n",
       "26   882762694511734784  Twitter for Iphone                12   \n",
       "27   882627270321602560  Twitter for Iphone                13   \n",
       "28   882268110199369728  Twitter for Iphone                13   \n",
       "29   881666595344535552  Twitter for Iphone                13   \n",
       "..                  ...                 ...               ...   \n",
       "814  667728196545200128  Twitter Web Client                11   \n",
       "815  667544320556335104  Twitter Web Client                10   \n",
       "816  667534815156183040  Twitter Web Client                 8   \n",
       "817  667530908589760512  Twitter Web Client                10   \n",
       "818  667517642048163840  Twitter Web Client                 8   \n",
       "819  667502640335572993  Twitter Web Client                11   \n",
       "820  667455448082227200  Twitter Web Client                 7   \n",
       "821  667453023279554560  Twitter Web Client                11   \n",
       "822  667405339315146752  Twitter for Iphone                 7   \n",
       "823  667393430834667520  Twitter for Iphone                 8   \n",
       "824  667211855547486208  Twitter for Iphone                 9   \n",
       "825  667200525029539841  Twitter for Iphone                11   \n",
       "826  667182792070062081  Twitter for Iphone                10   \n",
       "827  667174963120574464  Twitter for Iphone                 9   \n",
       "828  667171260800061440  Twitter for Iphone                10   \n",
       "829  667165590075940865  Twitter for Iphone                10   \n",
       "830  667160273090932737  Twitter for Iphone                11   \n",
       "831  667152164079423490  Twitter for Iphone                12   \n",
       "832  667119796878725120  Twitter for Iphone                10   \n",
       "833  667090893657276420  Twitter for Iphone                 7   \n",
       "834  667073648344346624  Twitter for Iphone                10   \n",
       "835  667062181243039745  Twitter for Iphone                10   \n",
       "836  666817836334096384  Twitter for Iphone                 9   \n",
       "837  666804364988780544  Twitter for Iphone                 8   \n",
       "838  666739327293083650  Twitter for Iphone                10   \n",
       "839  666691418707132416  Twitter for Iphone                 8   \n",
       "840  666644823164719104  Twitter for Iphone                 9   \n",
       "841  666454714377183233  Twitter for Iphone                10   \n",
       "842  666447344410484738  Twitter for Iphone                 9   \n",
       "843  666418789513326592  Twitter for Iphone                10   \n",
       "\n",
       "     rating_denominator        name        date      time dog_stage  \\\n",
       "0                    10       Tilly  2017-08-01  00:17:27      None   \n",
       "1                    10      Archie  2017-07-31  00:18:03      None   \n",
       "2                    10    Franklin  2017-07-29  16:00:24      None   \n",
       "3                    10        Zoey  2017-07-27  16:25:51      None   \n",
       "4                    10      Cassie  2017-07-26  15:59:51     doggo   \n",
       "5                    10        Koda  2017-07-26  00:31:25      None   \n",
       "6                    10         Ted  2017-07-25  00:10:02      None   \n",
       "7                    10      Stuart  2017-07-24  17:02:04     puppo   \n",
       "8                    10      Oliver  2017-07-24  00:19:32      None   \n",
       "9                    10         Jim  2017-07-23  00:22:39      None   \n",
       "10                   10        Zeke  2017-07-22  16:56:37      None   \n",
       "11                   10     Ralphus  2017-07-22  00:23:06      None   \n",
       "12                   10      Gerald  2017-07-20  16:49:33      None   \n",
       "13                   10     Jeffrey  2017-07-19  16:06:48      None   \n",
       "14                   10      Canela  2017-07-19  00:47:34      None   \n",
       "15                   10      Mingus  2017-07-16  23:58:41      None   \n",
       "16                   10      Roscoe  2017-07-15  23:25:31    pupper   \n",
       "17                   10       Jimbo  2017-07-14  22:10:11      None   \n",
       "18                   10      Maisey  2017-07-13  15:58:47      None   \n",
       "19                   10        Earl  2017-07-12  00:01:00      None   \n",
       "20                   10        Lola  2017-07-11  20:47:12      None   \n",
       "21                   10       Kevin  2017-07-11  00:00:02      None   \n",
       "22                   10        Yogi  2017-07-09  21:29:42     doggo   \n",
       "23                   10        Noah  2017-07-09  00:00:04      None   \n",
       "24                   10       Bella  2017-07-08  00:28:19      None   \n",
       "25                   10   Grizzwald  2017-07-07  16:22:55   floofer   \n",
       "26                   10         Gus  2017-07-06  00:46:41    pupper   \n",
       "27                   10     Stanley  2017-07-05  15:48:34      None   \n",
       "28                   10        Alfy  2017-07-04  16:01:23      None   \n",
       "29                   10        Gary  2017-07-03  00:11:11      None   \n",
       "..                  ...         ...         ...       ...       ...   \n",
       "814                  10       Olive  2015-11-20  15:36:22      None   \n",
       "815                  10        Kial  2015-11-20  03:25:43      None   \n",
       "816                  10       Frank  2015-11-20  02:47:56      None   \n",
       "817                  10   Naphaniel  2015-11-20  02:32:25      None   \n",
       "818                  10        Dook  2015-11-20  01:39:42      None   \n",
       "819                  10        Hall  2015-11-20  00:40:05      None   \n",
       "820                  10       Reese  2015-11-19  21:32:34      None   \n",
       "821                  10     Cupcake  2015-11-19  21:22:56      None   \n",
       "822                  10       Biden  2015-11-19  18:13:27      None   \n",
       "823                  10        Fwed  2015-11-19  17:26:08      None   \n",
       "824                  10   Genevieve  2015-11-19  05:24:37      None   \n",
       "825                  10      Joshwa  2015-11-19  04:39:35      None   \n",
       "826                  10     Timison  2015-11-19  03:29:07      None   \n",
       "827                  10    Clarence  2015-11-19  02:58:01      None   \n",
       "828                  10     Kenneth  2015-11-19  02:43:18      None   \n",
       "829                  10     Churlie  2015-11-19  02:20:46      None   \n",
       "830                  10     Bradlay  2015-11-19  01:59:39      None   \n",
       "831                  10       Pipsy  2015-11-19  01:27:25      None   \n",
       "832                  10        Gabe  2015-11-18  23:18:48      None   \n",
       "833                  10       Clybe  2015-11-18  21:23:57      None   \n",
       "834                  10        Dave  2015-11-18  20:15:26      None   \n",
       "835                  10        Keet  2015-11-18  19:29:52      None   \n",
       "836                  10        Jeph  2015-11-18  03:18:55      None   \n",
       "837                  10     Jockson  2015-11-18  02:25:23      None   \n",
       "838                  10       Lugan  2015-11-17  22:06:57      None   \n",
       "839                  10  Christoper  2015-11-17  18:56:35      None   \n",
       "840                  10     Jimothy  2015-11-17  15:51:26      None   \n",
       "841                  10    Kreggory  2015-11-17  03:16:00      None   \n",
       "842                  10       Scout  2015-11-17  02:46:43      None   \n",
       "843                  10      Walter  2015-11-17  00:53:15      None   \n",
       "\n",
       "                                               jpg_url  img_num  \\\n",
       "0      https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1   \n",
       "1      https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg        1   \n",
       "2      https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg        2   \n",
       "3      https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg        1   \n",
       "4      https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg        1   \n",
       "5      https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg        1   \n",
       "6      https://pbs.twimg.com/media/DFihzFfXsAYGDPR.jpg        1   \n",
       "7      https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg        1   \n",
       "8    https://pbs.twimg.com/ext_tw_video_thumb/88927...        1   \n",
       "9      https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg        1   \n",
       "10     https://pbs.twimg.com/media/DFWra-3VYAA2piG.jpg        1   \n",
       "11     https://pbs.twimg.com/media/DFTH_O-UQAACu20.jpg        3   \n",
       "12     https://pbs.twimg.com/media/DFMWn56WsAAkA7B.jpg        1   \n",
       "13     https://pbs.twimg.com/media/DFHDQBbXgAEqY7t.jpg        1   \n",
       "14     https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg        2   \n",
       "15     https://pbs.twimg.com/media/DE5Se8FXcAAJFx4.jpg        1   \n",
       "16     https://pbs.twimg.com/media/DE0BTnQUwAApKEH.jpg        1   \n",
       "17     https://pbs.twimg.com/media/DEumeWWV0AA-Z61.jpg        1   \n",
       "18     https://pbs.twimg.com/media/DEoH3yvXgAAzQtS.jpg        1   \n",
       "19     https://pbs.twimg.com/media/DEfjEaNXkAAtPlj.jpg        1   \n",
       "20     https://pbs.twimg.com/media/DEe2tZXXkAAwyX3.jpg        1   \n",
       "21     https://pbs.twimg.com/media/DEaZQkfXUAEC7qB.jpg        1   \n",
       "22     https://pbs.twimg.com/media/DEUtQbzW0AUTv_o.jpg        1   \n",
       "23     https://pbs.twimg.com/media/DEQGFgAXUAAEvfi.jpg        1   \n",
       "24     https://pbs.twimg.com/media/DELC9dZXUAADqUk.jpg        1   \n",
       "25     https://pbs.twimg.com/media/DEJT3FeXoAAtwUy.jpg        1   \n",
       "26     https://pbs.twimg.com/media/DEAz_HHXsAA-p_z.jpg        1   \n",
       "27     https://pbs.twimg.com/media/DD-40X3WAAAJPU5.jpg        1   \n",
       "28     https://pbs.twimg.com/media/DD5yKdPW0AArzX8.jpg        1   \n",
       "29     https://pbs.twimg.com/media/DDxPFwbWAAEbVVR.jpg        1   \n",
       "..                                                 ...      ...   \n",
       "814    https://pbs.twimg.com/media/CUQ_QahUAAAVQjn.jpg        1   \n",
       "815    https://pbs.twimg.com/media/CUOYBbbWIAAXQGU.jpg        1   \n",
       "816    https://pbs.twimg.com/media/CUOPYI5UcAAj_nO.jpg        1   \n",
       "817    https://pbs.twimg.com/media/CUOL0uGUkAAx7yh.jpg        1   \n",
       "818    https://pbs.twimg.com/media/CUN_wiBUkAAakT0.jpg        1   \n",
       "819    https://pbs.twimg.com/media/CUNyHTMUYAAQVch.jpg        1   \n",
       "820    https://pbs.twimg.com/media/CUNHMXTU8AAS3HH.jpg        1   \n",
       "821    https://pbs.twimg.com/media/CUNE_OSUwAAdHhX.jpg        1   \n",
       "822    https://pbs.twimg.com/media/CUMZnmhUEAEbtis.jpg        1   \n",
       "823    https://pbs.twimg.com/media/CUMOyd3XIAAl13H.jpg        1   \n",
       "824    https://pbs.twimg.com/media/CUJppKJWoAA75NP.jpg        1   \n",
       "825    https://pbs.twimg.com/media/CUJfVMPXIAAgbue.jpg        1   \n",
       "826    https://pbs.twimg.com/media/CUJPNjOWsAAZRqP.jpg        1   \n",
       "827    https://pbs.twimg.com/media/CUJIFoJWsAAL3Dc.jpg        1   \n",
       "828    https://pbs.twimg.com/media/CUJEuRIXIAAPDLt.jpg        1   \n",
       "829    https://pbs.twimg.com/media/CUI_kHBWsAAAef5.jpg        1   \n",
       "830    https://pbs.twimg.com/media/CUI6uuaW4AAvCIs.jpg        1   \n",
       "831    https://pbs.twimg.com/media/CUIzWk_UwAAfUNq.jpg        1   \n",
       "832    https://pbs.twimg.com/media/CUIV6F7XIAA1tAM.jpg        1   \n",
       "833    https://pbs.twimg.com/media/CUH7oLuUsAELWib.jpg        1   \n",
       "834    https://pbs.twimg.com/media/CUHr8WbWEAEBPgf.jpg        1   \n",
       "835    https://pbs.twimg.com/media/CUHhgvHUAAA4aB0.jpg        1   \n",
       "836    https://pbs.twimg.com/media/CUEDSMEWEAAuXVZ.jpg        1   \n",
       "837    https://pbs.twimg.com/media/CUD3A7YWoAA82N0.jpg        1   \n",
       "838    https://pbs.twimg.com/media/CUC74aTWoAInZey.jpg        1   \n",
       "839    https://pbs.twimg.com/media/CUCQTpEWEAA7EDz.jpg        1   \n",
       "840    https://pbs.twimg.com/media/CUBl6IwVAAA9_zT.jpg        1   \n",
       "841    https://pbs.twimg.com/media/CT-5Bs-WUAA2JeC.jpg        1   \n",
       "842    https://pbs.twimg.com/media/CT-yU5QWwAEjLX5.jpg        1   \n",
       "843    https://pbs.twimg.com/media/CT-YWb7U8AA7QnN.jpg        1   \n",
       "\n",
       "     favorite_count  retweet_count                 dog_breed    p_conf  \n",
       "0             29228           5273                 Chihuahua  0.323581  \n",
       "1             21976           3464                 Chihuahua  0.716012  \n",
       "2             35183           7718                    basset  0.555712  \n",
       "3             24432           3604             Irish_terrier  0.487574  \n",
       "4             27851           6070                  Pembroke  0.511319  \n",
       "5             26943           6114                   Samoyed  0.957979  \n",
       "6             23580           3700            French_bulldog  0.991650  \n",
       "7             13312           1874          golden_retriever  0.953442  \n",
       "8             22030           4420                   whippet  0.626152  \n",
       "9             25533           3742          golden_retriever  0.714719  \n",
       "10            22395           3514          golden_retriever  0.469760  \n",
       "11            17254           2863            Siberian_husky  0.700377  \n",
       "12            19104           2885            French_bulldog  0.995026  \n",
       "13            26532           4519                    basset  0.821664  \n",
       "14            59980          14953                  Pembroke  0.809197  \n",
       "15            10454           2616                    kuvasz  0.309706  \n",
       "16            18488           2613            French_bulldog  0.999201  \n",
       "17            28498           5590          Blenheim_spaniel  0.972494  \n",
       "18            31487           5306                       pug  0.369275  \n",
       "19            67029          14945         Italian_greyhound  0.259916  \n",
       "20            24302           4639                      chow  0.822103  \n",
       "21            21242           3896                       pug  0.546406  \n",
       "22            17880           2493           German_shepherd  0.707046  \n",
       "23            19093           2850                  Doberman  0.610946  \n",
       "24            40051           8223          golden_retriever  0.943082  \n",
       "25            19880           3061                      chow  0.987997  \n",
       "26            24699           4078        Labrador_retriever  0.850050  \n",
       "27            24531           5044                  Pembroke  0.542982  \n",
       "28            39049           9584          golden_retriever  0.762211  \n",
       "29            44398           8913                    Saluki  0.529012  \n",
       "..              ...            ...                       ...       ...  \n",
       "814             323            126                    kuvasz  0.360159  \n",
       "815             760            456                Pomeranian  0.412893  \n",
       "816             716            443                  Pembroke  0.435254  \n",
       "817             409            206          golden_retriever  0.633037  \n",
       "818             331            165         Italian_greyhound  0.125176  \n",
       "819             485            193        Labrador_retriever  0.996709  \n",
       "820             171             53           Tibetan_terrier  0.676376  \n",
       "821             279             73        Labrador_retriever  0.825670  \n",
       "822             410            194             Saint_Bernard  0.381377  \n",
       "823             168             50                  papillon  0.557009  \n",
       "824             425            209          golden_retriever  0.462556  \n",
       "825             544            227            Siberian_husky  0.694904  \n",
       "826           12495           5250          golden_retriever  0.949892  \n",
       "827             218             69                toy_poodle  0.266437  \n",
       "828             186             71           giant_schnauzer  0.841265  \n",
       "829            2376            964        miniature_pinscher  0.140173  \n",
       "830             219             50          golden_retriever  0.471351  \n",
       "831           42142          14563                toy_poodle  0.535411  \n",
       "832             284            105                  Pembroke  0.741563  \n",
       "833             286            105                 Chihuahua  0.959514  \n",
       "834             351            103                 Chihuahua  0.483682  \n",
       "835             192             45  Chesapeake_Bay_retriever  0.825678  \n",
       "836             442            209       miniature_schnauzer  0.496953  \n",
       "837             206             78            English_setter  0.328792  \n",
       "838             207             59          miniature_poodle  0.546933  \n",
       "839             160             41           German_shepherd  0.975401  \n",
       "840             207             68              Ibizan_hound  0.044333  \n",
       "841             447            179                 dalmatian  0.278954  \n",
       "842              90             19    curly-coated_retriever  0.322084  \n",
       "843             107             39               toy_terrier  0.149680  \n",
       "\n",
       "[844 rows x 14 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archive_master=pd.read_csv('twitter_archive_master.csv')\n",
    "t_archive_master"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and Visualizing Data\n",
    "In this section, analyze and visualize your wrangled data. You must produce at least **three (3) insights and one (1) visualization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for Iphone    831\n",
       "Twitter Web Client     10\n",
       "TweetDeck               3\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archive_master.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cooper      9\n",
       "Charlie     7\n",
       "Oliver      7\n",
       "Sadie       6\n",
       "Koda        6\n",
       "Winston     5\n",
       "Leo         5\n",
       "Toby        5\n",
       "Tucker      5\n",
       "Bo          5\n",
       "Gus         4\n",
       "Lucy        4\n",
       "Stanley     4\n",
       "Sophie      4\n",
       "Maggie      4\n",
       "Dave        4\n",
       "Bear        4\n",
       "Finn        4\n",
       "Oscar       4\n",
       "Larry       4\n",
       "Scooter     4\n",
       "Daisy       4\n",
       "Rusty       3\n",
       "Chester     3\n",
       "Duke        3\n",
       "Dexter      3\n",
       "Beau        3\n",
       "Cassie      3\n",
       "Louis       3\n",
       "Jax         3\n",
       "           ..\n",
       "Kara        1\n",
       "Gustav      1\n",
       "Einstein    1\n",
       "Brandy      1\n",
       "Deacon      1\n",
       "Bradlay     1\n",
       "Timber      1\n",
       "Jazz        1\n",
       "Benny       1\n",
       "Willow      1\n",
       "Chip        1\n",
       "Mike        1\n",
       "Walter      1\n",
       "Gunner      1\n",
       "Grizzie     1\n",
       "Fizz        1\n",
       "Sparky      1\n",
       "Vinscent    1\n",
       "Eriq        1\n",
       "Stuart      1\n",
       "Brudge      1\n",
       "Arlen       1\n",
       "Tonks       1\n",
       "Orion       1\n",
       "Dot         1\n",
       "Jed         1\n",
       "Dotsy       1\n",
       "Binky       1\n",
       "Diogi       1\n",
       "Jo          1\n",
       "Name: name, Length: 628, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_archive_master.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "retweet_count tweet_id                                             807106840509214720\n",
      "source                                               Twitter for Iphone\n",
      "rating_numerator                                                     13\n",
      "rating_denominator                                                   10\n",
      "name                                                            Stephan\n",
      "date                                                         2016-12-09\n",
      "time                                                           06:17:20\n",
      "dog_stage                                                          None\n",
      "jpg_url               https://pbs.twimg.com/ext_tw_video_thumb/80710...\n",
      "img_num                                                               1\n",
      "favorite_count                                                   111219\n",
      "retweet_count                                                     51432\n",
      "dog_breed                                                     Chihuahua\n",
      "p_conf                                                          0.50537\n",
      "Name: 219, dtype: object\n"
     ]
    }
   ],
   "source": [
    "col = \"retweet_count\"\n",
    "max_retweet_count = t_archive_master.loc[t_archive_master[col].idxmax()]\n",
    "print (col, max_retweet_count)      \n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "favorite_count tweet_id                                             807106840509214720\n",
      "source                                               Twitter for Iphone\n",
      "rating_numerator                                                     13\n",
      "rating_denominator                                                   10\n",
      "name                                                            Stephan\n",
      "date                                                         2016-12-09\n",
      "time                                                           06:17:20\n",
      "dog_stage                                                          None\n",
      "jpg_url               https://pbs.twimg.com/ext_tw_video_thumb/80710...\n",
      "img_num                                                               1\n",
      "favorite_count                                                   111219\n",
      "retweet_count                                                     51432\n",
      "dog_breed                                                     Chihuahua\n",
      "p_conf                                                          0.50537\n",
      "Name: 219, dtype: object\n"
     ]
    }
   ],
   "source": [
    "col = \"favorite_count\"\n",
    "max_favorite_count = t_archive_master.loc[t_archive_master[col].idxmax()]\n",
    "print (col, max_favorite_count)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "1. The most common source of tweets was Twitter for iphone, and the least common source was TweetDeck\n",
    "\n",
    "2. The most common dog name is Cooper\n",
    "\n",
    "3. A dog named 'Stephan' from the Chihuahua breed had the highest number of likes and retweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f98c6a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of the top 20 dog breeds \n",
    "plt.figure(figsize=(10, 8))\n",
    "breed = t_archive_master.dog_breed.value_counts().nlargest(20).plot(kind = 'bar');\n",
    "breed.set_title('Distribution of the top 20 dog breeds');\n",
    "breed.set_xlabel('Dog Breed');\n",
    "breed.set_ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f9accb38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 20 most common dog names\n",
    "plt.figure(figsize=(10, 8))\n",
    "name_dog = t_archive_master.name.value_counts().nlargest(20).plot(kind = 'bar');\n",
    "name_dog.set_title('Top 20 most common dog names');\n",
    "name_dog.set_xlabel('Dog Name');\n",
    "name_dog.set_ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5f9b76438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of the least 20 dog breeds \n",
    "plt.figure(figsize=(10, 8))\n",
    "least_breed = t_archive_master.dog_breed.value_counts().nsmallest(20).plot(kind = 'bar');\n",
    "least_breed.set_title('Distribution of the least 20 dog breeds');\n",
    "least_breed.set_xlabel('Dog Breed');\n",
    "least_breed.set_ylabel('Frequency');"
   ]
  },
  {
   "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
}