wrangle_act.ipynb

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   "source": [
    "# Project: Wrangling and Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wragle Act \n",
    "In this project, I'll be wrangling a tweet archive dataset of Twitter user [@dog_rates](https://twitter.com/dog_rates), also known as [WeRateDogs](https://en.wikipedia.org/wiki/WeRateDogs). Data will be collected from different sources in different formats to achieve this. \n",
    "\n",
    "The task of this project are as follows:\n",
    "\n",
    "Step 1: Gathering data\n",
    "\n",
    "Step 2: Assessing data\n",
    "\n",
    "Step 3: Cleaning data\n",
    "\n",
    "Step 4: Storing data\n",
    "\n",
    "Step 5: Analyzing, and visualizing data\n",
    "\n",
    "Step 6: Reporting\n",
    "    \n",
    "    data wrangling efforts\n",
    "    data analyses and visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "### Importing Required Packages(libraries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tweepy as twp\n",
    "import time\n",
    "from datetime import timedelta\n",
    "import requests\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import os\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.style.use('dark_background')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Gathering\n",
    "Three dataset will be used for this project. \n",
    "* Archive Data\n",
    "* Image Data\n",
    "* Twitter Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading dataset 1 from local device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loadind archive data to pandas dataframe\n",
    "archv_data = pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading dataset 2 from website using Request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# website url\n",
    "tsv_url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "tsv_response = requests.get(tsv_url) # accessing website\n",
    "\n",
    "# creating a file from the file name on the url \n",
    "with open(tsv_url.split('/')[-1], mode='wb') as tsv_data: \n",
    "    # writting accessed content to the file\n",
    "    tsv_data.write(tsv_response.content) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wrangle_act.ipynb',\n",
       " 'wrangle_report.ipynb',\n",
       " 'act_report.ipynb',\n",
       " 'twitter-archive-enhanced.csv',\n",
       " '.ipynb_checkpoints',\n",
       " 'image-predictions.tsv',\n",
       " 'tweet_json.txt',\n",
       " 'twitter_archive_master.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Listing current directory to confirm download \n",
    "os.listdir('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading data to pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading dataset 2, image data, to pandas dataframe\n",
    "img_data = pd.read_csv('image-predictions.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading dataset 3 from twitter using API (tweepy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Tweets IDs for iteration\n",
    "twt_ids = archv_data.tweet_id.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting my credentials for access and authentication.\n",
    "consumer_key = 'XXXXXXX'\n",
    "consumer_secret = 'XXXXXXXX'\n",
    "access_token = 'XXXXXXXX'\n",
    "access_token_secret = 'XXXXXXX'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Authentication\n",
    "auth = twp.OAuth1UserHandler(\n",
    "   consumer_key, consumer_secret, access_token, access_token_secret\n",
    ")\n",
    "api = twp.API(auth, wait_on_rate_limit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing content and writng to tweet_json.txt file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "index = 0\n",
    "#empty for errors\n",
    "twt_error_lst_1 = []\n",
    "\n",
    "# open txt file for writing\n",
    "with open('tweet_json.txt','w') as opened_book:\n",
    "    for ids in twt_ids:\n",
    "        # starting time\n",
    "        start_time = time.time()\n",
    "        try:\n",
    "            index += 1\n",
    "            # tweet content\n",
    "            twt_contents = api.get_status(ids,tweet_mode = 'extended')\n",
    "            # writing JSON to txt file\n",
    "            json.dump(twt_contents._json, opened_book)\n",
    "            opened_book.write('\\n')\n",
    "            \n",
    "        # catching errors that may occur while accessing contents\n",
    "        except twp.TweepyException as id_error:\n",
    "            # print out ids with error\n",
    "            print(str(index) +\" \"+'ID ' + str(ids) + \" Has an error:\", id_error.response.text)\n",
    "            # appending\n",
    "            twt_error_lst_1.append(ids)\n",
    "        # ending time\n",
    "        end_time = time.time()\n",
    "        \n",
    "print('='*100)\n",
    "m = \"Execution Time: %s secs \" % timedelta(seconds=end_time-start_time)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "len(twt_error_lst_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accesssing again from error list in-case error is due to rate limit or networt interference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "index = 0\n",
    "#empty for errors\n",
    "twt_error_lst_2 = []\n",
    "\n",
    "# open txt file for writing\n",
    "with open('tweet_json.txt','a') as opened_book:\n",
    "    for ids in twt_error_lst_1:\n",
    "        # starting time\n",
    "        start_time = time.time()\n",
    "        try:\n",
    "            index += 1\n",
    "            # tweet content\n",
    "            twt_contents = api.get_status(ids,tweet_mode = 'extended')\n",
    "            # writing JSON to txt file\n",
    "            json.dump(twt_contents._json, opened_book)\n",
    "            opened_book.write('\\n')\n",
    "            \n",
    "        # catching errors that may occur while accessing contents\n",
    "        except twp.TweepyException as id_error:\n",
    "            # print out ids with error\n",
    "            print(str(index) +\" \"+'ID ' + str(ids) + \" Has an error:\", id_error.response.text)\n",
    "            # appending\n",
    "            twt_error_lst_2.append(ids)\n",
    "        # ending time\n",
    "        end_time = time.time()\n",
    "        \n",
    "print('='*100)\n",
    "m = \"Execution Time: {} secs \".format(end_time-start_time)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading data to pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# empty list \n",
    "lst = []\n",
    "\n",
    "# open txt file for reading\n",
    "with open ('tweet_json.txt', 'r') as open_book:\n",
    "    for line in open_book:\n",
    "        each_tweet = json.loads(line)\n",
    "        # getting required parameters\n",
    "        tweet_id = each_tweet['id']\n",
    "        retwt_count = each_tweet['retweet_count']\n",
    "        fav_count = each_tweet['favorite_count']\n",
    "        timestamp = each_tweet['created_at']\n",
    "        # appending to empty list\n",
    "        lst.append({\n",
    "            \"tweet_id\":tweet_id,\n",
    "            \"retweet_count\":retwt_count,\n",
    "            \"favorite_count\":fav_count,\n",
    "            \"timestamp\":timestamp\n",
    "        })\n",
    "# converting to dataframe\n",
    "twtr_data = pd.DataFrame(lst)"
   ]
  },
  {
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   },
   "source": [
    "## Assessing Data\n",
    "* We 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 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",
    "* Tweets data beyond August 1st, 2017 won't be gathered because we don't have image predictions for these tweets and also, there is no access to the algorithm used.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing Archive Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0     892420643555336193                    NaN                  NaN   \n",
       "1     892177421306343426                    NaN                  NaN   \n",
       "2     891815181378084864                    NaN                  NaN   \n",
       "3     891689557279858688                    NaN                  NaN   \n",
       "4     891327558926688256                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "0     2017-08-01 16:23:56 +0000   \n",
       "1     2017-08-01 00:17:27 +0000   \n",
       "2     2017-07-31 00:18:03 +0000   \n",
       "3     2017-07-30 15:58:51 +0000   \n",
       "4     2017-07-29 16:00:24 +0000   \n",
       "...                         ...   \n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "0     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "0     This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1     This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2     This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3     This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4     This is Franklin. He would like you to stop ca...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                          NaN                        NaN   \n",
       "1                          NaN                        NaN   \n",
       "2                          NaN                        NaN   \n",
       "3                          NaN                        NaN   \n",
       "4                          NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "0     https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1     https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2     https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3     https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4     https://twitter.com/dog_rates/status/891327558...                12   \n",
       "...                                                 ...               ...   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name doggo floofer pupper puppo  \n",
       "0                     10   Phineas  None    None   None  None  \n",
       "1                     10     Tilly  None    None   None  None  \n",
       "2                     10    Archie  None    None   None  None  \n",
       "3                     10     Darla  None    None   None  None  \n",
       "4                     10  Franklin  None    None   None  None  \n",
       "...                  ...       ...   ...     ...    ...   ...  \n",
       "2351                  10      None  None    None   None  None  \n",
       "2352                  10         a  None    None   None  None  \n",
       "2353                  10         a  None    None   None  None  \n",
       "2354                  10         a  None    None   None  None  \n",
       "2355                  10      None  None    None   None  None  \n",
       "\n",
       "[2356 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing archive data \n",
    "archv_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# archive data information\n",
    "archv_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.427716e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>7.720400e+17</td>\n",
       "      <td>1.241698e+16</td>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.856705e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>6.236928e+16</td>\n",
       "      <td>9.599254e+16</td>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783989e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>7.186315e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.196279e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>7.804657e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993373e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>8.203146e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>7.874618e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "count  2.356000e+03           7.800000e+01         7.800000e+01   \n",
       "mean   7.427716e+17           7.455079e+17         2.014171e+16   \n",
       "std    6.856705e+16           7.582492e+16         1.252797e+17   \n",
       "min    6.660209e+17           6.658147e+17         1.185634e+07   \n",
       "25%    6.783989e+17           6.757419e+17         3.086374e+08   \n",
       "50%    7.196279e+17           7.038708e+17         4.196984e+09   \n",
       "75%    7.993373e+17           8.257804e+17         4.196984e+09   \n",
       "max    8.924206e+17           8.862664e+17         8.405479e+17   \n",
       "\n",
       "       retweeted_status_id  retweeted_status_user_id  rating_numerator  \\\n",
       "count         1.810000e+02              1.810000e+02       2356.000000   \n",
       "mean          7.720400e+17              1.241698e+16         13.126486   \n",
       "std           6.236928e+16              9.599254e+16         45.876648   \n",
       "min           6.661041e+17              7.832140e+05          0.000000   \n",
       "25%           7.186315e+17              4.196984e+09         10.000000   \n",
       "50%           7.804657e+17              4.196984e+09         11.000000   \n",
       "75%           8.203146e+17              4.196984e+09         12.000000   \n",
       "max           8.874740e+17              7.874618e+17       1776.000000   \n",
       "\n",
       "       rating_denominator  \n",
       "count         2356.000000  \n",
       "mean            10.455433  \n",
       "std              6.745237  \n",
       "min              0.000000  \n",
       "25%             10.000000  \n",
       "50%             10.000000  \n",
       "75%             10.000000  \n",
       "max            170.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# archive data numerical columns summary statistics\n",
    "archv_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>832088576586297345</td>\n",
       "      <td>8.320875e+17</td>\n",
       "      <td>30582082.0</td>\n",
       "      <td>2017-02-16 04:45:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>15</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>433</th>\n",
       "      <td>820690176645140481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-15 17:52:40 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>The floofs have been released I repeat the flo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/820690176...</td>\n",
       "      <td>84</td>\n",
       "      <td>70</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>784</th>\n",
       "      <td>775096608509886464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-11 22:20:06 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: After so many requests, this is...</td>\n",
       "      <td>7.403732e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-06-08 02:41:38 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/740373189...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</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>902</th>\n",
       "      <td>758467244762497024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-28 01:00:57 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Why does this never happen at my front door......</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/758467244...</td>\n",
       "      <td>165</td>\n",
       "      <td>150</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>1068</th>\n",
       "      <td>740373189193256964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-08 02:41:38 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After so many requests, this is Bretagne. She ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/740373189...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "342   832088576586297345           8.320875e+17           30582082.0   \n",
       "433   820690176645140481                    NaN                  NaN   \n",
       "784   775096608509886464                    NaN                  NaN   \n",
       "902   758467244762497024                    NaN                  NaN   \n",
       "1068  740373189193256964                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "342   2017-02-16 04:45:50 +0000   \n",
       "433   2017-01-15 17:52:40 +0000   \n",
       "784   2016-09-11 22:20:06 +0000   \n",
       "902   2016-07-28 01:00:57 +0000   \n",
       "1068  2016-06-08 02:41:38 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "342   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "433   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "784   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "902   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1068  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "342            @docmisterio account started on 11/15/15                  NaN   \n",
       "433   The floofs have been released I repeat the flo...                  NaN   \n",
       "784   RT @dog_rates: After so many requests, this is...         7.403732e+17   \n",
       "902   Why does this never happen at my front door......                  NaN   \n",
       "1068  After so many requests, this is Bretagne. She ...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "342                        NaN                        NaN   \n",
       "433                        NaN                        NaN   \n",
       "784               4.196984e+09  2016-06-08 02:41:38 +0000   \n",
       "902                        NaN                        NaN   \n",
       "1068                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "342                                                 NaN                11   \n",
       "433   https://twitter.com/dog_rates/status/820690176...                84   \n",
       "784   https://twitter.com/dog_rates/status/740373189...                 9   \n",
       "902   https://twitter.com/dog_rates/status/758467244...               165   \n",
       "1068  https://twitter.com/dog_rates/status/740373189...                 9   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper puppo  \n",
       "342                   15  None  None    None   None  None  \n",
       "433                   70  None  None    None   None  None  \n",
       "784                   11  None  None    None   None  None  \n",
       "902                  150  None  None    None   None  None  \n",
       "1068                  11  None  None    None   None  None  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing rows with denominator more than 10\n",
    "archv_data[archv_data.rating_denominator >10].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       745\n",
       "a           55\n",
       "Charlie     12\n",
       "Cooper      11\n",
       "Lucy        11\n",
       "Oliver      11\n",
       "Tucker      10\n",
       "Penny       10\n",
       "Lola        10\n",
       "Winston      9\n",
       "Bo           9\n",
       "Sadie        8\n",
       "the          8\n",
       "Daisy        7\n",
       "Buddy        7\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing name values in archive data \n",
    "archv_data.name.value_counts().head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a',\n",
       " 'actually',\n",
       " 'all',\n",
       " 'an',\n",
       " 'by',\n",
       " 'getting',\n",
       " 'his',\n",
       " 'incredibly',\n",
       " 'infuriating',\n",
       " 'just',\n",
       " 'life',\n",
       " 'light',\n",
       " 'mad',\n",
       " 'my',\n",
       " 'not',\n",
       " 'officially',\n",
       " 'old',\n",
       " 'one',\n",
       " 'quite',\n",
       " 'space',\n",
       " 'such',\n",
       " 'the',\n",
       " 'this',\n",
       " 'unacceptable',\n",
       " 'very'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extracting lowercase word from name column.\n",
    "lst = archv_data.name.str.extractall(r'(\\b[a-z]+\\b)')[0].tolist()\n",
    "a = set(lst)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking suspicious name for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56      Here is a pupper approaching maximum borkdrive...\n",
       "649     Here is a perfect example of someone who has t...\n",
       "801     Guys this is getting so out of hand. We only r...\n",
       "1002    This is a mighty rare blue-tailed hammer sherk...\n",
       "1004    Viewer discretion is advised. This is a terrib...\n",
       "1017    This is a carrot. We only rate dogs. Please on...\n",
       "1049    This is a very rare Great Alaskan Bush Pupper....\n",
       "1193    People please. This is a Deadly Mediterranean ...\n",
       "1207    This is a taco. We only rate dogs. Please only...\n",
       "1340    Here is a heartbreaking scene of an incredible...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing text with name \"a\"\n",
    "name_a = archv_data[archv_data.name == 'a']\n",
    "name_a.text.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_a.text.loc[2287]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### It seems the name extraction caught the wrong string. Let's check for others that are shown in the name value counts above since those seem to be of higher ocurence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "446     RT @dog_rates: This is Bo. He was a very good ...\n",
       "449     This is Bo. He was a very good First Doggo. 14...\n",
       "553     This is Bo. He's going to make me cry. 13/10 p...\n",
       "583     RT @dog_rates: This is Bo. He's a Benedoop Cum...\n",
       "679     This is Bo. He's a West Congolese Bugaboop Snu...\n",
       "686     RT @dog_rates: This is Bo and Ty. Bo eats pape...\n",
       "876     This is Bo and Ty. Bo eats paper and Ty felt l...\n",
       "978     This is Bo. He emanates happiness. 12/10 I cou...\n",
       "1683    This is Bo. He's a Benedoop Cumbersnatch. Seem...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing text with name \"Bo\"\n",
    "name_Bo = archv_data[archv_data.name == 'Bo']\n",
    "name_Bo.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1527    Stop sending in lobsters. This is the final wa...\n",
       "1603    This is the newly formed pupper a capella grou...\n",
       "1797    This is the happiest pupper I've ever seen. 10...\n",
       "1815    This is the saddest/sweetest/best picture I've...\n",
       "2037    This is the best thing I've ever seen so sprea...\n",
       "2212    Never forget this vine. You will not stop watc...\n",
       "2345    This is the happiest dog you will ever see. Ve...\n",
       "2346    Here is the Rand Paul of retrievers folks! He'...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing text with name \"the\"\n",
    "name_the = archv_data[archv_data.name == 'the']\n",
    "name_the.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "759     RT @dog_rates: This is an East African Chalupa...\n",
       "1025    This is an Iraqi Speed Kangaroo. It is not a d...\n",
       "1362    This is an East African Chalupa Seal. We only ...\n",
       "2204    This is an Irish Rigatoni terrier named Berta....\n",
       "2333    This is an extremely rare horned Parthenon. No...\n",
       "2335    This is an Albanian 3 1/2 legged  Episcopalian...\n",
       "2349    This is an odd dog. Hard on the outside but lo...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing text with name \"an\"\n",
    "name_an = archv_data[archv_data.name == 'an']\n",
    "name_an.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### From the above, it is obviousu that all names in lower case are not dog names, they are just random words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>855851453814013952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-22 18:31:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a puppo participating in the #ScienceMa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/855851453...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>854010172552949760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-17 16:34:26 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>At first I thought this was a shy doggo, but i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/854010172...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>floofer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>817777686764523521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-07 16:59:28 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Dido. She's playing the lead role in \"...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/817777686...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Dido</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>808106460588765185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-12 00:29:28 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have Burke (pupper) and Dexter (doggo)...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/808106460...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>802265048156610565</td>\n",
       "      <td>7.331095e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-11-25 21:37:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Like doggo, like pupper version 2. Both 11/10 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/802265048...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</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",
       "191  855851453814013952                    NaN                  NaN   \n",
       "200  854010172552949760                    NaN                  NaN   \n",
       "460  817777686764523521                    NaN                  NaN   \n",
       "531  808106460588765185                    NaN                  NaN   \n",
       "565  802265048156610565           7.331095e+17         4.196984e+09   \n",
       "\n",
       "                     timestamp  \\\n",
       "191  2017-04-22 18:31:02 +0000   \n",
       "200  2017-04-17 16:34:26 +0000   \n",
       "460  2017-01-07 16:59:28 +0000   \n",
       "531  2016-12-12 00:29:28 +0000   \n",
       "565  2016-11-25 21:37:47 +0000   \n",
       "\n",
       "                                                source  \\\n",
       "191  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "200  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "460  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "531  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "565  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                  text  retweeted_status_id  \\\n",
       "191  Here's a puppo participating in the #ScienceMa...                  NaN   \n",
       "200  At first I thought this was a shy doggo, but i...                  NaN   \n",
       "460  This is Dido. She's playing the lead role in \"...                  NaN   \n",
       "531  Here we have Burke (pupper) and Dexter (doggo)...                  NaN   \n",
       "565  Like doggo, like pupper version 2. Both 11/10 ...                  NaN   \n",
       "\n",
       "     retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "191                       NaN                        NaN   \n",
       "200                       NaN                        NaN   \n",
       "460                       NaN                        NaN   \n",
       "531                       NaN                        NaN   \n",
       "565                       NaN                        NaN   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "191  https://twitter.com/dog_rates/status/855851453...                13   \n",
       "200  https://twitter.com/dog_rates/status/854010172...                11   \n",
       "460  https://twitter.com/dog_rates/status/817777686...                13   \n",
       "531  https://twitter.com/dog_rates/status/808106460...                12   \n",
       "565  https://twitter.com/dog_rates/status/802265048...                11   \n",
       "\n",
       "     rating_denominator  name  doggo  floofer  pupper  puppo  \n",
       "191                  10  None  doggo     None    None  puppo  \n",
       "200                  10  None  doggo  floofer    None   None  \n",
       "460                  10  Dido  doggo     None  pupper   None  \n",
       "531                  10  None  doggo     None  pupper   None  \n",
       "565                  10  None  doggo     None  pupper   None  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for dog in two stage\n",
    "archv_data.loc[(archv_data[['doggo', 'floofer','pupper', 'puppo']] != 'None').sum(axis=1)>1] .head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>45</th>\n",
       "      <td>This is Bella. She hopes her smile made you sm...</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>RT @dog_rates: This is Logan, the Chow who liv...</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly...</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. ...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>I've been told there's a slight possibility he...</td>\n",
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      "text/plain": [
       "                                                   text  rating_numerator\n",
       "45    This is Bella. She hopes her smile made you sm...                 5\n",
       "340   RT @dog_rates: This is Logan, the Chow who liv...                75\n",
       "695   This is Logan, the Chow who lived. He solemnly...                75\n",
       "763   This is Sophie. She's a Jubilant Bush Pupper. ...                27\n",
       "1689  I've been told there's a slight possibility he...                 5\n",
       "1712  Here we have uncovered an entire battalion of ...                26"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparing rows where the text has a decimal ratings with the rating_numerator\n",
    "float_rating = archv_data[archv_data.text.str.contains(r\"(\\d+\\.\\d*\\/\\d+)\")\n",
    "                         ][['text','rating_numerator']]\n",
    "float_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking one of the text for more details.\n",
    "float_rating.text.loc[695]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the text above, we can see that the rating for the above row is inaccurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing Image data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "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",
       "    </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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
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       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2075 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "0     666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1     666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2     666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3     666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4     666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "...                  ...                                              ...   \n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0           1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1           1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2           1         German_shepherd  0.596461    True            malinois   \n",
       "3           1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4           1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "...       ...                     ...       ...     ...                 ...   \n",
       "2070        2                  basset  0.555712    True    English_springer   \n",
       "2071        1             paper_towel  0.170278   False  Labrador_retriever   \n",
       "2072        1               Chihuahua  0.716012    True            malamute   \n",
       "2073        1               Chihuahua  0.323581    True            Pekinese   \n",
       "2074        1                  orange  0.097049   False               bagel   \n",
       "\n",
       "       p2_conf  p2_dog                           p3   p3_conf  p3_dog  \n",
       "0     0.156665    True            Shetland_sheepdog  0.061428    True  \n",
       "1     0.074192    True          Rhodesian_ridgeback  0.072010    True  \n",
       "2     0.138584    True                   bloodhound  0.116197    True  \n",
       "3     0.360687    True           miniature_pinscher  0.222752    True  \n",
       "4     0.243682    True                     Doberman  0.154629    True  \n",
       "...        ...     ...                          ...       ...     ...  \n",
       "2070  0.225770    True  German_short-haired_pointer  0.175219    True  \n",
       "2071  0.168086    True                      spatula  0.040836   False  \n",
       "2072  0.078253    True                       kelpie  0.031379    True  \n",
       "2073  0.090647    True                     papillon  0.068957    True  \n",
       "2074  0.085851   False                       banana  0.076110   False  \n",
       "\n",
       "[2075 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing image data\n",
    "img_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# image data information\n",
    "img_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
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       "      <td>https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg</td>\n",
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       "      <td>1</td>\n",
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       "      <th>18</th>\n",
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       "      <td>https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
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       "      <td>https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>three-toed_sloth</td>\n",
       "      <td>0.914671</td>\n",
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       "      <td>otter</td>\n",
       "      <td>0.015250</td>\n",
       "      <td>False</td>\n",
       "      <td>great_grey_owl</td>\n",
       "      <td>0.013207</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>666362758909284353</td>\n",
       "      <td>https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>guinea_pig</td>\n",
       "      <td>0.996496</td>\n",
       "      <td>False</td>\n",
       "      <td>skunk</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>False</td>\n",
       "      <td>hamster</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>880935762899988482</td>\n",
       "      <td>https://pbs.twimg.com/media/DDm2Z5aXUAEDS2u.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>street_sign</td>\n",
       "      <td>0.251801</td>\n",
       "      <td>False</td>\n",
       "      <td>umbrella</td>\n",
       "      <td>0.115123</td>\n",
       "      <td>False</td>\n",
       "      <td>traffic_light</td>\n",
       "      <td>0.069534</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>881268444196462592</td>\n",
       "      <td>https://pbs.twimg.com/media/DDrk-f9WAAI-WQv.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>tusker</td>\n",
       "      <td>0.473303</td>\n",
       "      <td>False</td>\n",
       "      <td>Indian_elephant</td>\n",
       "      <td>0.245646</td>\n",
       "      <td>False</td>\n",
       "      <td>ibex</td>\n",
       "      <td>0.055661</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>convertible</td>\n",
       "      <td>0.738995</td>\n",
       "      <td>False</td>\n",
       "      <td>sports_car</td>\n",
       "      <td>0.139952</td>\n",
       "      <td>False</td>\n",
       "      <td>car_wheel</td>\n",
       "      <td>0.044173</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>887517139158093824</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/88751...</td>\n",
       "      <td>1</td>\n",
       "      <td>limousine</td>\n",
       "      <td>0.130432</td>\n",
       "      <td>False</td>\n",
       "      <td>tow_truck</td>\n",
       "      <td>0.029175</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>324 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                            jpg_url  \\\n",
       "6     666051853826850816    https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "17    666104133288665088    https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg   \n",
       "18    666268910803644416    https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg   \n",
       "21    666293911632134144    https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg   \n",
       "25    666362758909284353    https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg   \n",
       "...                  ...                                                ...   \n",
       "2021  880935762899988482    https://pbs.twimg.com/media/DDm2Z5aXUAEDS2u.jpg   \n",
       "2022  881268444196462592    https://pbs.twimg.com/media/DDrk-f9WAAI-WQv.jpg   \n",
       "2046  886680336477933568    https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg   \n",
       "2052  887517139158093824  https://pbs.twimg.com/ext_tw_video_thumb/88751...   \n",
       "2074  892420643555336193    https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                p1   p1_conf  p1_dog               p2   p2_conf  \\\n",
       "6           1        box_turtle  0.933012   False       mud_turtle  0.045885   \n",
       "17          1               hen  0.965932   False             cock  0.033919   \n",
       "18          1  desktop_computer  0.086502   False             desk  0.085547   \n",
       "21          1  three-toed_sloth  0.914671   False            otter  0.015250   \n",
       "25          1        guinea_pig  0.996496   False            skunk  0.002402   \n",
       "...       ...               ...       ...     ...              ...       ...   \n",
       "2021        1       street_sign  0.251801   False         umbrella  0.115123   \n",
       "2022        1            tusker  0.473303   False  Indian_elephant  0.245646   \n",
       "2046        1       convertible  0.738995   False       sports_car  0.139952   \n",
       "2052        1         limousine  0.130432   False        tow_truck  0.029175   \n",
       "2074        1            orange  0.097049   False            bagel  0.085851   \n",
       "\n",
       "      p2_dog              p3   p3_conf  p3_dog  \n",
       "6      False        terrapin  0.017885   False  \n",
       "17     False       partridge  0.000052   False  \n",
       "18     False        bookcase  0.079480   False  \n",
       "21     False  great_grey_owl  0.013207   False  \n",
       "25     False         hamster  0.000461   False  \n",
       "...      ...             ...       ...     ...  \n",
       "2021   False   traffic_light  0.069534   False  \n",
       "2022   False            ibex  0.055661   False  \n",
       "2046   False       car_wheel  0.044173   False  \n",
       "2052   False   shopping_cart  0.026321   False  \n",
       "2074   False          banana  0.076110   False  \n",
       "\n",
       "[324 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing image that is not specified as a dog breed\n",
    "img_data[(img_data.p1_dog == False)&(img_data.p2_dog == False)&(img_data.p3_dog == False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever      150\n",
       "Labrador_retriever    100\n",
       "Pembroke               89\n",
       "Chihuahua              83\n",
       "pug                    57\n",
       "                     ... \n",
       "pillow                  1\n",
       "carousel                1\n",
       "bald_eagle              1\n",
       "lorikeet                1\n",
       "orange                  1\n",
       "Name: p1, Length: 378, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing p1 values in image  data\n",
    "img_data.p1.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing Twitter data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>7027</td>\n",
       "      <td>33876</td>\n",
       "      <td>Tue Aug 01 16:23:56 +0000 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>5306</td>\n",
       "      <td>29375</td>\n",
       "      <td>Tue Aug 01 00:17:27 +0000 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>3489</td>\n",
       "      <td>22094</td>\n",
       "      <td>Mon Jul 31 00:18:03 +0000 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>7239</td>\n",
       "      <td>36993</td>\n",
       "      <td>Sun Jul 30 15:58:51 +0000 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>7777</td>\n",
       "      <td>35368</td>\n",
       "      <td>Sat Jul 29 16:00:24 +0000 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>37</td>\n",
       "      <td>89</td>\n",
       "      <td>Mon Nov 16 00:24:50 +0000 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>115</td>\n",
       "      <td>247</td>\n",
       "      <td>Mon Nov 16 00:04:52 +0000 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>36</td>\n",
       "      <td>100</td>\n",
       "      <td>Sun Nov 15 23:21:54 +0000 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>39</td>\n",
       "      <td>112</td>\n",
       "      <td>Sun Nov 15 23:05:30 +0000 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>424</td>\n",
       "      <td>2295</td>\n",
       "      <td>Sun Nov 15 22:32:08 +0000 2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2327 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  retweet_count  favorite_count  \\\n",
       "0     892420643555336193           7027           33876   \n",
       "1     892177421306343426           5306           29375   \n",
       "2     891815181378084864           3489           22094   \n",
       "3     891689557279858688           7239           36993   \n",
       "4     891327558926688256           7777           35368   \n",
       "...                  ...            ...             ...   \n",
       "2322  666049248165822465             37              89   \n",
       "2323  666044226329800704            115             247   \n",
       "2324  666033412701032449             36             100   \n",
       "2325  666029285002620928             39             112   \n",
       "2326  666020888022790149            424            2295   \n",
       "\n",
       "                           timestamp  \n",
       "0     Tue Aug 01 16:23:56 +0000 2017  \n",
       "1     Tue Aug 01 00:17:27 +0000 2017  \n",
       "2     Mon Jul 31 00:18:03 +0000 2017  \n",
       "3     Sun Jul 30 15:58:51 +0000 2017  \n",
       "4     Sat Jul 29 16:00:24 +0000 2017  \n",
       "...                              ...  \n",
       "2322  Mon Nov 16 00:24:50 +0000 2015  \n",
       "2323  Mon Nov 16 00:04:52 +0000 2015  \n",
       "2324  Sun Nov 15 23:21:54 +0000 2015  \n",
       "2325  Sun Nov 15 23:05:30 +0000 2015  \n",
       "2326  Sun Nov 15 22:32:08 +0000 2015  \n",
       "\n",
       "[2327 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing twitter data\n",
    "twtr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2327 entries, 0 to 2326\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   tweet_id        2327 non-null   int64 \n",
      " 1   retweet_count   2327 non-null   int64 \n",
      " 2   favorite_count  2327 non-null   int64 \n",
      " 3   timestamp       2327 non-null   object\n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 72.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# twitter data information\n",
    "twtr_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17     tweet_id\n",
       "29     tweet_id\n",
       "32    timestamp\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking common columns across the three dataset\n",
    "all_columns = pd.Series(list(archv_data) + list(img_data) + list(twtr_data))\n",
    "all_columns[all_columns.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "`archive_data`\n",
    "* `archv_data` table has retweet rows\n",
    "* unusual name in name column such as \"a\", \"the\" and \"an\"\n",
    "* empty rows of _name_, and _dog_stage_ are filled with _None_ instead of _NaN_\n",
    "* rating_numerator extracted wrongly and it should be float data type.\n",
    "* some rating_denominator are above 10.\n",
    "* erroneous data types (tweet_id and timestamp)\n",
    "* empty dog names in `archv_data` \n",
    "\n",
    "`img_data`\n",
    "* some predictions are not dog breed\n",
    "* tweet id is integer data type\n",
    "* The prediction names contain lowercase, uppercase and underscore. \n",
    "\n",
    "`twtr_data`\n",
    "* erroneous data types (tweet_id and timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 7,
        "hidden": false,
        "row": 40,
        "width": 12
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "### Tidiness issues\n",
    "* one variable in four columns in `archv_data` table (doggo,floofer,pupper and puppo) \n",
    "* retweet count and favorite count should be part of `archv_data` table\n",
    "* image prediction should be part of `archv_data`"
   ]
  },
  {
   "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",
    "\n",
    "#### Make copy of original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making copies of original pieces of data\n",
    "archv_clean = archv_data.copy()\n",
    "img_clean = img_data.copy()\n",
    "twtr_clean = twtr_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### issue #1: One variable in four columns in `archv_data` table (doggo, floofer,  pupper and puppo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:  Doggo,Floofer, Pupper and Puppo are dog stages. It should be in one column. Combine all four stages to one column. The four columns would be dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating dog_stage column\n",
    "archv_clean['dog_stage'] = archv_clean[['doggo','floofer','pupper','puppo']].apply(lambda x: ','.join(x.astype(str)), axis=1)\n",
    "archv_clean['dog_stage'] = archv_clean['dog_stage'].str.replace(r'(None,)',repl='', regex=True)\n",
    "archv_clean['dog_stage'] = archv_clean['dog_stage'].str.replace(r'(,None)',repl='', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping doggo, pupper and puppo column\n",
    "archv_clean.drop(columns=['doggo','floofer','pupper','puppo'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None             1976\n",
       "pupper            245\n",
       "doggo              83\n",
       "puppo              29\n",
       "doggo,pupper       12\n",
       "floofer             9\n",
       "doggo,puppo         1\n",
       "doggo,floofer       1\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting values in dog_stage\n",
    "archv_clean.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  dog_stage                   2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(7)\n",
      "memory usage: 257.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking dog_stage column is created\n",
    "archv_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### issue #2: Retweet count and favorite count should be part of `archv_data` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:  Merge the _retweet_count_ and _favorite_count_ to the `archv_data` table, joining on _timestamp_ and _tweet_id_. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing timestamp to datetime\n",
    "archv_clean.timestamp = pd.to_datetime(archv_clean.timestamp)\n",
    "twtr_clean.timestamp = pd.to_datetime(twtr_clean.timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# merging twitter's table to archive table \n",
    "archv_clean = pd.merge(archv_clean,twtr_clean,on=['timestamp','tweet_id'], how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</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>7027.0</td>\n",
       "      <td>33876.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>5306.0</td>\n",
       "      <td>29375.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                  timestamp  \\\n",
       "0 2017-08-01 16:23:56+00:00   \n",
       "1 2017-08-01 00:17:27+00:00   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "\n",
       "   rating_denominator     name dog_stage  retweet_count  favorite_count  \n",
       "0                  10  Phineas      None         7027.0         33876.0  \n",
       "1                  10    Tilly      None         5306.0         29375.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking retweet count and favorite count has been added successfully\n",
    "archv_clean.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>37.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  retweet_count  favorite_count\n",
       "2351  666049248165822465           37.0            89.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting random timestamp from the new archv_clean data for further confirmation\n",
    "archv_clean[archv_clean.timestamp == '2015-11-16 00:24:50 +0000'][['tweet_id',\n",
    "                                                                   'retweet_count','favorite_count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2313</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>37</td>\n",
       "      <td>93</td>\n",
       "      <td>2015-11-16 02:38:37+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>37</td>\n",
       "      <td>89</td>\n",
       "      <td>2015-11-16 00:24:50+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  retweet_count  favorite_count  \\\n",
       "2313  666082916733198337             37              93   \n",
       "2322  666049248165822465             37              89   \n",
       "\n",
       "                     timestamp  \n",
       "2313 2015-11-16 02:38:37+00:00  \n",
       "2322 2015-11-16 00:24:50+00:00  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query twitter data based on above retweet count to confirm tweet_id is the same \n",
    "twtr_clean[twtr_clean.retweet_count==37.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### issue #3: Image prediction should be part of `archv_data`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Merge the `img_data` table to `archv_data` table, joining on _tweet_id_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing tweet_id datatype from 'int' to 'str'\n",
    "archv_clean.tweet_id = archv_clean.tweet_id.astype(str)\n",
    "img_clean.tweet_id = img_clean.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging img_clean data to archv_data\n",
    "AllDogData= pd.merge(archv_clean, img_clean, on='tweet_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</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>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                  timestamp  \\\n",
       "0 2017-08-01 16:23:56+00:00   \n",
       "1 2017-08-01 00:17:27+00:00   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  ...  img_num         p1  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...  ...      1.0     orange   \n",
       "1  https://twitter.com/dog_rates/status/892177421...  ...      1.0  Chihuahua   \n",
       "\n",
       "    p1_conf p1_dog        p2   p2_conf p2_dog        p3   p3_conf  p3_dog  \n",
       "0  0.097049  False     bagel  0.085851  False    banana  0.076110   False  \n",
       "1  0.323581   True  Pekinese  0.090647   True  papillon  0.068957    True  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the combined data\n",
    "AllDogData.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #4: `Archive Data` table has retweet rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:  Since we are considering original ratings (no retweets) that have images, we would filter for ratings with no retweets, i.e. in_reply_to_status_id & retweeted_status_id columns should be null. . After extraction the columns will be dropped because it is all empty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# slicing dataframe for rows with no retweet\n",
    "no_retweet = AllDogData[(AllDogData.in_reply_to_status_id.isnull()) & \n",
    "                         (AllDogData.retweeted_status_id.isnull())]\n",
    "\n",
    "# make a copy of no_retweet data\n",
    "drop_columns = no_retweet.copy()\n",
    "# dropping empty columns related to retweet\n",
    "drop_columns.drop(columns=['in_reply_to_status_id','in_reply_to_user_id','retweeted_status_id',\n",
    "                          'retweeted_status_user_id','retweeted_status_timestamp'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copying the new dataframe back to AllDogData\n",
    "AllDogData = drop_columns.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 27 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2097 non-null   object             \n",
      " 1   in_reply_to_status_id       0 non-null      float64            \n",
      " 2   in_reply_to_user_id         0 non-null      float64            \n",
      " 3   timestamp                   2097 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2097 non-null   object             \n",
      " 5   text                        2097 non-null   object             \n",
      " 6   retweeted_status_id         0 non-null      float64            \n",
      " 7   retweeted_status_user_id    0 non-null      float64            \n",
      " 8   retweeted_status_timestamp  0 non-null      object             \n",
      " 9   expanded_urls               2094 non-null   object             \n",
      " 10  rating_numerator            2097 non-null   int64              \n",
      " 11  rating_denominator          2097 non-null   int64              \n",
      " 12  name                        2097 non-null   object             \n",
      " 13  dog_stage                   2097 non-null   object             \n",
      " 14  retweet_count               2089 non-null   float64            \n",
      " 15  favorite_count              2089 non-null   float64            \n",
      " 16  jpg_url                     1971 non-null   object             \n",
      " 17  img_num                     1971 non-null   float64            \n",
      " 18  p1                          1971 non-null   object             \n",
      " 19  p1_conf                     1971 non-null   float64            \n",
      " 20  p1_dog                      1971 non-null   object             \n",
      " 21  p2                          1971 non-null   object             \n",
      " 22  p2_conf                     1971 non-null   float64            \n",
      " 23  p2_dog                      1971 non-null   object             \n",
      " 24  p3                          1971 non-null   object             \n",
      " 25  p3_conf                     1971 non-null   float64            \n",
      " 26  p3_dog                      1971 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(10), int64(2), object(14)\n",
      "memory usage: 458.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# previewing no_retweet data to cofirm that retweet columns were empty \n",
    "no_retweet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 22 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2097 non-null   object             \n",
      " 1   timestamp           2097 non-null   datetime64[ns, UTC]\n",
      " 2   source              2097 non-null   object             \n",
      " 3   text                2097 non-null   object             \n",
      " 4   expanded_urls       2094 non-null   object             \n",
      " 5   rating_numerator    2097 non-null   int64              \n",
      " 6   rating_denominator  2097 non-null   int64              \n",
      " 7   name                2097 non-null   object             \n",
      " 8   dog_stage           2097 non-null   object             \n",
      " 9   retweet_count       2089 non-null   float64            \n",
      " 10  favorite_count      2089 non-null   float64            \n",
      " 11  jpg_url             1971 non-null   object             \n",
      " 12  img_num             1971 non-null   float64            \n",
      " 13  p1                  1971 non-null   object             \n",
      " 14  p1_conf             1971 non-null   float64            \n",
      " 15  p1_dog              1971 non-null   object             \n",
      " 16  p2                  1971 non-null   object             \n",
      " 17  p2_conf             1971 non-null   float64            \n",
      " 18  p2_dog              1971 non-null   object             \n",
      " 19  p3                  1971 non-null   object             \n",
      " 20  p3_conf             1971 non-null   float64            \n",
      " 21  p3_dog              1971 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(6), int64(2), object(13)\n",
      "memory usage: 376.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking the new data\n",
    "AllDogData.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #5: Unusual name in name column such as \"a\", \"the\" and \"an\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: All dog names that are lowercase are not dog names. Delete rows with dog name in lowercase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# deleting rows with dog name in lower case\n",
    "AllDogData = AllDogData[~AllDogData.name.str.contains(r'(\\b[a-z]+\\b)')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, timestamp, source, text, expanded_urls, rating_numerator, rating_denominator, name, dog_stage, retweet_count, favorite_count, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 22 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for names with lowercase\n",
    "AllDogData[AllDogData.name.str.contains(r'(\\b[a-z]+\\b)')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #6: Empty rows of _name_, and _dog_stage_ are filled with _None_ instead of _NaN_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:  Replace all _\"None\"_ with _NaN_ in _dog_stage_, _name_ and _floofer_ column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# colummn for replacement\n",
    "col = ['name','dog_stage']\n",
    "# replacing 'None' with NaN\n",
    "AllDogData[col] = AllDogData[col].replace('None',np.NaN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Franklin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2344</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1993 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name dog_stage\n",
       "0      Phineas       NaN\n",
       "1        Tilly       NaN\n",
       "2       Archie       NaN\n",
       "3        Darla       NaN\n",
       "4     Franklin       NaN\n",
       "...        ...       ...\n",
       "2342       NaN       NaN\n",
       "2343       NaN       NaN\n",
       "2344       NaN       NaN\n",
       "2351       NaN       NaN\n",
       "2355       NaN       NaN\n",
       "\n",
       "[1993 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previewing column of interest\n",
    "AllDogData[col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #7: Rating_numerator extracted wrongly and it should be float data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Extract _rating_numerator_ accurately and make it float data type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllDogData['rating_numerator'] = AllDogData['text'].str.extract(r\"(\\d+\\.?\\d?\\d?)\\/\\d?\", expand = False).astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(AllDogData['rating_numerator'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>This is Bella. She hopes her smile made you sm...</td>\n",
       "      <td>13.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly...</td>\n",
       "      <td>9.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. ...</td>\n",
       "      <td>11.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>Here we have uncovered an entire battalion of ...</td>\n",
       "      <td>11.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  rating_numerator\n",
       "45    This is Bella. She hopes her smile made you sm...             13.50\n",
       "695   This is Logan, the Chow who lived. He solemnly...              9.75\n",
       "763   This is Sophie. She's a Jubilant Bush Pupper. ...             11.27\n",
       "1712  Here we have uncovered an entire battalion of ...             11.26"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllDogData[AllDogData['text'].str.contains(r\"(\\d+\\.\\d*\\/\\d+)\")][['text','rating_numerator',]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #8: Some rating_denominator are above 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: \"The fact that the rating numerators are greater than the denominators does not need to be cleaned. This [unique rating](http://knowyourmeme.com/memes/theyre-good-dogs-brent) system is a big part of the popularity of WeRateDogs\". However, on the website, 10 seems to be the default denominator. Drop rating denominator above 10 ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllDogData = AllDogData[AllDogData.rating_denominator == 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, timestamp, source, text, expanded_urls, rating_numerator, rating_denominator, name, dog_stage, retweet_count, favorite_count, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 22 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for rating denominator above 10\n",
    "AllDogData[AllDogData.rating_denominator != 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #9: Some predictions are not dog breed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Delete none dog breed from data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllDogData = AllDogData.query('~((p1_dog == False) & (p2_dog == False) & (p3_dog == False))')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, timestamp, source, text, expanded_urls, rating_numerator, rating_denominator, name, dog_stage, retweet_count, favorite_count, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 22 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for false dog breed across the three predictions\n",
    "AllDogData.query('((p1_dog == False) & (p2_dog == False) & (p3_dog == False))')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1692, 22)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previewing current datashape\n",
    "AllDogData.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #10: Prediction names contain lowercase, uppercase and underscore. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Make all names in  _prediction_name_ lowercase and replace underscore with space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making all prediction names lower case and replacing '_' with \" \"\n",
    "col = ['p1','p2','p3']\n",
    "AllDogData[col] = AllDogData[col].apply(lambda x: (x.str.lower()))\n",
    "AllDogData[col] = AllDogData[col].apply(lambda x: (x.str.replace('_',' ')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "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>p2</th>\n",
       "      <th>p3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chihuahua</td>\n",
       "      <td>pekinese</td>\n",
       "      <td>papillon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chihuahua</td>\n",
       "      <td>malamute</td>\n",
       "      <td>kelpie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>paper towel</td>\n",
       "      <td>labrador retriever</td>\n",
       "      <td>spatula</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>basset</td>\n",
       "      <td>english springer</td>\n",
       "      <td>german short-haired pointer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chesapeake bay retriever</td>\n",
       "      <td>irish terrier</td>\n",
       "      <td>indian elephant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342</th>\n",
       "      <td>pug</td>\n",
       "      <td>bull mastiff</td>\n",
       "      <td>french bulldog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>walker hound</td>\n",
       "      <td>english foxhound</td>\n",
       "      <td>ibizan hound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2344</th>\n",
       "      <td>gordon setter</td>\n",
       "      <td>yorkshire terrier</td>\n",
       "      <td>pekinese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>miniature pinscher</td>\n",
       "      <td>rottweiler</td>\n",
       "      <td>doberman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>welsh springer spaniel</td>\n",
       "      <td>collie</td>\n",
       "      <td>shetland sheepdog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1692 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            p1                  p2  \\\n",
       "1                    chihuahua            pekinese   \n",
       "2                    chihuahua            malamute   \n",
       "3                  paper towel  labrador retriever   \n",
       "4                       basset    english springer   \n",
       "5     chesapeake bay retriever       irish terrier   \n",
       "...                        ...                 ...   \n",
       "2342                       pug        bull mastiff   \n",
       "2343              walker hound    english foxhound   \n",
       "2344             gordon setter   yorkshire terrier   \n",
       "2351        miniature pinscher          rottweiler   \n",
       "2355    welsh springer spaniel              collie   \n",
       "\n",
       "                               p3  \n",
       "1                        papillon  \n",
       "2                          kelpie  \n",
       "3                         spatula  \n",
       "4     german short-haired pointer  \n",
       "5                 indian elephant  \n",
       "...                           ...  \n",
       "2342               french bulldog  \n",
       "2343                 ibizan hound  \n",
       "2344                     pekinese  \n",
       "2351                     doberman  \n",
       "2355            shetland sheepdog  \n",
       "\n",
       "[1692 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previewing prediction name for confirmation\n",
    "AllDogData[col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Undocumented Assessment \n",
    "\n",
    "### Issue #11: Some column data types changed when cleaning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Change retweet_count, favorite_count, is_dog_breed and img_num to their respective data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# columns list\n",
    "col = ['retweet_count','favorite_count','img_num']\n",
    "# replacing NaN with '0' for conversion to work\n",
    "AllDogData[col] = AllDogData[col].replace(np.nan,0)\n",
    "\n",
    "# changing datatype\n",
    "AllDogData.retweet_count = AllDogData.retweet_count.astype(int)\n",
    "AllDogData.favorite_count = AllDogData.favorite_count.astype(int)\n",
    "AllDogData.img_num = AllDogData.img_num.astype(int)\n",
    "\n",
    "# changing dog breeds data type to \"bool\"\n",
    "col = ['p1_dog','p2_dog','p3_dog']\n",
    "AllDogData[col] = AllDogData[col].astype(bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1692 entries, 1 to 2355\n",
      "Data columns (total 22 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1692 non-null   object             \n",
      " 1   timestamp           1692 non-null   datetime64[ns, UTC]\n",
      " 2   source              1692 non-null   object             \n",
      " 3   text                1692 non-null   object             \n",
      " 4   expanded_urls       1689 non-null   object             \n",
      " 5   rating_numerator    1692 non-null   float64            \n",
      " 6   rating_denominator  1692 non-null   int64              \n",
      " 7   name                1224 non-null   object             \n",
      " 8   dog_stage           278 non-null    object             \n",
      " 9   retweet_count       1692 non-null   int32              \n",
      " 10  favorite_count      1692 non-null   int32              \n",
      " 11  jpg_url             1572 non-null   object             \n",
      " 12  img_num             1692 non-null   int32              \n",
      " 13  p1                  1572 non-null   object             \n",
      " 14  p1_conf             1572 non-null   float64            \n",
      " 15  p1_dog              1692 non-null   bool               \n",
      " 16  p2                  1572 non-null   object             \n",
      " 17  p2_conf             1572 non-null   float64            \n",
      " 18  p2_dog              1692 non-null   bool               \n",
      " 19  p3                  1572 non-null   object             \n",
      " 20  p3_conf             1572 non-null   float64            \n",
      " 21  p3_dog              1692 non-null   bool               \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(4), int32(3), int64(1), object(10)\n",
      "memory usage: 249.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# previewing\n",
    "AllDogData.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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllDogData.to_csv('twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and Visualizing Data\n",
    "In this section, the wrangled and cleaned data will be analyzed and visualized. Producing at least **three (3) insights and one (1) visualization.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading data\n",
    "Dog_data = pd.read_csv('twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5306</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3489</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7239</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>paper towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>labrador retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                  timestamp  \\\n",
       "0  892177421306343426  2017-08-01 00:17:27+00:00   \n",
       "1  891815181378084864  2017-07-31 00:18:03+00:00   \n",
       "2  891689557279858688  2017-07-30 15:58:51+00:00   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  \\\n",
       "0  This is Tilly. She's just checking pup on you....   \n",
       "1  This is Archie. He is a rare Norwegian Pouncin...   \n",
       "2  This is Darla. She commenced a snooze mid meal...   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892177421...              13.0   \n",
       "1  https://twitter.com/dog_rates/status/891815181...              12.0   \n",
       "2  https://twitter.com/dog_rates/status/891689557...              13.0   \n",
       "\n",
       "   rating_denominator    name dog_stage  retweet_count  ...  img_num  \\\n",
       "0                  10   Tilly       NaN           5306  ...        1   \n",
       "1                  10  Archie       NaN           3489  ...        1   \n",
       "2                  10   Darla       NaN           7239  ...        1   \n",
       "\n",
       "            p1   p1_conf p1_dog                  p2   p2_conf p2_dog  \\\n",
       "0    chihuahua  0.323581   True            pekinese  0.090647   True   \n",
       "1    chihuahua  0.716012   True            malamute  0.078253   True   \n",
       "2  paper towel  0.170278  False  labrador retriever  0.168086   True   \n",
       "\n",
       "         p3   p3_conf p3_dog  \n",
       "0  papillon  0.068957   True  \n",
       "1    kelpie  0.031379   True  \n",
       "2   spatula  0.040836  False  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dog_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping columns no needed for analysis\n",
    "Redundant_col = ['tweet_id','source','text','expanded_urls','jpg_url','img_num'] \n",
    "Dog_data = Dog_data.drop(Redundant_col, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping empty dog name\n",
    "Dog_data.dropna(subset='name', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "<ul>\n",
    "    <li>What is associated with maximum retweet_count</li> \n",
    "    <li>What is associated with maximum favorite_count</li>\n",
    "    <li>What is associated with the maximum rating</li>\n",
    "    <li>What is the highest confidence score of algorithm 1</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dog Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FnU2bNkmSGjVqJA8PD40dO1Zjx44tdLm1a9dWQkKCrr32WrVq1arAfvrscaV1zTXX6MCBA8rNzS1yTIMGDZSQkODwpbH09/42f7qzzndcs9lsTi8XrkFR5yJ3+vRph8cLFixQSEiI3nnnHe3cuVMnT56Uh4eHoqKi5OFRuhO7itppledv7IuzTlPEHUQ8PT1LtMwLrSv/+Xvuuee0c+fOQseeu9M+d/tIZbeNisr7XIXFAMC6Kvt+/0Lyc5o7d65mz55d6Jhzr4lQ1nk4exxxZplnzztw4EDNnj1bX3/9td555x0dP35cubm5Cg0N1TXXXFOMyEuOYwiAs/cDHh4eysvLU69evQrdb537XrgwZ86cUUxMjDp37qxrrrlGderU0caNG5WUlKRq1aopKChInTp10r59++yFmfx9/zvvvKOoqKhCl5tfaPLw8NCqVas0YcKEQsf98ssvF4yxIhX1nv58x3B3PD7DeRR1YFejRg11795dr776qt544w17f6NGjQqMLW5BoCTyf9LUqFEjhwtC1qxZUzVr1izz9eVLTU1VjRo1CvSXpvpdmPxvj9PT07V27VqnllEW2+jw4cPy9PTUtddeq/3799v7a9euLX9//wIXuQRQeVWm/X7+vu2aa65xeMPdpEkTh3HJyclKT0+Xp6en0/viwtadv65169Y5TGvSpInb7FfvvfdeHTx4sMAF8V977TWHx+c7fkgFn1NJatq0qZKTkx3O0gGAcx08eFAeHh6Ki4vTr7/+et6x5zvubNy4US+88IK6d++u5ORk+3vaPXv2qFOnTurUqZOWLl1qH59/NmJOTs4F9/0HDx7UZZdddsFxpTkuHjx4UEFBQapatarOnDlT6JjDhw+re/fuuuyyyxyKXU2bNrVPl/53ls25n2dK+1mmPI77KB9cUwd2+RXbcyu0I0eOLDA2/yryhRVDnLV27Vrl5OToiSeecOgfPnx4ma2jMAcPHlSNGjXUsmVLe19gYKDuueeeMl3P9u3b9dtvv+m5556Tj49PgelXXHHFBZdRFtso/44A586TfwX9wu4UA6Byqkz7/RUrVkiSnnrqKYf+c3PJy8vTwoUL1bdvXzVv3rzAcoqzLz7Xtm3blJSUpKFDhzr8pOm2225Ts2bN3Ga/Wtj27tChg4KDgx3G5Rdmzt3WiYmJ+umnnzRo0CCHn+Y1b95cPXv2tB9fAKAoixYt0pkzZxQWFlbo9LML+hkZGUX+DHjjxo265JJLNHLkSPtPrPL7/+///k/16tWzX09H+rugv27dOj3++OMKDAwssLyz9/35Z7D27NmzwDg/Pz/72fxF7SuLY+HChapVq9Z5j3fLly9X1apVC4wZNWqU8vLy7Mc9m82m5OTkAtf6GTZsWInjOltGRkaZHvNRfjhTB3Y2m03r16/X6NGjVa1aNcXHx6tnz55q2LBhgbHbt2+XJL355pv64osvlJOTo2+//bZU39AdP35c7733np577jl98803WrlypVq1aqVevXopOTm53KrFX3zxhcaPH6+vv/5a77//vi699FI98cQT+uWXX9S2bdsyW48xRo888ohWrFihPXv2aObMmYqPj1e9evXUtWtXpaen68477zzvMspiG+3evVuzZs3S448/rho1amj9+vXq0KGDBg8erK+//lrff/99meUMwL1Vpv3+rl279Nlnn+nJJ5+Un5+ftmzZom7duhV61tGLL76orl27KiYmRtOmTdPevXtVs2ZNtWnTRt27d9fll19eojzOnDmjF154QbNmzdL69ev1+eef229pHhcXp0mTJpVoeeVl6dKl6tu3r77++mstW7ZMDRs21NChQ7V3715ddtll9nGZmZnas2eP+vfvr19++UUpKSn6+eeftWfPHj3//PNasWKFoqOjNWPGDPstzdPS0hQeHu665ABYwu+//66XX35Zb7/9tq666iotXrxYNptNDRs21D333KOpU6fab96xfft23X///Xr33Xf1448/6uTJk/azb6Kjo5WTk6OmTZs6XJR/w4YN9mLG2UUdSXryySe1adMmxcbGatq0afr9998VEBCg4OBgXXnllWrdurWkv3+ideedd2rp0qWaNWuWtm/fLh8fH7Vs2VL33nuvrrrqKv3555/n3VdeyJw5c/TQQw9p0qRJ6tChgzZu3CgfHx91795dH3zwgZYsWaJvv/1W3333nd58801dddVV2rVrl3r27Km7775bkyZNcrgW2vTp0xUaGqpp06Zp27Zt6ty5sxo3blyqbbV9+3Z1795do0aNUkJCguLi4rR169ZSLRPlx+W34KJVfMu/nd3ll1/u0F+3bl2zcOFCk5KSYlJTU838+fNNYGBgobfrGzNmjDl69Kg5c+aMw21ui7q1bdu2bR3mL+y2rB4eHua1114zCQkJJiMjw6xZs8Y0adLEJCcnmw8++KBEORpjTERERIH+c+OTZLp37252795tMjMzzb59+8yAAQOKvKX5ucvMv43gs88+W2h+ffv2dehv1aqV+eqrr0xycrI5ffq0iYuLM1988YXp2rXrBbdPWW0jT09P88orr5iDBw+arKwsc/jwYfPmm28WuMVjXFyc+fbbb13+eqXRaKVvF8N+39vb20yePNkkJycbm81mvvnmG1OvXr1Cc6lVq5aJiIgwhw8fNllZWSYhIcGsXr3aPPLIIwXiPXc/XtTtY/v162e2b99uTp8+bU6cOGHmzp1r6tat6zBm5syZxmazFYh93bp1JjY2tkD/ufvhom5pXti8M2fOLHA74BdffNHExcWZ06dPm+3bt5vbb7+90HE33nij+fHHH01mZmaB5++WW24xGzduNBkZGeavv/4y33zzjWnatGmxXm80Gu3iaefbD9xzzz1mw4YNxmazGZvNZvbu3WsiIiLMtddeax9z6aWXmk8//dSkpKQYY0yB/VRMTIwxxpj27dvb++rWrWuMMebw4cOFxtSwYUMza9Ysk5CQYLKysszRo0fNkiVLzL/+9S+HcT4+PubNN980v/zyi8nMzDTHjx83mzZtMs8884ypWrWqfdz59pUXapdccol544037O/HExISzIIFC0zDhg0d4nj33XfNH3/8YbKyssyBAwcKfObIX9a0adNMamqqSUtLM1988YW54ooriryl+bnbJP+4nX9cl2QaN25svv/+e5ORkWGMMdze3I1blf/+Abit/DtCjRkzRm+99ZarwwEAlDP2+wAAAMXDNXXgVi655JICffnXQ+BnQQBQ+bDfBwAAcB7X1IFb6d+/vwYPHqzly5fr5MmT6tixowYMGKCoqCht2bJFkhQQEHDeZZw+fVrp6ekVES4AoJTY7wMAKotq1apd8O6NaWlpyszMrKCIcLFw+W/AaLT8dsMNN5jVq1eb5ORkk5WVZY4cOWImTZpkfHx87GMuhN970mg0mnUa+30ajUajVZaWf92z8zn3emw0Wmkb19SB5XTr1u280xMSErRv374KigYAUN7Y7wMArKBGjRoXvHvunj17lJiYWEER4WJAUQcAAAAAAMCCuFAyAAAAAACABVn2Qsl169aVzWZzdRgAUCy+vr5KSEhwdRgXBY4PAKyE40PF4fgAwEqKe3ywZFGnbt26io+Pd3UYAFAi9erV4417OeP4AMCKOD6UP44PAKyoOMcHSxZ18ivs9erVK1G13dfXV/Hx8SWez5WsGLNE3BXJijFLF1fc+fNYKU+rcvb4UJGs+tovDnKznsqal2SN3Dg+VBxnjg9WeA2VFDlZAzlZQ3nmVJLjgyWLOvlsNptTT56z87mSFWOWiLsiWTFmibhRPqywfawQo7PIzXoqa15S5c4NJefM66EyvobIyRrIyRpcnRMXSgYAAAAAALAgijoAAAAAAAAWRFEHAAAAAADAgijqAAAAAAAAWBBFHQAAAAAAAAuiqAMAAAAAAGBBFHUAAAAAAAAsiKIOAAAAAACABVHUAQAAAAAAsCCKOgAAAAAAABZEUQcAAAAAAMCCKOoAAAAAAABYEEUdAAAAAAAAC6rq6gAAVFLr1p1/uqfn3/8uWybl5v6vv2vX8osJAErr3H3WhbBPA6yvpP/vJf7vA6gwnKkDALigTp06acmSJYqPj5cxRnfddZd9WtWqVfX2229r9+7dOnnypOLj4zV79mzVqVPHYRn+/v769NNPlZaWptTUVE2fPl0+Pj4OY1q2bKkNGzbo9OnTOnLkiJ5//vkKyQ8AAACwIoo6AIAL8vHx0a5du/Tkk08WmHbppZeqTZs2euONN9SmTRv961//UpMmTbRkyRKHcfPmzVPz5s3Vo0cP9enTR507d9bUqVPt0319fbVq1SodPnxYbdu21fPPP6/w8HA9+uij5Z4fAAAAYEX8/AoAcEErV67UypUrC52Wnp6unj17OvQNHz5cP/74o+rXr6+jR4+qadOm6tWrl9q1a6ft27dLkkaMGKHly5frueee07FjxzRw4EB5eXnp3//+t3JycrR37161bt1azzzzjKZNm1buOQIAAABWw5k6AIAy5+fnp7y8PP3111+SpODgYKWmptoLOpK0Zs0a5eXlKSgoyD5mw4YNysnJsY+JiopS06ZNVaNGjYoMHwAAALAEztQBAJQpb29vjR8/Xp9//rlsNpskKTAwUMePH3cYl5ubq5SUFAUGBtrHxMXFOYxJSkqyT8svEJ3Ny8tL3t7e9se+vr4O/7ojK8TorIsit/yLvBd/xnKIpuxcFNvMjXNz59gAANZAUQcAUGaqVq2qBQsWqEqVKnriiSfKfX2hoaEKDw8v0B8fH1/u6y4tK8TorEqdW0hIyWZITy+fQMpYpd5mlTg3AAAo6gAAykR+QadBgwa65ZZb7GfpSFJiYqJq167tMN7T01M1a9ZUYmKifUxAQIDDmPzH+WPONW7cOE2cONH+2NfXV/Hx8apXr57D+t2JFWJ01kWR25YtspXk1sa9e5dfUGXgothmbpxbfowAADiLog4AoNTyCzrXXnutunbtqpSUFIfp0dHR8vf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",
      "text/plain": [
       "<Figure size 1400x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure\n",
    "Dog_data.hist(bins=20,color='c',figsize=(14,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is associated with maximum retweet_count\n",
    "\n",
    "Function to extract maximum value from a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining function\n",
    "def extract_max(col):\n",
    "    max_id = Dog_data[col].idxmax() # extracting id of max value\n",
    "    max_df = pd.DataFrame(Dog_data.loc[max_id]) # locating max id and extracting row as dataframe \n",
    "    return max_df # return dataframe when function is called"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "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>375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <td>2016-12-09 06:17:20+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_numerator</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_denominator</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>Stephan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_stage</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet_count</th>\n",
       "      <td>51790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>111909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>chihuahua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_conf</th>\n",
       "      <td>0.50537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2</th>\n",
       "      <td>pomeranian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2_conf</th>\n",
       "      <td>0.120358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3</th>\n",
       "      <td>toy terrier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3_conf</th>\n",
       "      <td>0.077008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          375\n",
       "timestamp           2016-12-09 06:17:20+00:00\n",
       "rating_numerator                         13.0\n",
       "rating_denominator                         10\n",
       "name                                  Stephan\n",
       "dog_stage                                 NaN\n",
       "retweet_count                           51790\n",
       "favorite_count                         111909\n",
       "p1                                  chihuahua\n",
       "p1_conf                               0.50537\n",
       "p1_dog                                   True\n",
       "p2                                 pomeranian\n",
       "p2_conf                              0.120358\n",
       "p2_dog                                   True\n",
       "p3                                toy terrier\n",
       "p3_conf                              0.077008\n",
       "p3_dog                                   True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_max('retweet_count') # extracting highest retweet count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _**highest**_ retweet count is _**51790**_ and the dog's name is _**Stephan**_, having a favorite count of _**111,909**_. Let's check for the highest favorite count.\n",
    "\n",
    "#### What is associated with maximum favorite_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>375</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <td>2016-12-09 06:17:20+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_numerator</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_denominator</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>Stephan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_stage</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>retweet_count</th>\n",
       "      <td>51790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>111909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>chihuahua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_conf</th>\n",
       "      <td>0.50537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2</th>\n",
       "      <td>pomeranian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2_conf</th>\n",
       "      <td>0.120358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3</th>\n",
       "      <td>toy terrier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3_conf</th>\n",
       "      <td>0.077008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          375\n",
       "timestamp           2016-12-09 06:17:20+00:00\n",
       "rating_numerator                         13.0\n",
       "rating_denominator                         10\n",
       "name                                  Stephan\n",
       "dog_stage                                 NaN\n",
       "retweet_count                           51790\n",
       "favorite_count                         111909\n",
       "p1                                  chihuahua\n",
       "p1_conf                               0.50537\n",
       "p1_dog                                   True\n",
       "p2                                 pomeranian\n",
       "p2_conf                              0.120358\n",
       "p2_dog                                   True\n",
       "p3                                toy terrier\n",
       "p3_conf                              0.077008\n",
       "p3_dog                                   True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_max('favorite_count') # extracting highest favorite count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest favorite count is directly related to the highest retweet count. \n",
    "\n",
    "#### What is associated with the maximum rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <td>2017-07-26 15:59:51+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_numerator</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_denominator</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>Cassie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_stage</th>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet_count</th>\n",
       "      <td>6110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>27990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>pembroke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_conf</th>\n",
       "      <td>0.511319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2</th>\n",
       "      <td>cardigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2_conf</th>\n",
       "      <td>0.451038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3</th>\n",
       "      <td>chihuahua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3_conf</th>\n",
       "      <td>0.029248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            8\n",
       "timestamp           2017-07-26 15:59:51+00:00\n",
       "rating_numerator                         14.0\n",
       "rating_denominator                         10\n",
       "name                                   Cassie\n",
       "dog_stage                               doggo\n",
       "retweet_count                            6110\n",
       "favorite_count                          27990\n",
       "p1                                   pembroke\n",
       "p1_conf                              0.511319\n",
       "p1_dog                                   True\n",
       "p2                                   cardigan\n",
       "p2_conf                              0.451038\n",
       "p2_dog                                   True\n",
       "p3                                  chihuahua\n",
       "p3_conf                              0.029248\n",
       "p3_dog                                   True"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_max('rating_numerator') # extracting highest rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum rating is _**14**_. The name of the dog with this rating is _**Cassie**_  and it's in its doggo stage.\n",
    "\n",
    "#### What is the highest confidence score of algorithm 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "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>717</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <td>2016-06-22 01:50:58+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_numerator</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_denominator</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>Lenox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_stage</th>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet_count</th>\n",
       "      <td>2318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>6559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>barrow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_conf</th>\n",
       "      <td>0.999962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_dog</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2</th>\n",
       "      <td>basset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2_conf</th>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2_dog</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3</th>\n",
       "      <td>wok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3_conf</th>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3_dog</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          717\n",
       "timestamp           2016-06-22 01:50:58+00:00\n",
       "rating_numerator                         10.0\n",
       "rating_denominator                         10\n",
       "name                                    Lenox\n",
       "dog_stage                               doggo\n",
       "retweet_count                            2318\n",
       "favorite_count                           6559\n",
       "p1                                     barrow\n",
       "p1_conf                              0.999962\n",
       "p1_dog                                  False\n",
       "p2                                     basset\n",
       "p2_conf                              0.000014\n",
       "p2_dog                                   True\n",
       "p3                                        wok\n",
       "p3_conf                              0.000006\n",
       "p3_dog                                  False"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_max('p1_conf') # extracting highest confidence score of algorithm 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Lenox**_ a _**doggo**_ happens to be the dog associated with the highest confidence score _**(0.999962)**_. Lenox, though having the highest confidence score was predicted as **False** (not a dog) in algorithm 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Different Dog Stage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# series of dog stage\n",
    "DogStagesCount = Dog_data.dog_stage.value_counts()\n",
    "\n",
    "# setting plot with size\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# plotting bar chart\n",
    "plt.bar(DogStagesCount.index,DogStagesCount.values)\n",
    "\n",
    "# setting labels and size\n",
    "plt.title('Different Dog Stage',fontsize =15)\n",
    "plt.xlabel('Dog Stage Name',fontsize=15)\n",
    "plt.ylabel('Dog Count', fontsize =15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# view plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the chart that the majority of the dogs posted on _WeRateDog_ twitter page are in their _**pupper**_ stage. If they are the most posted stage of a dog, that means they have the highest retweet count, right? Let's find out!\n",
    "\n",
    "#### Most Retweeted Dog Stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# series of retweet dog stage\n",
    "RetwtDogStage = Dog_data.groupby('dog_stage')['retweet_count'].sum()\n",
    "RetwtDogStage = RetwtDogStage.sort_values(ascending=False)\n",
    "\n",
    "# setting plot with size\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# plotting bar chart\n",
    "plt.bar(RetwtDogStage.index, RetwtDogStage.values)\n",
    "\n",
    "# setting labels and size\n",
    "plt.title('Most Retweeted Dog Stage',fontsize =15)\n",
    "plt.xlabel('Dog Stage Name',fontsize=15)\n",
    "plt.ylabel('Retweet Sum [in Million]', fontsize =15)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.yticks(fontsize=13)\n",
    "\n",
    "# view plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! Our inference was correct. _**Pupper**_ stage has the most retweets. Let's look at the most predicted dog.\n",
    "\n",
    "#### 15 Most Predicted Dog Breed in Algorithm 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dog breed series\n",
    "MostPredictedBreed_1 = Dog_data.p1.value_counts()\n",
    "# 15 top dog breed\n",
    "MostPredictedBreed_1 = MostPredictedBreed_1.head(15).sort_values()\n",
    "\n",
    "# setting plot with size\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# plotting horizontal bar chart\n",
    "plt.barh(MostPredictedBreed_1.index,MostPredictedBreed_1.values)\n",
    "\n",
    "# setting labels and size\n",
    "plt.title('15 Most Predicted Dog Breed in Algorithm 1',fontsize =15)\n",
    "plt.xlabel('Breed Count',fontsize=15)\n",
    "plt.ylabel('Breed Name', fontsize =15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "\n",
    "# view plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Golden retriever** is the most predicted breed in algorithm 1.\n",
    "\n",
    "#### 15 Most Predicted Dog Breed in Algorithm 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dog breed series\n",
    "MostPredictedBreed_2 = Dog_data.p2.value_counts()\n",
    "# 15 top dog breed\n",
    "MostPredictedBreed_2 = MostPredictedBreed_2.head(15).sort_values()\n",
    "\n",
    "# setting plot with size\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# plotting horizontal bar chart\n",
    "plt.barh(MostPredictedBreed_2.index,MostPredictedBreed_2.values)\n",
    "\n",
    "# setting labels and size\n",
    "plt.title('15 Most Predicted Dog Breed in Algorithm 2',fontsize =15)\n",
    "plt.xlabel('Breed Count',fontsize=15)\n",
    "plt.ylabel('Breed Name', fontsize =15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "\n",
    "# view plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In algorithm 2, **Labrador retriever** is the most predicted dog breed followed by **Golden retriever**\n",
    "\n",
    "#### 15 Most Predicted Dog Breed in Algorithm 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dog breed series\n",
    "MostPredictedBreed_3 = Dog_data.p3.value_counts()\n",
    "# 15 top dog breed\n",
    "MostPredictedBreed_3 = MostPredictedBreed_3.head(15).sort_values()\n",
    "\n",
    "# setting plot with size\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# plotting horizontal bar chart\n",
    "plt.barh(MostPredictedBreed_3.index,MostPredictedBreed_3.values)\n",
    "\n",
    "# setting labels and size\n",
    "plt.title('15 Most Predicted Dog Breed in Algorithm 3',fontsize =15)\n",
    "plt.xlabel('Breed Count',fontsize=15)\n",
    "plt.ylabel('Breed Name', fontsize =15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "\n",
    "# view plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**Labrador retriever**_ is the most predicted dog breed in algorithm 3 followed by _**Chihuahua**_.\n",
    "\n",
    "#### Relationship Between Favorite count and Retweet count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and increase the figure size\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# Create a scatter plot of duration versus year\n",
    "x = Dog_data.retweet_count\n",
    "y = Dog_data.favorite_count\n",
    "plt.scatter(x,y,c='c')\n",
    "\n",
    "# Create a title\n",
    "plt.title('Favorite count vs. Retweet Count',fontsize =15)\n",
    "plt.xlabel('Retweet count',fontsize=15)\n",
    "plt.ylabel('Favorite count', fontsize =15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a positive relationship between favorite count and retweet count.\n",
    "\n",
    "#### Relationship Between Rating and Retweet count  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and increase the figure size\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# Create a scatter plot of duration versus year\n",
    "x = Dog_data.retweet_count\n",
    "y = Dog_data.rating_numerator\n",
    "plt.scatter(x,y,c='c')\n",
    "\n",
    "# Create a title\n",
    "plt.title('Rating vs. Retweet count',fontsize =15)\n",
    "plt.xlabel('Retweet count',fontsize=15)\n",
    "plt.ylabel('Rating', fontsize =15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At least with the above plot we can say the number of retweets is not dependent on the rating. The highest rating has less than 10,000 retweet count compared to ratings below 12 which have over 10,000 retweets.\n",
    "\n",
    "#### Relationship Between Rating and Favorite count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and increase the figure size\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# Create a scatter plot of duration versus year\n",
    "x = Dog_data.favorite_count\n",
    "y = Dog_data.rating_numerator\n",
    "plt.scatter(x,y,c='c')\n",
    "\n",
    "# Create a title\n",
    "plt.title('Rating vs. Favorite count',fontsize =15)\n",
    "plt.xlabel('Favorite count',fontsize=15)\n",
    "plt.ylabel('Rating', fontsize =15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above relationship is similar to the previous one. Favorite count of dog tweets is not dependent on the rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "The three data was gathered successfully, though some twitter data could not be accessed, probably because it was deleted. Three data was wrangled and merged to form a master dataset which was later used for analysis and visualization. Contained in the folder where you find this file are two reports, **wrangle_report & act_report**, that  briefly describes the wrangling efforts and  communicates all the insights and displays the visualization(s) produced from this wrangled data."
   ]
  }
 ],
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     "report_default": {
      "name": "report",
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  },
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit",
   "language": "python",
   "name": "python3"
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