wrangle_act.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 0,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "# Project: Wrangling and Analyze Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project uses scraped json data of Twitter user ratings of dogs, and analyzes them."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Gathering\n",
    "In the cell below, gather **all** three pieces of data for this project and load them in the notebook. **Note:** the methods required to gather each data are different.\n",
    "1. Directly download the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"twitter-archive-enhanced.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response = requests.get(url)\n",
    "\n",
    "# Save tsv to file\n",
    "with open('image-predictions.tsv', mode='wb') as file:\n",
    "    file.write(response.content)\n",
    "    \n",
    "df_predictions = pd.read_csv('image-predictions.tsv', sep='\\t')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Use the Tweepy library or provided json library to query additional data via the Twitter API (tweet_json.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using provided json file\n",
    "import json\n",
    "\n",
    "# List creation\n",
    "list_API = []\n",
    "\n",
    "# Read the .txt file line by line into a list of dictionaries\n",
    "for line in open('tweet-json.txt', 'r'):\n",
    "    line_data = json.loads(line)\n",
    "    list_API.append({'tweet_id': line_data['id_str'],\n",
    "                                    'retweet_count': line_data['retweet_count'],\n",
    "                                    'favorite_count': line_data['favorite_count']})\n",
    "    \n",
    "df_API = pd.DataFrame.from_dict(list_API)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 28,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Assessing Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess dataframe, 'df'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas   NaN     NaN    NaN   NaN  \n",
       "1                  10     Tilly   NaN     NaN    NaN   NaN  \n",
       "2                  10    Archie   NaN     NaN    NaN   NaN  \n",
       "3                  10     Darla   NaN     NaN    NaN   NaN  \n",
       "4                  10  Franklin   NaN     NaN    NaN   NaN  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual assessment of 'df' head\n",
    "# Observations - dog stage is split across four columns, score split across two columns, many NaN values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "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",
       "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",
       "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",
       "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",
       "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",
       "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",
       "2351                  10  NaN   NaN     NaN    NaN   NaN  \n",
       "2352                  10    a   NaN     NaN    NaN   NaN  \n",
       "2353                  10    a   NaN     NaN    NaN   NaN  \n",
       "2354                  10    a   NaN     NaN    NaN   NaN  \n",
       "2355                  10  NaN   NaN     NaN    NaN   NaN  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual assessment of 'df' head\n",
    "# Observations - nothing specific\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        1611 non-null   object \n",
      " 13  doggo                       97 non-null     object \n",
      " 14  floofer                     10 non-null     object \n",
      " 15  pupper                      257 non-null    object \n",
      " 16  puppo                       30 non-null     object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Programmatic assessment of 'df' - using info function to asses non-null count and data types\n",
    "# Observations - 181 retweets present, 2356 rows in total, some missing information in expanded_urls etc.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.427716e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>7.720400e+17</td>\n",
       "      <td>1.241698e+16</td>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.856705e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>6.236928e+16</td>\n",
       "      <td>9.599254e+16</td>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783989e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>7.186315e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.196279e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>7.804657e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993373e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>8.203146e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>7.874618e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "count  2.356000e+03           7.800000e+01         7.800000e+01   \n",
       "mean   7.427716e+17           7.455079e+17         2.014171e+16   \n",
       "std    6.856705e+16           7.582492e+16         1.252797e+17   \n",
       "min    6.660209e+17           6.658147e+17         1.185634e+07   \n",
       "25%    6.783989e+17           6.757419e+17         3.086374e+08   \n",
       "50%    7.196279e+17           7.038708e+17         4.196984e+09   \n",
       "75%    7.993373e+17           8.257804e+17         4.196984e+09   \n",
       "max    8.924206e+17           8.862664e+17         8.405479e+17   \n",
       "\n",
       "       retweeted_status_id  retweeted_status_user_id  rating_numerator  \\\n",
       "count         1.810000e+02              1.810000e+02       2356.000000   \n",
       "mean          7.720400e+17              1.241698e+16         13.126486   \n",
       "std           6.236928e+16              9.599254e+16         45.876648   \n",
       "min           6.661041e+17              7.832140e+05          0.000000   \n",
       "25%           7.186315e+17              4.196984e+09         10.000000   \n",
       "50%           7.804657e+17              4.196984e+09         11.000000   \n",
       "75%           8.203146e+17              4.196984e+09         12.000000   \n",
       "max           8.874740e+17              7.874618e+17       1776.000000   \n",
       "\n",
       "       rating_denominator  \n",
       "count         2356.000000  \n",
       "mean            10.455433  \n",
       "std              6.745237  \n",
       "min              0.000000  \n",
       "25%             10.000000  \n",
       "50%             10.000000  \n",
       "75%             10.000000  \n",
       "max            170.000000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Programmatic assessment of 'df' - using descibe function to assess max min ranges and count\n",
    "# Observations - rating_denominator is not constant\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Programmatic assessment of 'df' - looking for duplicates\n",
    "# Observations - no duplicate rows\n",
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2278\n",
       "in_reply_to_user_id           2278\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           2175\n",
       "retweeted_status_user_id      2175\n",
       "retweeted_status_timestamp    2175\n",
       "expanded_urls                   59\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                           745\n",
       "doggo                         2259\n",
       "floofer                       2346\n",
       "pupper                        2099\n",
       "puppo                         2326\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for null values\n",
    "# Observation - reply columns, retween columns, and expanded urls contains null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking for tweet_id duplicates\n",
    "# Observations - all unique\n",
    "df.tweet_id.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "2351    False\n",
       "2352     True\n",
       "2353     True\n",
       "2354     True\n",
       "2355    False\n",
       "Name: name, Length: 2356, dtype: bool"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking for irregularities in dog name\n",
    "# Observations - lowercase appear to be all non dog names\n",
    "mask = df.name.str.contains('^[a-z]', na = False, regex = True)\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "a               55\n",
       "actually         2\n",
       "all              1\n",
       "an               7\n",
       "by               1\n",
       "getting          2\n",
       "his              1\n",
       "incredibly       1\n",
       "infuriating      1\n",
       "just             4\n",
       "life             1\n",
       "light            1\n",
       "mad              2\n",
       "my               1\n",
       "not              2\n",
       "officially       1\n",
       "old              1\n",
       "one              4\n",
       "quite            4\n",
       "space            1\n",
       "such             1\n",
       "the              8\n",
       "this             1\n",
       "unacceptable     1\n",
       "very             5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[mask].name.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>881536004380872706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-02 15:32:16 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a pupper approaching maximum borkdrive...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/881536004...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pupper</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>792913359805018113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-31 02:17:31 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a perfect example of someone who has t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/792913359...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>778396591732486144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-21 00:53:04 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is an East African Chalupa...</td>\n",
       "      <td>7.030419e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-02-26 02:20:37 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/703041949...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>an</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>772581559778025472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-04 23:46:12 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Guys this is getting so out of hand. We only r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/772581559...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>765395769549590528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-16 03:52:26 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is my dog. Her name is Zoey. She knows I'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/765395769...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>my</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:35:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is an odd dog. Hard on the outside but lo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666051853...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>an</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:30:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a truly beautiful English Wilson Staff...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666050758...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "56    881536004380872706                    NaN                  NaN   \n",
       "649   792913359805018113                    NaN                  NaN   \n",
       "759   778396591732486144                    NaN                  NaN   \n",
       "801   772581559778025472                    NaN                  NaN   \n",
       "852   765395769549590528                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2349  666051853826850816                    NaN                  NaN   \n",
       "2350  666050758794694657                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "56    2017-07-02 15:32:16 +0000   \n",
       "649   2016-10-31 02:17:31 +0000   \n",
       "759   2016-09-21 00:53:04 +0000   \n",
       "801   2016-09-04 23:46:12 +0000   \n",
       "852   2016-08-16 03:52:26 +0000   \n",
       "...                         ...   \n",
       "2349  2015-11-16 00:35:11 +0000   \n",
       "2350  2015-11-16 00:30:50 +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",
       "\n",
       "                                                 source  \\\n",
       "56    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "649   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "759   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "801   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "852   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2349  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2350  <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",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "56    Here is a pupper approaching maximum borkdrive...                  NaN   \n",
       "649   Here is a perfect example of someone who has t...                  NaN   \n",
       "759   RT @dog_rates: This is an East African Chalupa...         7.030419e+17   \n",
       "801   Guys this is getting so out of hand. We only r...                  NaN   \n",
       "852   This is my dog. Her name is Zoey. She knows I'...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2349  This is an odd dog. Hard on the outside but lo...                  NaN   \n",
       "2350  This is a truly beautiful English Wilson Staff...                  NaN   \n",
       "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",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "56                         NaN                        NaN   \n",
       "649                        NaN                        NaN   \n",
       "759               4.196984e+09  2016-02-26 02:20:37 +0000   \n",
       "801                        NaN                        NaN   \n",
       "852                        NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2349                       NaN                        NaN   \n",
       "2350                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "56    https://twitter.com/dog_rates/status/881536004...                14   \n",
       "649   https://twitter.com/dog_rates/status/792913359...                13   \n",
       "759   https://twitter.com/dog_rates/status/703041949...                10   \n",
       "801   https://twitter.com/dog_rates/status/772581559...                10   \n",
       "852   https://twitter.com/dog_rates/status/765395769...                13   \n",
       "...                                                 ...               ...   \n",
       "2349  https://twitter.com/dog_rates/status/666051853...                 2   \n",
       "2350  https://twitter.com/dog_rates/status/666050758...                10   \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",
       "\n",
       "      rating_denominator name doggo floofer  pupper puppo  \n",
       "56                    10    a   NaN     NaN  pupper   NaN  \n",
       "649                   10    a   NaN     NaN     NaN   NaN  \n",
       "759                   10   an   NaN     NaN     NaN   NaN  \n",
       "801                   10    a   NaN     NaN     NaN   NaN  \n",
       "852                   10   my   NaN     NaN     NaN   NaN  \n",
       "...                  ...  ...   ...     ...     ...   ...  \n",
       "2349                  10   an   NaN     NaN     NaN   NaN  \n",
       "2350                  10    a   NaN     NaN     NaN   NaN  \n",
       "2352                  10    a   NaN     NaN     NaN   NaN  \n",
       "2353                  10    a   NaN     NaN     NaN   NaN  \n",
       "2354                  10    a   NaN     NaN     NaN   NaN  \n",
       "\n",
       "[71 rows x 17 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect potentially erroneous dog names\n",
    "# Observations - many of these aren't actually dogs\n",
    "df.query('name ==\"a\" or name == \"the\" or name == \"an\" or name == \"my\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for text duplicates to see if dog is duplicated or just dog name\n",
    "df.text.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: timestamp, dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to see most recent timestamp\n",
    "# Observations - August 8th 2017 latest time stamp\n",
    "df.timestamp.sort_values(ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas   NaN     NaN    NaN   NaN  \n",
       "1                  10     Tilly   NaN     NaN    NaN   NaN  \n",
       "2                  10    Archie   NaN     NaN    NaN   NaN  \n",
       "3                  10     Darla   NaN     NaN    NaN   NaN  \n",
       "4                  10  Franklin   NaN     NaN    NaN   NaN  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine dogs that are 'floofer'\n",
    "# Observations - index 200 is also labelled 'doggo'\n",
    "df[df.floofer != 'None'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect dogs that don't have a stage\n",
    "# Observations - some exist without a stage\n",
    "df.query('doggo == \"None\" and floofer == \"None\" and pupper == \"None\" and puppo == \"None\"').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating_denominator\n",
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "20        2\n",
       "80        2\n",
       "70        1\n",
       "7         1\n",
       "15        1\n",
       "150       1\n",
       "170       1\n",
       "0         1\n",
       "90        1\n",
       "40        1\n",
       "130       1\n",
       "110       1\n",
       "16        1\n",
       "120       1\n",
       "2         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine denominators\n",
    "# Observations - mostly '10' but not all the same\n",
    "df['rating_denominator'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess dataframe, 'df_predictions'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual assessment\n",
    "# Observations - useful information can be used to identify as dog and dog breed, assessed three times with varying confidence\n",
    "df_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\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   p2_conf  \\\n",
       "2070        2       basset  0.555712    True    English_springer  0.225770   \n",
       "2071        1  paper_towel  0.170278   False  Labrador_retriever  0.168086   \n",
       "2072        1    Chihuahua  0.716012    True            malamute  0.078253   \n",
       "2073        1    Chihuahua  0.323581    True            Pekinese  0.090647   \n",
       "2074        1       orange  0.097049   False               bagel  0.085851   \n",
       "\n",
       "      p2_dog                           p3   p3_conf  p3_dog  \n",
       "2070    True  German_short-haired_pointer  0.175219    True  \n",
       "2071    True                      spatula  0.040836   False  \n",
       "2072    True                       kelpie  0.031379    True  \n",
       "2073    True                     papillon  0.068957    True  \n",
       "2074   False                       banana  0.076110   False  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual assessment\n",
    "# Observations - some images are recognized not as dogs e.g. paper_towel, orange\n",
    "df_predictions.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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": [
    "# Programmatic assessment\n",
    "# Observations - 2075 entries, all non-null, tweet_id is also an integer as with first df\n",
    "df_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Programmatic assessment\n",
    "# Observations - nothing specific\n",
    "df_predictions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p1\n",
       "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: count, Length: 378, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assses dog breeds in p1\n",
    "# Observations - many non-dog values e.g. sea_urchin\n",
    "df_predictions.p1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p2\n",
       "Labrador_retriever    104\n",
       "golden_retriever       92\n",
       "Cardigan               73\n",
       "Chihuahua              44\n",
       "Pomeranian             42\n",
       "                     ... \n",
       "medicine_chest          1\n",
       "quail                   1\n",
       "horse_cart              1\n",
       "waffle_iron             1\n",
       "bagel                   1\n",
       "Name: count, Length: 405, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Assses dog breeds in p2\n",
    "# Observations - many non-dog values e.g. laptop\n",
    "df_predictions.p2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p3\n",
       "Labrador_retriever    79\n",
       "Chihuahua             58\n",
       "golden_retriever      48\n",
       "Eskimo_dog            38\n",
       "kelpie                35\n",
       "                      ..\n",
       "ox                     1\n",
       "assault_rifle          1\n",
       "axolotl                1\n",
       "pot                    1\n",
       "banana                 1\n",
       "Name: count, Length: 408, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assses dog breeds in p3\n",
    "# Observations - many non-dog values e.g. beach_wagon\n",
    "df_predictions.p3.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p1_dog\n",
       "True     1532\n",
       "False     543\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assess number non-dog in p1\n",
    "df_predictions.p1_dog.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p2_dog\n",
       "True     1553\n",
       "False     522\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assess number non-dog in p2\n",
    "df_predictions.p2_dog.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p3_dog\n",
       "True     1499\n",
       "False     576\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assess number non-dog in p3\n",
    "df_predictions.p3_dog.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess dataframe, 'df_API'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>8853</td>\n",
       "      <td>39467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>6514</td>\n",
       "      <td>33819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>4328</td>\n",
       "      <td>25461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>8964</td>\n",
       "      <td>42908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>9774</td>\n",
       "      <td>41048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  retweet_count  favorite_count\n",
       "0  892420643555336193           8853           39467\n",
       "1  892177421306343426           6514           33819\n",
       "2  891815181378084864           4328           25461\n",
       "3  891689557279858688           8964           42908\n",
       "4  891327558926688256           9774           41048"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intiail visual assessment\n",
    "# Observations - useful infomration such as tweet_id, favorite_count and retweet_count to aid in analysis\n",
    "df_API.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   tweet_id        2354 non-null   object\n",
      " 1   retweet_count   2354 non-null   int64 \n",
      " 2   favorite_count  2354 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 55.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Programmatic assessment\n",
    "# Observations - 2354 entries, 0 null values, tweed_id is object not integer (this will need to be changed to merge with other dfs)\n",
    "df_API.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2354.000000</td>\n",
       "      <td>2354.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3164.797366</td>\n",
       "      <td>8080.968564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5284.770364</td>\n",
       "      <td>11814.771334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>624.500000</td>\n",
       "      <td>1415.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1473.500000</td>\n",
       "      <td>3603.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3652.000000</td>\n",
       "      <td>10122.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79515.000000</td>\n",
       "      <td>132810.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       retweet_count  favorite_count\n",
       "count    2354.000000     2354.000000\n",
       "mean     3164.797366     8080.968564\n",
       "std      5284.770364    11814.771334\n",
       "min         0.000000        0.000000\n",
       "25%       624.500000     1415.000000\n",
       "50%      1473.500000     3603.500000\n",
       "75%      3652.000000    10122.250000\n",
       "max     79515.000000   132810.000000"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Programmatic assessment\n",
    "# Observations - favorite count ranges from zero to 132810 and retweet_count from 0 to 79515\n",
    "df_API.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issues\n",
    "\n",
    "Note: Three new dataframes with the suffix '_clean' are created to preserve raw data.\n",
    "\n",
    "1. Regarding 'df_clean', remove 181 retweets.\n",
    "\n",
    "2. Regarding 'df_clean', remove rows that are using typo dog names. For the most part these appear to be spam (lobsters, humans, etc.), and names with lowercase first characters. This includes the following names; 'a', 'the', 'an', 'my'.\n",
    "\n",
    "3. Regarding 'df_clean', recreate the user rating from 'text' column and convert to float.\n",
    "\n",
    "4. Regarding 'df_clean', concatenate rating_numerator2 and rating_denominator2 into a single column 'score_ratio' and calculate a new value converting the ratio into a percentage for ease of analysis (readability and ranking), 'score_percentage'.\n",
    "\n",
    "5. Regarding 'df_predictions_clean', keep rows where each AI recognizes the image as a dog.\n",
    "\n",
    "6. Regarding 'df_API_clean', convert data type of column 'tweet_id' in dataframe 'df_API_clean' to int64.\n",
    "\n",
    "### Tidiness issues\n",
    "\n",
    "1. Regarding 'df_clean', drop columns that aren't of interest in later analysis.\n",
    "\n",
    "2. Regarding 'df_clean', create a new column to account for dogs that aren't defined as either doggo, floofer, pupper and pupper. Once complete, melt the *doggo*, *floofer*, *pupper*, *puppo* and new 'Unknown' columns to a *dog_stage* column. Remove 'None' entries. Remove those that had multiple dog_stage definitions (remove duplicates).\n",
    "\n",
    "3. Merge 'df_predictions_clean' with 'df_clean', then merge 'df_API_clean' with 'df_clean'."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 32,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Cleaning Data\n",
    "In this section, clean **all** of the issues you documented while assessing. \n",
    "\n",
    "**Note:** Make a copy of the original data before cleaning. Cleaning includes merging individual pieces of data according to the rules of [tidy data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html). The result should be a high-quality and tidy master pandas DataFrame (or DataFrames, if appropriate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of original pieces of data\n",
    "df_clean = df.copy()\n",
    "df_predictions_clean = df_predictions.copy()\n",
    "df_API_clean = df_API.copy()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #1:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Regarding 'df_clean', remove 181 retweets."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = df_clean[df_clean.retweeted_status_id.isnull()==True]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[df_clean.retweeted_status_id.isnull()==False]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #2:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "#### Define\n",
    "Regarding 'df_clean', remove rows that are using typo dog names. For the most part these appear to be spam (lobsters, humans, etc.), and names with lowercase first characters. This includes the following names; 'a', 'the', 'an', 'my'."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "2351    False\n",
       "2352     True\n",
       "2353     True\n",
       "2354     True\n",
       "2355    False\n",
       "Name: name, Length: 2175, dtype: bool"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove the dog names lowercase values from the dataset, you can index of rows where the dog names were lower case\n",
    "mask = df_clean.name.str.islower().fillna(False)\n",
    "mask"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_dog_name_index = df_clean[mask].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then drop the rows using this index\n",
    "df_clean.drop(lower_dog_name_index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: count, dtype: int64)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df_clean.name.str.contains('^[a-z]', na= False, regex = True)\n",
    "df_clean[mask].name.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2071, 17)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #3:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Regarding 'df_clean', recreate the user rating from 'text' column and convert to float."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean[['rating_numerator2', 'rating_denominator2']] = df_clean.text.str.extract('((?:\\d+\\.)?\\d+)\\/(\\d+)', expand=True)\n",
    "df_clean = df_clean.astype({'rating_numerator2': float,'rating_denominator2':float})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>rating_numerator2</th>\n",
       "      <th>rating_denominator2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</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",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  rating_numerator2  \\\n",
       "0                  10  Phineas   NaN     NaN    NaN   NaN               13.0   \n",
       "\n",
       "   rating_denominator2  \n",
       "0                 10.0  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2071 entries, 0 to 2355\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2071 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                   2071 non-null   object \n",
      " 4   source                      2071 non-null   object \n",
      " 5   text                        2071 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               2013 non-null   object \n",
      " 10  rating_numerator            2071 non-null   int64  \n",
      " 11  rating_denominator          2071 non-null   int64  \n",
      " 12  name                        1391 non-null   object \n",
      " 13  doggo                       84 non-null     object \n",
      " 14  floofer                     10 non-null     object \n",
      " 15  pupper                      222 non-null    object \n",
      " 16  puppo                       25 non-null     object \n",
      " 17  rating_numerator2           2071 non-null   float64\n",
      " 18  rating_denominator2         2071 non-null   float64\n",
      "dtypes: float64(6), int64(3), object(10)\n",
      "memory usage: 323.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_clean.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #4:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Regarding 'df_clean', concatenate rating_numerator2 and rating_denominator2 into a single column 'score_ratio' and calculate a new value converting the ratio into a percentage for ease of analysis (readability and ranking), 'score_percentage'."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>rating_numerator2</th>\n",
       "      <th>rating_denominator2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>835246439529840640</td>\n",
       "      <td>8.352460e+17</td>\n",
       "      <td>26259576.0</td>\n",
       "      <td>2017-02-24 21:54:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@jonnysun @Lin_Manuel ok jomny I know you're e...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-19 23:06:23 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspir...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.gofundme.com/sams-smile,https://tw...</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Sam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "313  835246439529840640           8.352460e+17           26259576.0   \n",
       "342  832088576586297345           8.320875e+17           30582082.0   \n",
       "433  820690176645140481                    NaN                  NaN   \n",
       "516  810984652412424192                    NaN                  NaN   \n",
       "902  758467244762497024                    NaN                  NaN   \n",
       "\n",
       "                     timestamp  \\\n",
       "313  2017-02-24 21:54:03 +0000   \n",
       "342  2017-02-16 04:45:50 +0000   \n",
       "433  2017-01-15 17:52:40 +0000   \n",
       "516  2016-12-19 23:06:23 +0000   \n",
       "902  2016-07-28 01:00:57 +0000   \n",
       "\n",
       "                                                source  \\\n",
       "313  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "342  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "433  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "516  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "902  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                  text  retweeted_status_id  \\\n",
       "313  @jonnysun @Lin_Manuel ok jomny I know you're e...                  NaN   \n",
       "342           @docmisterio account started on 11/15/15                  NaN   \n",
       "433  The floofs have been released I repeat the flo...                  NaN   \n",
       "516  Meet Sam. She smiles 24/7 &amp; secretly aspir...                  NaN   \n",
       "902  Why does this never happen at my front door......                  NaN   \n",
       "\n",
       "     retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "313                       NaN                        NaN   \n",
       "342                       NaN                        NaN   \n",
       "433                       NaN                        NaN   \n",
       "516                       NaN                        NaN   \n",
       "902                       NaN                        NaN   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "313                                                NaN               960   \n",
       "342                                                NaN                11   \n",
       "433  https://twitter.com/dog_rates/status/820690176...                84   \n",
       "516  https://www.gofundme.com/sams-smile,https://tw...                24   \n",
       "902  https://twitter.com/dog_rates/status/758467244...               165   \n",
       "\n",
       "     rating_denominator name doggo floofer pupper puppo  rating_numerator2  \\\n",
       "313                   0  NaN   NaN     NaN    NaN   NaN              960.0   \n",
       "342                  15  NaN   NaN     NaN    NaN   NaN               11.0   \n",
       "433                  70  NaN   NaN     NaN    NaN   NaN               84.0   \n",
       "516                   7  Sam   NaN     NaN    NaN   NaN               24.0   \n",
       "902                 150  NaN   NaN     NaN    NaN   NaN              165.0   \n",
       "\n",
       "     rating_denominator2  \n",
       "313                  0.0  \n",
       "342                 15.0  \n",
       "433                 70.0  \n",
       "516                  7.0  \n",
       "902                150.0  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[df_clean.rating_denominator2 != 10].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['score_percentage'] = (df_clean.rating_numerator2 / df_clean.rating_denominator2)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['score_ratio'] = df_clean[\"rating_numerator2\"].astype(str) + '/' + df_clean[\"rating_denominator2\"].astype(str)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "score_ratio\n",
       "12.0/10.0      485\n",
       "10.0/10.0      419\n",
       "11.0/10.0      409\n",
       "13.0/10.0      300\n",
       "9.0/10.0       141\n",
       "8.0/10.0        91\n",
       "7.0/10.0        47\n",
       "14.0/10.0       39\n",
       "5.0/10.0        33\n",
       "6.0/10.0        30\n",
       "3.0/10.0        18\n",
       "4.0/10.0        12\n",
       "1.0/10.0         7\n",
       "2.0/10.0         7\n",
       "4.0/20.0         2\n",
       "420.0/10.0       2\n",
       "0.0/10.0         2\n",
       "80.0/80.0        1\n",
       "45.0/50.0        1\n",
       "44.0/40.0        1\n",
       "17.0/10.0        1\n",
       "143.0/130.0      1\n",
       "99.0/90.0        1\n",
       "7.0/11.0         1\n",
       "20.0/16.0        1\n",
       "9.5/10.0         1\n",
       "11.26/10.0       1\n",
       "144.0/120.0      1\n",
       "121.0/110.0      1\n",
       "165.0/150.0      1\n",
       "50.0/50.0        1\n",
       "9.0/11.0         1\n",
       "1776.0/10.0      1\n",
       "13.5/10.0        1\n",
       "11.27/10.0       1\n",
       "9.75/10.0        1\n",
       "24.0/7.0         1\n",
       "84.0/70.0        1\n",
       "11.0/15.0        1\n",
       "960.0/0.0        1\n",
       "15.0/10.0        1\n",
       "182.0/10.0       1\n",
       "666.0/10.0       1\n",
       "88.0/80.0        1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is noted that one of the results is divided by zero and thus equates to infinity, this is ignored because it's part of the fun.\n",
    "df_clean['score_ratio'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #5:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Regarding 'df_predictions_clean', keep rows where each AI recognizes the image as a dog."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predictions_clean = df_predictions_clean.query('p1_dog == True and p2_dog == True and p3_dog == True')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions_clean.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #6:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Regarding 'df_API_clean', convert data type of column 'tweet_id' in dataframe 'df_API_clean' to int64."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_API_clean = df_API.astype({\"tweet_id\": int})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        2354 non-null   int64\n",
      " 1   retweet_count   2354 non-null   int64\n",
      " 2   favorite_count  2354 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "df_API_clean.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness Issue #1:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Regarding 'df_clean', drop columns that aren't of interest in later analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean.drop(['in_reply_to_status_id','in_reply_to_user_id','source','retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp','expanded_urls','rating_numerator','rating_denominator'],axis=1,inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>rating_numerator2</th>\n",
       "      <th>rating_denominator2</th>\n",
       "      <th>score_percentage</th>\n",
       "      <th>score_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.0/10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                  timestamp  \\\n",
       "0  892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "\n",
       "                                                text     name doggo floofer  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...  Phineas   NaN     NaN   \n",
       "\n",
       "  pupper puppo  rating_numerator2  rating_denominator2  score_percentage  \\\n",
       "0    NaN   NaN               13.0                 10.0             130.0   \n",
       "\n",
       "  score_ratio  \n",
       "0   13.0/10.0  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness Issue #2:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Melt dog stage columns into single column and fix multiple stage tweets."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan], dtype=object)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# handle none\n",
    "df_clean.doggo.replace('None', '', inplace=True)\n",
    "df_clean.floofer.replace('None', '', inplace=True)\n",
    "df_clean.pupper.replace('None', '', inplace=True)\n",
    "df_clean.puppo.replace('None', '', inplace=True)\n",
    "\n",
    "# merge into column\n",
    "df_clean['dog_stage'] = df_clean.doggo + df_clean.floofer + df_clean.pupper + df_clean.puppo\n",
    "\n",
    "df_clean.dog_stage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# handle multiple stages\n",
    "df_clean.loc[df_clean.dog_stage == 'doggofloofer', 'dog_stage'] = 'doggo, floofer'\n",
    "df_clean.loc[df_clean.dog_stage == 'doggopupper', 'dog_stage'] = 'doggo, pupper'\n",
    "df_clean.loc[df_clean.dog_stage == 'doggopuppo', 'dog_stage'] = 'doggo, puppo'\n",
    "\n",
    "# handle missing values\n",
    "df_clean.loc[df_clean.dog_stage == '', 'dog_stage'] = np.nan\n",
    "\n",
    "# drop old columns\n",
    "df_clean.drop(['doggo', 'floofer', 'pupper', 'puppo'], axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan], dtype=object)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.dog_stage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "      <th>rating_numerator2</th>\n",
       "      <th>rating_denominator2</th>\n",
       "      <th>score_percentage</th>\n",
       "      <th>score_ratio</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                  timestamp  \\\n",
       "0  892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "\n",
       "                                                text     name  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...  Phineas   \n",
       "\n",
       "   rating_numerator2  rating_denominator2  score_percentage score_ratio  \\\n",
       "0               13.0                 10.0             130.0   13.0/10.0   \n",
       "\n",
       "  dog_stage  \n",
       "0       NaN  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness Issue #3:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Merge 'df_predictions_clean' with 'df_clean', then merge 'df_API_clean' with 'df_clean'."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = pd.merge(df_clean, df_predictions_clean, on=['tweet_id'], how='inner')\n",
    "df_clean = pd.merge(df_clean, df_API_clean, on=['tweet_id'], how='inner')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "      <th>rating_numerator2</th>\n",
       "      <th>rating_denominator2</th>\n",
       "      <th>score_percentage</th>\n",
       "      <th>score_ratio</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>...</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</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 +0000</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>...</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",
       "      <td>6514</td>\n",
       "      <td>33819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                  timestamp  \\\n",
       "0  892177421306343426  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                                text   name  \\\n",
       "0  This is Tilly. She's just checking pup on you....  Tilly   \n",
       "\n",
       "   rating_numerator2  rating_denominator2  score_percentage score_ratio  \\\n",
       "0               13.0                 10.0             130.0   13.0/10.0   \n",
       "\n",
       "  dog_stage                                          jpg_url  ...   p1_conf  \\\n",
       "0       NaN  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg  ...  0.323581   \n",
       "\n",
       "  p1_dog        p2   p2_conf p2_dog        p3   p3_conf p3_dog  retweet_count  \\\n",
       "0   True  Pekinese  0.090647   True  papillon  0.068957   True           6514   \n",
       "\n",
       "   favorite_count  \n",
       "0           33819  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1148, 22)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "      <th>rating_numerator2</th>\n",
       "      <th>rating_denominator2</th>\n",
       "      <th>score_percentage</th>\n",
       "      <th>score_ratio</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>...</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</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 +0000</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>...</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",
       "      <td>6514</td>\n",
       "      <td>33819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>Archie</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>12.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>...</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",
       "      <td>4328</td>\n",
       "      <td>25461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>12.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>...</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",
       "      <td>9774</td>\n",
       "      <td>41048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>890729181411237888</td>\n",
       "      <td>2017-07-28 00:22:40 +0000</td>\n",
       "      <td>When you watch your owner call another dog a g...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.566142</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.178406</td>\n",
       "      <td>True</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.076507</td>\n",
       "      <td>True</td>\n",
       "      <td>16716</td>\n",
       "      <td>56848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>890609185150312448</td>\n",
       "      <td>2017-07-27 16:25:51 +0000</td>\n",
       "      <td>This is Zoey. She doesn't want to be one of th...</td>\n",
       "      <td>Zoey</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487574</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_setter</td>\n",
       "      <td>0.193054</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.118184</td>\n",
       "      <td>True</td>\n",
       "      <td>4429</td>\n",
       "      <td>28226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>2015-11-16 02:38:37 +0000</td>\n",
       "      <td>Here we have a well-established sunblockerspan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489814</td>\n",
       "      <td>True</td>\n",
       "      <td>bull_mastiff</td>\n",
       "      <td>0.404722</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.048960</td>\n",
       "      <td>True</td>\n",
       "      <td>47</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>2015-11-16 01:59:36 +0000</td>\n",
       "      <td>Let's hope this flight isn't Malaysian (lol). ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.260857</td>\n",
       "      <td>True</td>\n",
       "      <td>English_foxhound</td>\n",
       "      <td>0.175382</td>\n",
       "      <td>True</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.097471</td>\n",
       "      <td>True</td>\n",
       "      <td>174</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>2015-11-16 01:52:02 +0000</td>\n",
       "      <td>Here we have a northern speckled Rhododendron....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.503672</td>\n",
       "      <td>True</td>\n",
       "      <td>Yorkshire_terrier</td>\n",
       "      <td>0.174201</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.109454</td>\n",
       "      <td>True</td>\n",
       "      <td>67</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "      <td>41</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.0/10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "      <td>532</td>\n",
       "      <td>2535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1148 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                  timestamp  \\\n",
       "0     892177421306343426  2017-08-01 00:17:27 +0000   \n",
       "1     891815181378084864  2017-07-31 00:18:03 +0000   \n",
       "2     891327558926688256  2017-07-29 16:00:24 +0000   \n",
       "3     890729181411237888  2017-07-28 00:22:40 +0000   \n",
       "4     890609185150312448  2017-07-27 16:25:51 +0000   \n",
       "...                  ...                        ...   \n",
       "1143  666082916733198337  2015-11-16 02:38:37 +0000   \n",
       "1144  666073100786774016  2015-11-16 01:59:36 +0000   \n",
       "1145  666071193221509120  2015-11-16 01:52:02 +0000   \n",
       "1146  666049248165822465  2015-11-16 00:24:50 +0000   \n",
       "1147  666020888022790149  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                   text      name  \\\n",
       "0     This is Tilly. She's just checking pup on you....     Tilly   \n",
       "1     This is Archie. He is a rare Norwegian Pouncin...    Archie   \n",
       "2     This is Franklin. He would like you to stop ca...  Franklin   \n",
       "3     When you watch your owner call another dog a g...       NaN   \n",
       "4     This is Zoey. She doesn't want to be one of th...      Zoey   \n",
       "...                                                 ...       ...   \n",
       "1143  Here we have a well-established sunblockerspan...       NaN   \n",
       "1144  Let's hope this flight isn't Malaysian (lol). ...       NaN   \n",
       "1145  Here we have a northern speckled Rhododendron....       NaN   \n",
       "1146  Here we have a 1949 1st generation vulpix. Enj...       NaN   \n",
       "1147  Here we have a Japanese Irish Setter. Lost eye...       NaN   \n",
       "\n",
       "      rating_numerator2  rating_denominator2  score_percentage score_ratio  \\\n",
       "0                  13.0                 10.0             130.0   13.0/10.0   \n",
       "1                  12.0                 10.0             120.0   12.0/10.0   \n",
       "2                  12.0                 10.0             120.0   12.0/10.0   \n",
       "3                  13.0                 10.0             130.0   13.0/10.0   \n",
       "4                  13.0                 10.0             130.0   13.0/10.0   \n",
       "...                 ...                  ...               ...         ...   \n",
       "1143                6.0                 10.0              60.0    6.0/10.0   \n",
       "1144               10.0                 10.0             100.0   10.0/10.0   \n",
       "1145                9.0                 10.0              90.0    9.0/10.0   \n",
       "1146                5.0                 10.0              50.0    5.0/10.0   \n",
       "1147                8.0                 10.0              80.0    8.0/10.0   \n",
       "\n",
       "     dog_stage                                          jpg_url  ...  \\\n",
       "0          NaN  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg  ...   \n",
       "1          NaN  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg  ...   \n",
       "2          NaN  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg  ...   \n",
       "3          NaN  https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg  ...   \n",
       "4          NaN  https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg  ...   \n",
       "...        ...                                              ...  ...   \n",
       "1143       NaN  https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg  ...   \n",
       "1144       NaN  https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg  ...   \n",
       "1145       NaN  https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg  ...   \n",
       "1146       NaN  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg  ...   \n",
       "1147       NaN  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg  ...   \n",
       "\n",
       "       p1_conf p1_dog                 p2   p2_conf p2_dog  \\\n",
       "0     0.323581   True           Pekinese  0.090647   True   \n",
       "1     0.716012   True           malamute  0.078253   True   \n",
       "2     0.555712   True   English_springer  0.225770   True   \n",
       "3     0.566142   True         Eskimo_dog  0.178406   True   \n",
       "4     0.487574   True       Irish_setter  0.193054   True   \n",
       "...        ...    ...                ...       ...    ...   \n",
       "1143  0.489814   True       bull_mastiff  0.404722   True   \n",
       "1144  0.260857   True   English_foxhound  0.175382   True   \n",
       "1145  0.503672   True  Yorkshire_terrier  0.174201   True   \n",
       "1146  0.560311   True         Rottweiler  0.243682   True   \n",
       "1147  0.465074   True             collie  0.156665   True   \n",
       "\n",
       "                               p3   p3_conf p3_dog  retweet_count  \\\n",
       "0                        papillon  0.068957   True           6514   \n",
       "1                          kelpie  0.031379   True           4328   \n",
       "2     German_short-haired_pointer  0.175219   True           9774   \n",
       "3                        Pembroke  0.076507   True          16716   \n",
       "4        Chesapeake_Bay_retriever  0.118184   True           4429   \n",
       "...                           ...       ...    ...            ...   \n",
       "1143               French_bulldog  0.048960   True             47   \n",
       "1144                 Ibizan_hound  0.097471   True            174   \n",
       "1145                     Pekinese  0.109454   True             67   \n",
       "1146                     Doberman  0.154629   True             41   \n",
       "1147            Shetland_sheepdog  0.061428   True            532   \n",
       "\n",
       "      favorite_count  \n",
       "0              33819  \n",
       "1              25461  \n",
       "2              41048  \n",
       "3              56848  \n",
       "4              28226  \n",
       "...              ...  \n",
       "1143             121  \n",
       "1144             335  \n",
       "1145             154  \n",
       "1146             111  \n",
       "1147            2535  \n",
       "\n",
       "[1148 rows x 22 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean"
   ]
  },
  {
   "attachments": {},
   "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": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean.to_csv('twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and Visualizing Data\n",
    "In this section, analyze and visualize your wrangled data. You must produce at least **three (3) insights and one (1) visualization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261           golden_retriever\n",
       "209               Irish_setter\n",
       "110    black-and-tan_coonhound\n",
       "Name: p1, dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top three tweets by 'score_percentage'\n",
    "df_clean.sort_values('score_percentage', ascending = False).p1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p1\n",
       "black-and-tan_coonhound    140.000000\n",
       "Saluki                     125.000000\n",
       "briard                     123.333333\n",
       "Name: score_percentage, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'p1' and assess the mean 'score_percentage', sort for top three results\n",
    "df_clean.groupby('p1')['score_percentage'].mean().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p1\n",
       "black-and-tan_coonhound    33911.0\n",
       "Irish_water_spaniel        25225.0\n",
       "Saluki                     24060.0\n",
       "Name: favorite_count, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'p1' and assess the mean 'favorite_count', sort for top three results\n",
    "df_clean.groupby('p1')['favorite_count'].mean().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "1. The top three scoring dogs (using 'p1' as the most confident AI interpretation) are 'golden_retriever', 'Irish_setter', 'black-and-tan_coonhound'.\n",
    "\n",
    "2. The top three highest scoring dog breeds by mean (using 'p1' as the most confident AI interpretation) are 'black-and-tan_coonhound' and 'Saluki' and 'briard'.\n",
    "\n",
    "3. The top three highest dog breeds w.r.t. 'favorite_count' by mean (using 'p1' as the most confident AI interpretation) are 'black-and-tan_coonhound', 'Irish_water_spaniel', and 'Saluki'."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As score mean can be swayed by a small number of biased tweets, I think favorite count (multi-user rated) would be a better value for visualisation of popular dog breeds\n",
    "df_clean.groupby('p1')['favorite_count'].mean().sort_values(ascending=False).head(10).plot(kind='bar',alpha=0.5, color= 'Blue');\n",
    "plt.figsize=(10,8);\n",
    "plt.title('Top 10 Mean Favorite Counts of Tweets by Dog Breed');\n",
    "plt.xlabel('Dog Breed');\n",
    "plt.ylabel('Favorite Count');"
   ]
  }
 ],
 "metadata": {
  "extensions": {
   "jupyter_dashboards": {
    "activeView": "report_default",
    "version": 1,
    "views": {
     "grid_default": {
      "cellMargin": 10,
      "defaultCellHeight": 20,
      "maxColumns": 12,
      "name": "grid",
      "type": "grid"
     },
     "report_default": {
      "name": "report",
      "type": "report"
     }
    }
   }
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}