Ingest, Transform and Visualize Data with AWS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Register and visualize dataset\n",
    "\n",
    "In this notebook - ingest and transform the customer product reviews dataset with AWS data stack services such as AWS Glue and Amazon Athena for ingesting and querying the dataset. Finally with the help of AWS Data Wrangler to analyze the dataset and plot some visuals extracting insights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "\n",
    "- [1. Ingest and transform the public dataset](#c1w1-1.)\n",
    "  - [1.1. List the dataset files in the public S3 bucket](#c1w1-1.1.)\n",
    "    - [Exercise 1](#c1w1-ex-1)\n",
    "  - [1.2. Copy the data locally to the notebook](#c1w1-1.2.)\n",
    "  - [1.3. Transform the data](#c1w1-1.3.)\n",
    "  - [1.4 Write the data to a CSV file](#c1w1-1.4.)\n",
    "- [2. Register the public dataset for querying and visualizing](#c1w1-2.)\n",
    "  - [2.1. Register S3 dataset files as a table for querying](#c1w1-2.1.)\n",
    "    - [Exercise 2](#c1w1-ex-2)\n",
    "  - [2.2. Create default S3 bucket for Amazon Athena](#c1w1-2.2.)\n",
    "- [3. Visualize data](#c1w1-3.)\n",
    "  - [3.1. Preparation for data visualization](#c1w1-3.1.)\n",
    "  - [3.2. How many reviews per sentiment?](#c1w1-3.2.)\n",
    "    - [Exercise 3](#c1w1-ex-3)\n",
    "  - [3.3. Which product categories are highest rated by average sentiment?](#c1w1-3.3.)\n",
    "  - [3.4. Which product categories have the most reviews?](#c1w1-3.4.)\n",
    "    - [Exercise 4](#c1w1-ex-4)\n",
    "  - [3.5. What is the breakdown of sentiments per product category?](#c1w1-3.5.)\n",
    "  - [3.6. Analyze the distribution of review word counts](#c1w1-3.6.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's install the required modules first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# please ignore warning messages during the installation\n",
    "!pip install --disable-pip-version-check -q sagemaker==2.35.0\n",
    "!pip install --disable-pip-version-check -q pandas==1.1.4\n",
    "!pip install --disable-pip-version-check -q awswrangler==2.7.0\n",
    "!pip install --disable-pip-version-check -q numpy==1.18.5\n",
    "!pip install --disable-pip-version-check -q seaborn==0.11.0\n",
    "!pip install --disable-pip-version-check -q matplotlib===3.3.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-1.'></a>\n",
    "# 1. Ingest and transform the public dataset\n",
    "\n",
    "The dataset [Women's Clothing Reviews](https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews) has been chosen as the main dataset.\n",
    "\n",
    "It is shared in a public Amazon S3 bucket, and is available as a comma-separated value (CSV) text format:\n",
    "\n",
    "`s3://dlai-practical-data-science/data/raw/womens_clothing_ecommerce_reviews.csv`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-1.1.'></a>\n",
    "### 1.1. List the dataset files in the public S3 bucket\n",
    "\n",
    "The [AWS Command Line Interface (CLI)](https://awscli.amazonaws.com/v2/documentation/api/latest/index.html) is a unified tool to manage your AWS services. With just one tool, you can control multiple AWS services from the command line and automate them through scripts. You will use it to list the dataset files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**View dataset files in CSV format**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```aws s3 ls [bucket_name]``` function lists all objects in the S3 bucket. Let's use it to view the reviews data files in CSV format:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the list of the files available in the public bucket `s3://dlai-practical-data-science/data/raw/`.\n",
    "\n",
    "**Instructions**:\n",
    "Use `aws s3 ls [bucket_name]` function. To run the AWS CLI command from the notebook you will need to put an exclamation mark in front of it: `!aws`. You should see the data file `womens_clothing_ecommerce_reviews.csv` in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-04-30 02:21:06    8457214 womens_clothing_ecommerce_reviews.csv\n"
     ]
    }
   ],
   "source": [
    "!aws s3 ls s3://dlai-practical-data-science/data/raw/ # Replace None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Copy the data locally"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```aws s3 cp [bucket_name/file_name] [file_name]``` function copies the file from the S3 bucket into the local environment or into another S3 bucket. Let's use it to copy the file with the dataset locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://dlai-practical-data-science/data/raw/womens_clothing_ecommerce_reviews.csv to ./womens_clothing_ecommerce_reviews.csv\n"
     ]
    }
   ],
   "source": [
    "!aws s3 cp s3://dlai-practical-data-science/data/raw/womens_clothing_ecommerce_reviews.csv ./womens_clothing_ecommerce_reviews.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use the Pandas dataframe to load and preview the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23486, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "\n",
    "df = pd.read_csv('./womens_clothing_ecommerce_reviews.csv',\n",
    "                 index_col=0)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clothing ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "      <th>Division Name</th>\n",
       "      <th>Department Name</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>847</td>\n",
       "      <td>33</td>\n",
       "      <td>Cute, crisp shirt</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077</td>\n",
       "      <td>60</td>\n",
       "      <td>Some major design flaws</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1049</td>\n",
       "      <td>50</td>\n",
       "      <td>My favorite buy!</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847</td>\n",
       "      <td>47</td>\n",
       "      <td>Flattering shirt</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23481</th>\n",
       "      <td>1104</td>\n",
       "      <td>34</td>\n",
       "      <td>Great dress for many occasions</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23482</th>\n",
       "      <td>862</td>\n",
       "      <td>48</td>\n",
       "      <td>Wish it was made of cotton</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Knits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23483</th>\n",
       "      <td>1104</td>\n",
       "      <td>31</td>\n",
       "      <td>Cute, but see through</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23484</th>\n",
       "      <td>1084</td>\n",
       "      <td>28</td>\n",
       "      <td>Very cute dress, perfect for summer parties an...</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23485</th>\n",
       "      <td>1104</td>\n",
       "      <td>52</td>\n",
       "      <td>Please make more like this one!</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23486 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clothing ID  Age                                              Title  \\\n",
       "0              847   33                                  Cute, crisp shirt   \n",
       "1             1080   34                                                NaN   \n",
       "2             1077   60                            Some major design flaws   \n",
       "3             1049   50                                   My favorite buy!   \n",
       "4              847   47                                   Flattering shirt   \n",
       "...            ...  ...                                                ...   \n",
       "23481         1104   34                     Great dress for many occasions   \n",
       "23482          862   48                         Wish it was made of cotton   \n",
       "23483         1104   31                              Cute, but see through   \n",
       "23484         1084   28  Very cute dress, perfect for summer parties an...   \n",
       "23485         1104   52                    Please make more like this one!   \n",
       "\n",
       "                                             Review Text  Rating  \\\n",
       "0      If this product was in petite  i would get the...       4   \n",
       "1      Love this dress!  it's sooo pretty.  i happene...       5   \n",
       "2      I had such high hopes for this dress and reall...       3   \n",
       "3      I love  love  love this jumpsuit. it's fun  fl...       5   \n",
       "4      This shirt is very flattering to all due to th...       5   \n",
       "...                                                  ...     ...   \n",
       "23481  I was very happy to snag this dress at such a ...       5   \n",
       "23482  It reminds me of maternity clothes. soft  stre...       3   \n",
       "23483  This fit well  but the top was very see throug...       3   \n",
       "23484  I bought this dress for a wedding i have this ...       3   \n",
       "23485  This dress in a lovely platinum is feminine an...       5   \n",
       "\n",
       "       Recommended IND  Positive Feedback Count   Division Name  \\\n",
       "0                    1                        2         General   \n",
       "1                    1                        4         General   \n",
       "2                    0                        0         General   \n",
       "3                    1                        0  General Petite   \n",
       "4                    1                        6         General   \n",
       "...                ...                      ...             ...   \n",
       "23481                1                        0  General Petite   \n",
       "23482                1                        0  General Petite   \n",
       "23483                0                        1  General Petite   \n",
       "23484                1                        2         General   \n",
       "23485                1                       22  General Petite   \n",
       "\n",
       "      Department Name Class Name  \n",
       "0                Tops    Blouses  \n",
       "1             Dresses    Dresses  \n",
       "2             Dresses    Dresses  \n",
       "3             Bottoms      Pants  \n",
       "4                Tops    Blouses  \n",
       "...               ...        ...  \n",
       "23481         Dresses    Dresses  \n",
       "23482            Tops      Knits  \n",
       "23483         Dresses    Dresses  \n",
       "23484         Dresses    Dresses  \n",
       "23485         Dresses    Dresses  \n",
       "\n",
       "[23486 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-1.3.'></a>\n",
    "### 1.3. Transform the data\n",
    "To simplify the task, you will transform the data into a comma-separated value (CSV) file that contains only a `review_body`, `product_category`, and `sentiment` derived from the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22628, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed = df.rename(columns={'Review Text': 'review_body',\n",
    "                                    'Rating': 'star_rating',\n",
    "                                    'Class Name': 'product_category'})\n",
    "df_transformed.drop(columns=['Clothing ID', 'Age', 'Title', 'Recommended IND', 'Positive Feedback Count', 'Division Name', 'Department Name'],\n",
    "                    inplace=True)\n",
    "\n",
    "df_transformed.dropna(inplace=True)\n",
    "\n",
    "df_transformed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now convert the `star_rating` into the `sentiment` (positive, neutral, negative), which later on will be for the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22626, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_sentiment(star_rating):\n",
    "    if star_rating in {1, 2}: # negative\n",
    "        return -1 \n",
    "    if star_rating == 3:      # neutral\n",
    "        return 0\n",
    "    if star_rating in {4, 5}: # positive\n",
    "        return 1\n",
    "\n",
    "# transform star_rating into the sentiment\n",
    "df_transformed['sentiment'] = df_transformed['star_rating'].apply(lambda star_rating: \n",
    "    to_sentiment(star_rating=star_rating) \n",
    ")\n",
    "\n",
    "# drop the star rating column\n",
    "df_transformed.drop(columns=['star_rating'],\n",
    "                    inplace=True)\n",
    "\n",
    "# remove reviews for product_categories with < 10 reviews\n",
    "df_transformed = df_transformed.groupby('product_category').filter(lambda reviews : len(reviews) > 10)[['sentiment', 'review_body', 'product_category']]\n",
    "\n",
    "df_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>sentiment</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23481</th>\n",
       "      <td>1</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23482</th>\n",
       "      <td>0</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23483</th>\n",
       "      <td>0</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23484</th>\n",
       "      <td>0</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23485</th>\n",
       "      <td>1</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22626 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sentiment                                        review_body  \\\n",
       "0              1  If this product was in petite  i would get the...   \n",
       "1              1  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2              0  I had such high hopes for this dress and reall...   \n",
       "3              1  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4              1  This shirt is very flattering to all due to th...   \n",
       "...          ...                                                ...   \n",
       "23481          1  I was very happy to snag this dress at such a ...   \n",
       "23482          0  It reminds me of maternity clothes. soft  stre...   \n",
       "23483          0  This fit well  but the top was very see throug...   \n",
       "23484          0  I bought this dress for a wedding i have this ...   \n",
       "23485          1  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  \n",
       "0              Blouses  \n",
       "1              Dresses  \n",
       "2              Dresses  \n",
       "3                Pants  \n",
       "4              Blouses  \n",
       "...                ...  \n",
       "23481          Dresses  \n",
       "23482            Knits  \n",
       "23483          Dresses  \n",
       "23484          Dresses  \n",
       "23485          Dresses  \n",
       "\n",
       "[22626 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the results\n",
    "df_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-1.4.'></a>\n",
    "### 1.4 Write the data to a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transformed.to_csv('./womens_clothing_ecommerce_reviews_transformed.csv', \n",
    "                      index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sentiment,review_body,product_category\n",
      "1,If this product was in petite  i would get the petite. the regular is a little long on me but a tailor can do a simple fix on that.     fits nicely! i'm 5'4  130lb and pregnant so i bough t medium to grow into.     the tie can be front or back so provides for some nice flexibility on form fitting.,Blouses\n",
      "1,\"Love this dress!  it's sooo pretty.  i happened to find it in a store  and i'm glad i did bc i never would have ordered it online bc it's petite.  i bought a petite and am 5'8\"\".  i love the length on me- hits just a little below the knee.  would definitely be a true midi on someone who is truly petite.\",Dresses\n",
      "0,I had such high hopes for this dress and really wanted it to work for me. i initially ordered the petite small (my usual size) but i found this to be outrageously small. so small in fact that i could not zip it up! i reordered it in petite medium  which was just ok. overall  the top half was comfortable and fit nicely  but the bottom half had a very tight under layer and several somewhat cheap (net) over layers. imo  a major design flaw was the net over layer sewn directly into the zipper - it c,Dresses\n",
      "1,I love  love  love this jumpsuit. it's fun  flirty  and fabulous! every time i wear it  i get nothing but great compliments!,Pants\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 ./womens_clothing_ecommerce_reviews_transformed.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Register the public dataset for querying and visualizing\n",
    "Register the public dataset into an S3-backed database table so we can query and visualize our dataset at scale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-2.1.'></a>\n",
    "### 2.1. Register S3 dataset files as a table for querying\n",
    "Let's import required modules.\n",
    "\n",
    "`boto3` is the AWS SDK for Python to create, configure, and manage AWS services, such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3). The SDK provides an object-oriented API as well as low-level access to AWS services. \n",
    "\n",
    "`sagemaker` is the SageMaker Python SDK which provides several high-level abstractions for working with the Amazon SageMaker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S3 Bucket: sagemaker-us-east-1-028209366847\n",
      "Region: us-east-1\n",
      "Account ID: 028209366847\n"
     ]
    }
   ],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "sess   = sagemaker.Session()\n",
    "# S3 bucket name\n",
    "bucket = sess.default_bucket()\n",
    "# AWS region\n",
    "region = boto3.Session().region_name\n",
    "\n",
    "# Account ID \n",
    "sts = boto3.Session(region_name=region).client(service_name=\"sts\", region_name=region)\n",
    "account_id = sts.get_caller_identity()['Account']\n",
    "\n",
    "print('S3 Bucket: {}'.format(bucket))\n",
    "print('Region: {}'.format(region))\n",
    "print('Account ID: {}'.format(account_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy the file into the S3 bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upload: ./womens_clothing_ecommerce_reviews_transformed.csv to s3://sagemaker-us-east-1-028209366847/data/transformed/womens_clothing_ecommerce_reviews_transformed.csv\n"
     ]
    }
   ],
   "source": [
    "!aws s3 cp ./womens_clothing_ecommerce_reviews_transformed.csv s3://$bucket/data/transformed/womens_clothing_ecommerce_reviews_transformed.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import AWS Data Wrangler**\n",
    "\n",
    "[AWS Data Wrangler](https://github.com/awslabs/aws-data-wrangler) is an AWS Professional Service open source python initiative that extends the power of Pandas library to AWS connecting dataframes and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, Amazon QuickSight, etc).\n",
    "\n",
    "Built on top of other open-source projects like Pandas, Apache Arrow, Boto3, SQLAlchemy, Psycopg2 and PyMySQL, it offers abstracted functions to execute usual ETL tasks like load/unload data from data lakes, data warehouses and databases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review the AWS Data Wrangler documentation: https://aws-data-wrangler.readthedocs.io/en/stable/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import awswrangler as wr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create AWS Glue Catalog database**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data catalog features of **AWS Glue** and the inbuilt integration to Amazon S3 simplify the process of identifying data and deriving the schema definition out of the discovered data. Using AWS Glue crawlers within your data catalog, you can traverse your data stored in Amazon S3 and build out the metadata tables that are defined in your data catalog.\n",
    "\n",
    "Here you will use `wr.catalog.create_database` function to create a database with the name `dsoaws_deep_learning` (\"dsoaws\" stands for \"Data Science on AWS\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "wr.catalog.create_database(\n",
    "    name='dsoaws_deep_learning',\n",
    "    exist_ok=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database name: dsoaws_deep_learning\n"
     ]
    }
   ],
   "source": [
    "dbs = wr.catalog.get_databases()\n",
    "\n",
    "for db in dbs:\n",
    "    print(\"Database name: \" + db['Name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Register CSV data with AWS Glue Catalog**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "wr.catalog.create_csv_table(\n",
    "    ### BEGIN SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    database='dsoaws_deep_learning', # Replace None\n",
    "    ### END SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    path='s3://{}/data/transformed/'.format(bucket), \n",
    "    table=\"reviews\",    \n",
    "    columns_types={\n",
    "        'sentiment': 'int',        \n",
    "        'review_body': 'string',\n",
    "        'product_category': 'string'      \n",
    "    },\n",
    "    mode='overwrite',\n",
    "    skip_header_line_count=1,\n",
    "    sep=','\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review the table shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Column Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Partition</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sentiment</td>\n",
       "      <td>int</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>review_body</td>\n",
       "      <td>string</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>product_category</td>\n",
       "      <td>string</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Column Name    Type  Partition Comment\n",
       "0         sentiment     int      False        \n",
       "1       review_body  string      False        \n",
       "2  product_category  string      False        "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = wr.catalog.table(database='dsoaws_deep_learning',\n",
    "                         table='reviews')\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-2.2.'></a>\n",
    "### 2.2. Create default S3 bucket for Amazon Athena\n",
    "\n",
    "Amazon Athena requires this S3 bucket to store temporary query results and improve performance of subsequent queries.\n",
    "\n",
    "The contents of this bucket are mostly binary and human-unreadable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://aws-athena-query-results-028209366847-us-east-1/'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# S3 bucket name\n",
    "wr.athena.create_athena_bucket()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-3.'></a>\n",
    "# 3. Visualize data\n",
    "\n",
    "**Reviews dataset - column descriptions**\n",
    "\n",
    "- `sentiment`: The review's sentiment (-1, 0, 1).\n",
    "- `product_category`: Broad product category that can be used to group reviews (in this case digital videos).\n",
    "- `review_body`: The text of the review."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-3.1.'></a>\n",
    "### 3.1. Preparation for data visualization\n",
    "\n",
    "**Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Settings**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set AWS Glue database and table name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do not change the database and table names - they are used for grading purposes!\n",
    "database_name = 'dsoaws_deep_learning'\n",
    "table_name = 'reviews'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set seaborn parameters. You can review seaborn documentation following the [link](https://seaborn.pydata.org/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style = 'seaborn-whitegrid'\n",
    "\n",
    "sns.set(rc={\"font.style\":\"normal\",\n",
    "            \"axes.facecolor\":\"white\",\n",
    "            'grid.color': '.8',\n",
    "            'grid.linestyle': '-',\n",
    "            \"figure.facecolor\":\"white\",\n",
    "            \"figure.titlesize\":20,\n",
    "            \"text.color\":\"black\",\n",
    "            \"xtick.color\":\"black\",\n",
    "            \"ytick.color\":\"black\",\n",
    "            \"axes.labelcolor\":\"black\",\n",
    "            \"axes.grid\":True,\n",
    "            'axes.labelsize':10,\n",
    "            'xtick.labelsize':10,\n",
    "            'font.size':10,\n",
    "            'ytick.labelsize':10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Run SQL queries using Amazon Athena**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Amazon Athena** lets you query data in Amazon S3 using a standard SQL interface. It reflects the databases and tables in the AWS Glue Catalog. You can create interactive queries and perform any data manipulations required for further downstream processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. How many reviews per sentiment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT sentiment, COUNT(sentiment) AS count_sentiment\n",
      "FROM reviews\n",
      "GROUP BY sentiment\n",
      "ORDER BY sentiment\n",
      "\n"
     ]
    }
   ],
   "source": [
    "statement_count_by_sentiment = \"\"\"\n",
    "SELECT sentiment, COUNT(sentiment) AS count_sentiment\n",
    "FROM reviews\n",
    "GROUP BY sentiment\n",
    "ORDER BY sentiment\n",
    "\"\"\"\n",
    "\n",
    "print(statement_count_by_sentiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query data in Amazon Athena database cluster using the prepared SQL statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sentiment  count_sentiment\n",
      "0         -1             2370\n",
      "1          0             2823\n",
      "2          1            17433\n"
     ]
    }
   ],
   "source": [
    "df_count_by_sentiment = wr.athena.read_sql_query(\n",
    "    sql=statement_count_by_sentiment,\n",
    "    database=database_name\n",
    ")\n",
    "\n",
    "print(df_count_by_sentiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preview the results of the query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sentiment'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 264,
       "width": 390
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_count_by_sentiment.plot(kind='bar', x='sentiment', y='count_sentiment', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Total number of reviews per `product_category`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT product_category, COUNT(sentiment) AS count_sentiment\n",
      "FROM reviews\n",
      "GROUP BY product_category \n",
      "ORDER BY count_sentiment DESC\n",
      "\n"
     ]
    }
   ],
   "source": [
    "statement_count_by_category = \"\"\"\n",
    "SELECT product_category, COUNT(sentiment) AS count_sentiment\n",
    "FROM reviews\n",
    "GROUP BY product_category \n",
    "ORDER BY count_sentiment DESC\n",
    "\"\"\"\n",
    "\n",
    "print(statement_count_by_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query data in Amazon Athena database passing the prepared SQL statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 505 ms, sys: 26.7 ms, total: 531 ms\n",
      "Wall time: 3.29 s\n"
     ]
    },
    {
     "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>product_category</th>\n",
       "      <th>count_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dresses</td>\n",
       "      <td>6145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Knits</td>\n",
       "      <td>4626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blouses</td>\n",
       "      <td>2983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sweaters</td>\n",
       "      <td>1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pants</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jeans</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fine gauge</td>\n",
       "      <td>1059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Skirts</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jackets</td>\n",
       "      <td>683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lounge</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Swim</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Outerwear</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shorts</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sleep</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Legwear</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Intimates</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Layering</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Trend</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_category  count_sentiment\n",
       "0           Dresses             6145\n",
       "1             Knits             4626\n",
       "2           Blouses             2983\n",
       "3          Sweaters             1380\n",
       "4             Pants             1350\n",
       "5             Jeans             1104\n",
       "6        Fine gauge             1059\n",
       "7            Skirts              903\n",
       "8           Jackets              683\n",
       "9            Lounge              669\n",
       "10             Swim              332\n",
       "11        Outerwear              319\n",
       "12           Shorts              304\n",
       "13            Sleep              214\n",
       "14          Legwear              158\n",
       "15        Intimates              147\n",
       "16         Layering              132\n",
       "17            Trend              118"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df_count_by_category = wr.athena.read_sql_query(\n",
    "    sql=statement_count_by_category,\n",
    "    database=database_name\n",
    ")\n",
    "\n",
    "df_count_by_category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-3.3.'></a>\n",
    "### 3.3. Which product categories are highest rated by average sentiment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the SQL statement to find the average sentiment per product category, showing the results in the descending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT product_category, AVG(sentiment) AS avg_sentiment\n",
      "FROM reviews \n",
      "GROUP BY product_category \n",
      "ORDER BY avg_sentiment DESC\n",
      "\n"
     ]
    }
   ],
   "source": [
    "statement_avg_by_category = \"\"\"\n",
    "SELECT product_category, AVG(sentiment) AS avg_sentiment\n",
    "FROM {} \n",
    "GROUP BY product_category \n",
    "ORDER BY avg_sentiment DESC\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "print(statement_avg_by_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query data in Amazon Athena database passing the prepared SQL statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 571 ms, sys: 37.7 ms, total: 609 ms\n",
      "Wall time: 4.57 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_avg_by_category = wr.athena.read_sql_query(\n",
    "    sql=statement_avg_by_category,\n",
    "    database=database_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preview the results of the query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>product_category</th>\n",
       "      <th>avg_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Layering</td>\n",
       "      <td>0.780303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jeans</td>\n",
       "      <td>0.746377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lounge</td>\n",
       "      <td>0.745889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sleep</td>\n",
       "      <td>0.710280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shorts</td>\n",
       "      <td>0.707237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pants</td>\n",
       "      <td>0.705185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Intimates</td>\n",
       "      <td>0.700680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jackets</td>\n",
       "      <td>0.699854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Skirts</td>\n",
       "      <td>0.696567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Legwear</td>\n",
       "      <td>0.696203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Fine gauge</td>\n",
       "      <td>0.692162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Outerwear</td>\n",
       "      <td>0.683386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Knits</td>\n",
       "      <td>0.653913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Swim</td>\n",
       "      <td>0.644578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Dresses</td>\n",
       "      <td>0.643287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sweaters</td>\n",
       "      <td>0.641304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Blouses</td>\n",
       "      <td>0.641301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Trend</td>\n",
       "      <td>0.483051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_category  avg_sentiment\n",
       "0          Layering       0.780303\n",
       "1             Jeans       0.746377\n",
       "2            Lounge       0.745889\n",
       "3             Sleep       0.710280\n",
       "4            Shorts       0.707237\n",
       "5             Pants       0.705185\n",
       "6         Intimates       0.700680\n",
       "7           Jackets       0.699854\n",
       "8            Skirts       0.696567\n",
       "9           Legwear       0.696203\n",
       "10       Fine gauge       0.692162\n",
       "11        Outerwear       0.683386\n",
       "12            Knits       0.653913\n",
       "13             Swim       0.644578\n",
       "14          Dresses       0.643287\n",
       "15         Sweaters       0.641304\n",
       "16          Blouses       0.641301\n",
       "17            Trend       0.483051"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_avg_by_category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_values_barplot(axs, space):\n",
    "    def _show_on_plot(ax):\n",
    "        for p in ax.patches:\n",
    "            _x = p.get_x() + p.get_width() + float(space)\n",
    "            _y = p.get_y() + p.get_height()\n",
    "            value = round(float(p.get_width()),2)\n",
    "            ax.text(_x, _y, value, ha=\"left\")\n",
    "\n",
    "    if isinstance(axs, np.ndarray):\n",
    "        for idx, ax in np.ndenumerate(axs):\n",
    "            _show_on_plot(ax)\n",
    "    else:\n",
    "        _show_on_plot(axs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 274,
       "width": 415
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plot\n",
    "barplot = sns.barplot(\n",
    "    data = df_avg_by_category, \n",
    "    y='product_category',\n",
    "    x='avg_sentiment', \n",
    "    color=\"b\", \n",
    "    saturation=1\n",
    ")\n",
    "\n",
    "# Set the size of the figure\n",
    "sns.set(rc={'figure.figsize':(15.0, 10.0)})\n",
    "    \n",
    "# Set title and x-axis ticks \n",
    "plt.title('Average sentiment by product category')\n",
    "#plt.xticks([-1, 0, 1], ['Negative', 'Neutral', 'Positive'])\n",
    "\n",
    "# Helper code to show actual values afters bars \n",
    "show_values_barplot(barplot, 0.1)\n",
    "\n",
    "plt.xlabel(\"Average sentiment\")\n",
    "plt.ylabel(\"Product category\")\n",
    "\n",
    "plt.tight_layout()\n",
    "# Do not change the figure name - it is used for grading purposes!\n",
    "plt.savefig('avg_sentiment_per_category.png', dpi=300)\n",
    "\n",
    "# Show graphic\n",
    "plt.show(barplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-east-1-028209366847/images/avg_sentiment_per_category.png'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upload image to S3 bucket\n",
    "sess.upload_data(path='avg_sentiment_per_category.png', bucket=bucket, key_prefix=\"images\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-3.4.'></a>\n",
    "### 3.4. Which product categories have the most reviews?\n",
    "\n",
    "Set the SQL statement to find the count of sentiment per product category, showing the results in the descending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT product_category, COUNT(sentiment) AS count_reviews \n",
      "FROM reviews\n",
      "GROUP BY product_category \n",
      "ORDER BY count_reviews DESC\n",
      "\n"
     ]
    }
   ],
   "source": [
    "statement_count_by_category_desc = \"\"\"\n",
    "SELECT product_category, COUNT(sentiment) AS count_reviews \n",
    "FROM {}\n",
    "GROUP BY product_category \n",
    "ORDER BY count_reviews DESC\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "print(statement_count_by_category_desc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query data in Amazon Athena database passing the prepared SQL statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 468 ms, sys: 12 ms, total: 480 ms\n",
      "Wall time: 3.01 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_count_by_category_desc = wr.athena.read_sql_query(\n",
    "    sql=statement_count_by_category_desc,\n",
    "    database=database_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store maximum number of sentiment for the visualization plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest number of reviews (in a single category): 6145\n"
     ]
    }
   ],
   "source": [
    "max_sentiment = df_count_by_category_desc['count_reviews'].max()\n",
    "print('Highest number of reviews (in a single category): {}'.format(max_sentiment))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualization**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `barplot` function to plot number of reviews per product category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 706,
       "width": 1067
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create seaborn barplot\n",
    "barplot = sns.barplot(\n",
    "    ### BEGIN SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    data=df_count_by_category_desc, # Replace None\n",
    "    y='product_category', # Replace None\n",
    "    x='count_reviews', # Replace None\n",
    "    ### END SOLUTION - DO NOT delete this comment for grading purposes\n",
    "    color=\"b\",\n",
    "    saturation=1\n",
    ")\n",
    "\n",
    "# Set the size of the figure\n",
    "sns.set(rc={'figure.figsize':(15.0, 10.0)})\n",
    "    \n",
    "# Set title\n",
    "plt.title(\"Number of reviews per product category\")\n",
    "plt.xlabel(\"Number of reviews\")\n",
    "plt.ylabel(\"Product category\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Do not change the figure name - it is used for grading purposes!\n",
    "plt.savefig('num_reviews_per_category.png', dpi=300)\n",
    "\n",
    "# Show the barplot\n",
    "plt.show(barplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-3.5.'></a>\n",
    "### 3.5. What is the breakdown of sentiments per product category?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the SQL statement to find the count of sentiment per product category and sentiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SELECT product_category,\n",
      "         sentiment,\n",
      "         COUNT(sentiment) AS count_reviews\n",
      "FROM reviews\n",
      "GROUP BY  product_category, sentiment\n",
      "ORDER BY  product_category ASC, sentiment DESC, count_reviews\n",
      "\n"
     ]
    }
   ],
   "source": [
    "statement_count_by_category_and_sentiment = \"\"\"\n",
    "SELECT product_category,\n",
    "         sentiment,\n",
    "         COUNT(sentiment) AS count_reviews\n",
    "FROM {}\n",
    "GROUP BY  product_category, sentiment\n",
    "ORDER BY  product_category ASC, sentiment DESC, count_reviews\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "print(statement_count_by_category_and_sentiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query data in Amazon Athena database passing the prepared SQL statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 431 ms, sys: 18.1 ms, total: 449 ms\n",
      "Wall time: 4.29 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_count_by_category_and_sentiment = wr.athena.read_sql_query(\n",
    "    sql=statement_count_by_category_and_sentiment,\n",
    "    database=database_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare for stacked percentage horizontal bar plot showing proportion of sentiments per product category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of reviews: 22626\n"
     ]
    }
   ],
   "source": [
    "# Create grouped dataframes by category and by sentiment\n",
    "grouped_category = df_count_by_category_and_sentiment.groupby('product_category')\n",
    "grouped_star = df_count_by_category_and_sentiment.groupby('sentiment')\n",
    "\n",
    "# Create sum of sentiments per star sentiment\n",
    "df_sum = df_count_by_category_and_sentiment.groupby(['sentiment']).sum()\n",
    "\n",
    "# Calculate total number of sentiments\n",
    "total = df_sum['count_reviews'].sum()\n",
    "print('Total number of reviews: {}'.format(total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create dictionary of product categories and array of star rating distribution per category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "distribution = {}\n",
    "count_reviews_per_star = []\n",
    "i=0\n",
    "\n",
    "for category, sentiments in grouped_category:\n",
    "    count_reviews_per_star = []\n",
    "    for star in sentiments['sentiment']:\n",
    "        count_reviews_per_star.append(sentiments.at[i, 'count_reviews'])\n",
    "        i=i+1;\n",
    "    distribution[category] = count_reviews_per_star"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build array per star across all categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Blouses': [2256, 384, 343],\n",
       " 'Dresses': [4634, 830, 681],\n",
       " 'Fine gauge': [837, 118, 104],\n",
       " 'Intimates': [117, 16, 14],\n",
       " 'Jackets': [550, 61, 72],\n",
       " 'Jeans': [909, 110, 85],\n",
       " 'Knits': [3523, 605, 498],\n",
       " 'Layering': [113, 9, 10],\n",
       " 'Legwear': [126, 16, 16],\n",
       " 'Lounge': [545, 78, 46],\n",
       " 'Outerwear': [254, 29, 36],\n",
       " 'Pants': [1074, 154, 122],\n",
       " 'Shorts': [240, 39, 25],\n",
       " 'Skirts': [714, 104, 85],\n",
       " 'Sleep': [175, 16, 23],\n",
       " 'Sweaters': [1036, 193, 151],\n",
       " 'Swim': [252, 42, 38],\n",
       " 'Trend': [78, 19, 21]}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blouses</th>\n",
       "      <td>75.628562</td>\n",
       "      <td>12.872947</td>\n",
       "      <td>11.498491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dresses</th>\n",
       "      <td>75.410903</td>\n",
       "      <td>13.506916</td>\n",
       "      <td>11.082181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine gauge</th>\n",
       "      <td>79.036827</td>\n",
       "      <td>11.142587</td>\n",
       "      <td>9.820585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intimates</th>\n",
       "      <td>79.591837</td>\n",
       "      <td>10.884354</td>\n",
       "      <td>9.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jackets</th>\n",
       "      <td>80.527086</td>\n",
       "      <td>8.931186</td>\n",
       "      <td>10.541728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeans</th>\n",
       "      <td>82.336957</td>\n",
       "      <td>9.963768</td>\n",
       "      <td>7.699275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knits</th>\n",
       "      <td>76.156507</td>\n",
       "      <td>13.078253</td>\n",
       "      <td>10.765240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>85.606061</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>7.575758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legwear</th>\n",
       "      <td>79.746835</td>\n",
       "      <td>10.126582</td>\n",
       "      <td>10.126582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>81.464873</td>\n",
       "      <td>11.659193</td>\n",
       "      <td>6.875934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>79.623824</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>11.285266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>79.555556</td>\n",
       "      <td>11.407407</td>\n",
       "      <td>9.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>78.947368</td>\n",
       "      <td>12.828947</td>\n",
       "      <td>8.223684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>79.069767</td>\n",
       "      <td>11.517165</td>\n",
       "      <td>9.413068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>81.775701</td>\n",
       "      <td>7.476636</td>\n",
       "      <td>10.747664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>75.072464</td>\n",
       "      <td>13.985507</td>\n",
       "      <td>10.942029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>75.903614</td>\n",
       "      <td>12.650602</td>\n",
       "      <td>11.445783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>66.101695</td>\n",
       "      <td>16.101695</td>\n",
       "      <td>17.796610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    1          0         -1\n",
       "Blouses     75.628562  12.872947  11.498491\n",
       "Dresses     75.410903  13.506916  11.082181\n",
       "Fine gauge  79.036827  11.142587   9.820585\n",
       "Intimates   79.591837  10.884354   9.523810\n",
       "Jackets     80.527086   8.931186  10.541728\n",
       "Jeans       82.336957   9.963768   7.699275\n",
       "Knits       76.156507  13.078253  10.765240\n",
       "Layering    85.606061   6.818182   7.575758\n",
       "Legwear     79.746835  10.126582  10.126582\n",
       "Lounge      81.464873  11.659193   6.875934\n",
       "Outerwear   79.623824   9.090909  11.285266\n",
       "Pants       79.555556  11.407407   9.037037\n",
       "Shorts      78.947368  12.828947   8.223684\n",
       "Skirts      79.069767  11.517165   9.413068\n",
       "Sleep       81.775701   7.476636  10.747664\n",
       "Sweaters    75.072464  13.985507  10.942029\n",
       "Swim        75.903614  12.650602  11.445783\n",
       "Trend       66.101695  16.101695  17.796610"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_distribution_pct = pd.DataFrame(distribution).transpose().apply(\n",
    "    lambda num_sentiments: num_sentiments/sum(num_sentiments)*100, axis=1\n",
    ")\n",
    "df_distribution_pct.columns=['1', '0', '-1']\n",
    "df_distribution_pct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualization**\n",
    "\n",
    "Plot the distributions of sentiments per product category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Rs2bNUo4cOfTEE0+oYsWKcnR01MmTJ1ONJS1DhgwxEiycnZ2Nz7Wrq6tiY2N1/vx5HThwwEiISc3MmTM1fPhwIxHC399fAQEB8vHxUVxcnA4cOKANGzYoLi5Of/31l65du6YxY8akG9+9jF+tW7dWmTJltGnTJuPma+vWra3OpHEvY0l0dLQGDBigU6dOyc/PT40aNTLGu2XLlunatWu6cOGCBgwYoEWLFunTTz/V3Llz5erqqsDAQPn6+io2Nlbr1q0zzvN3332n6tWrq1atWlb7vH37tvr162eMJ05OTqpXr57Kly8vV1dXRUZGavPmzTp8+LCioqL02muv6aeffko30eLGjRvq37+/jh07pqJFi6px48by8fHR1atXtWrVKp05c0Zms1lffPGFKlasmGIMSBq7J0yYoGvXrllsS87aLEYZFR4ertdee03Xrl2Tr6+vmjRpInd3d4WHh+vff/9VZGSkrl+/rrffflsTJkxI8fsjSVaN73fLjmujW7du6YUXXtB///1nbPP19VW9evXk4+OjO3fuKDw8XP/9959OnTqVIvlGSkykSEqwKFOmjCpUqKDChQvLxcVFMTExOnLkiDZt2qRbt25p37596tevn+bMmaPcuXNbtHMv14979uxR3759jTi8vb3VoEEDFS1aVA4ODjpz5ozWrFmjq1evaufOnerevbvmzp1r9XMTHx+vV155xWKmsdq1a6tGjRpycnLSsWPHtGbNGs2bNy9FMl9qsut6bOTIkVqwYIFy5sxpjGcmk0lHjhyRs7OzXFxc1LZtW82aNUu3b9/WP//8k24yU9L/R0jS008/bfOsHwAAAADuYgYAAMB91bRpU7O/v7/Z39/fvHXr1gztm5CQYK5Zs6ax/08//ZSizty5c43ysWPHpij/+++/jfL+/fubY2JiUu3v4MGD5oULF1ot69mzp9HO2bNn0409eVz+/v7mcuXKmadNm2ZOSEhIc7+k+k2bNrVavnXrVqNO2bJlzf7+/uaPP/7YHBsba1EvNjbW/PHHHxt1q1evbg4PD0/R3tmzZ406PXv2zNBxWTvfZrPZPHjw4Az9zG2J4dChQ+aKFSsa9UaMGJHimG/fvm3+/PPPjToVK1Y079+/32p7yT+X/v7+5ueee858/vz5FPWWLl1qnOdy5cpZrZMR69evt+h3woQJ5vj4eIs6169fN7/55ptGnVq1aqXary0/j4xK/vPz9/c3165d2xwcHJzmPi+//LJR/9133zVfvXo1RZ34+HjzxIkTjXrNmzc3x8XFWdSJjo42V65c2ezv72+uUqWKOTo6Os1+hw8fbrQ3ceLEFOXJvy+DBw+22saZM2fMNWrUMPv7+5urVq1qDgoKsvo9jYiIsBgHxowZk6LO7Nmz0xyvkn/Wn3zySbO/v7+5fPny5qioqBR1k3+WV65caVGWVeOarZIfd9myZc116tQx79q1K0W93bt3m+vUqWPU/eyzz6y2l5Xn/O74ksbPw4cP39MxJ8WZ1GZgYKA5NDQ01boXLlwwT5s2zWrZzp07jfGrbt265rVr11qtd/r0aXPbtm2NPufMmWO1XlaPX2PHjjXamjt3bqrHmFxGflclfW4mTZqUYry7cOGCxfG8++67Zn9/f3Pv3r3Nly9ftqibkJBg8Xutb9++qcY3YsQIi3qpHfv8+fONn01AQID5ypUr6R6Lv7+/edy4cSnGr9u3b5sHDhxoU3zJjzkrJf+dkPSzHzt2bIrzHhUVZTFuN2rUKNXxNqvG97vjy8i1ka2GDBlicd2zePHiVOseOHDAPHXq1BTbr1y5Yv7uu+/S/L5funTJ3LdvX4vPQ2oyev149epVc5MmTYzfD5MmTbJ6LqOjoy2uFQYNGmS1vcmTJxt1qlatanX8OXPmjPmpp54yPjNpfbez+3qsU6dO5rCwsFTPz/79+4267du3T7We2Zx4LZz0O6l8+fL3fA0HAAAAPM6sL5QIAACAB5LJZLJYvzw8PDzDbSR/unjgwIEpnjRMrly5cmrbtm2G+7BF//791atXryx9gs5sNqtOnToaMWJEimVUnJ2dNWLECNWuXVtS4tO3U6ZMybK+77cJEyYYT7w3bdpUn3zySYpjdnJy0kcffWRMex4XF6fx48en23bBggX166+/Wl2XvlWrVnrqqackSQkJCfr333/v6TjGjRtnvO7Vq5f69++fYj33PHny6Ntvv1XlypUlJS798Pvvv99Tv/fi+++/Nz5H1mzZskVr1qyRJLVo0ULffPON3N3dU9RzcHDQiy++qD59+khKXA5i8eLFFnVcXV2N5VFu3bqlFStWpNpvXFycMYuHg4OD2rVrl7ED+39jx47V9evXJSUug9GxY0er31MvLy+NHz9ehQoVkiRNnz49xTTpyWecsDZDRfJtr776qqTEp4yTP2F8d10HB4cUM/jYc1wzm80aM2aMqlatmqKsSpUq+uGHH4z3s2bNSrFMgZS15/xuTk5O+vXXX+Xv75+Rw7LqwIEDxusePXqoSJEiqdb18fFRr169rJZ9/fXXiouLk6Ojo3799ddUZ2gpXry4Jk2aZMy4MWHChHRjvJ/j173o06eP+vXrl2K88/Hx0VtvvWW8X7hwoYoUKaKff/45xVP1JpNJH3zwgVxdXSVJmzdvNj5HyZ06dUozZsyQlPiZHD9+vNXzI0kdOnTQ+++/Lylxxoak/dLSo0cPvfbaa3J0tJws1cnJSSNGjFCuXLnSjO9+MZvN6ty5swYOHJjivOfNm1djx45VyZIlJSUudTJv3rwUbWTl+G5NVl4bHTp0SEFBQZKkHDly6Ndff1Xr1q1TrV++fHmrsyB4eHjonXfeSfP77unpqR9//FE+Pj6SpNmzZ99j9P/z+++/G9e7gwYNUr9+/VJ81qTE35fffvutMWPIwoULUywfFRcXp0mTJhnvhw0bZnX8KVasmCZMmGDTknzZeT3m4eGhiRMnqnDhwqnWqVChgipWrCgp8WeeNHOaNatXr9aVK1ckSQ0bNkx1HAAAAACQPpIsAAAAHjLJ/5ifNLV2RiRfj/vWrVtZElNG5cqVK9214zPrrbfeSvXmhMlksrh5tWDBAmOq+odJTEyMxc32pBtiqXnvvfeM16tWrVJUVFSa9V944YU0p+9OvlRH8puuGXXy5Ent2rVLkpQzZ04NHDgw1bqOjo565513jPfz58+3y8+uRo0aqU4hn+SPP/4wXr/77rvp3izr16+f8Xr16tUpyjt06GC8Tm1ddklav369cfOkXr16mbp5cuXKFSNRo3r16mrevHma9V1dXdW1a1dJickv27dvtygvXry4kRCwa9cu3b5926I8aamQggULqm3btsbN4ruXEImMjDSm5i9fvnyKm5r2HNeeeOKJFMuXJFe3bl3Vr19fUuKNtbtvtGb1Ob9bq1atVLp06XSPwxbJz/PNmzcz1cahQ4e0Y8cOI7ak5KnUFChQwEiKOX36tI4dO5Zm/fs1ft0LJycnvfzyy6mWN2nSxOJ93759lSdPHqt1XVxcjM9fQkKCDh8+nKLOjBkzjGUg3nrrrXRvHHfr1s1IbLE2JiXn5OSk1157LdVyDw8P1a1b14jv0KFDabaXnRwcHPTmm2+mWu7s7GxxLPPnz09RJ6vH9+Sy+tpo5syZxu/JTp06pbqUTFZxcXFR06ZNJSUmqSQtCXIvEhISNHPmTEmJvyfSWwrD0dHRWIYjPj5ea9eutSjfvHmzLl26JCnx91PHjh1TbatYsWLpLiuX3ddjXbt2laenZ5p1JKlLly7G67lz56ZaL3nZs88+m267AAAAAFJHkgUAAMBDJunGh6RMPRFarlw54/XIkSN16tSprAgrQ2rVqmXcTM1KXl5eqlGjRpp1atasKS8vL0mJNzdPnjyZ5XFktz179hhPTZYpU0alSpVKs37JkiVVoUIFSYk3HZISG1LTqFGjdNtLcvny5fQDTkXyNeLr1atn9Wng5OrVqycPDw9JiT+7EydOZLrvzLr75ufdEhISjFkYihYtKl9f33TbLFCggPGE+t69e1OUN2jQwLjJEhISogsXLlht559//jFet2/fPt1+rdm2bZvu3Llj9GuL8uXLG6+txZ90Azg2NtbiZy79L5miTp06ypEjh3ET8O5ZL0JCQoybhdYSGuw5rqWXFCHJeHpZknbu3GlRlh3nPLn0PrMZkXwmpSlTpmjz5s0ZbmPLli3G64YNG9q0T/Kfb3rHe7/Gr3tRqVKlFLNSJOfm5maMdZLSTewqXry48TrpBnJySefcyckpzYSgJM7OzsbvlQMHDlgk19ytYsWK6d4ETn7Orc3kcr9UqlRJBQsWTLNOs2bNjFkuDh06pJiYGKMsO8b35LL62ij5ONqpU6csadNsNuvEiRNavny5/vrrL/3+++/67bffjH+hoaFG3ay4vjp48KCuXr0qSapfv36KGUisST4+3j2rQ/Lrn8DAwHSTZJJmkkpNdl+P2Tp+t23b1vj/g0WLFik2NjZFnQsXLmjjxo2SEq+Xs/J3AwAAAPA4Sjm/HgAAAB5oyaeGz8wf49u2bavx48fr/PnzOnDggFq1aqUqVaqoXr16qlatmqpVq6Z8+fJlZcgp2HJjIjOS34hLi7+/v3Ej6uTJk/Lz88uWeLLL6dOnjdfJbyakpUKFCsZT28n3tyatKcElWTxRnfwGVEZl9DhMJpPKly9v3DA8ffp0ujc0slp6n5WwsDDjydTQ0FCLm9K2SJqJIjlHR0e1adNG06ZNU0JCgv75558UTztHR0cbU9i7uLjoySefzFC/SZI/Zf7jjz/qxx9/zND+1m6g1qlTx3giPDg42Hiq/dSpU0bCSNK2unXrau3atTpy5IiuXLlijEXJZ7awdpPYnuOaLZ/d5J+Du288Zsc5Ty4rx7dSpUqpWbNmxhPYL7zwgkqUKKEGDRqoRo0aqlq1qooVK5ZmG8mPd/DgwRo8eHCGYkjveO/X+HUvkmZ3SYuLi4txczmtpQKS6ia5e4aR+Ph4Y/aPuLg4Y1kBW8XHx+vatWupJoWkF5v0YJxzybbvap48eVSsWDGdPn1a8fHxOnPmjHFtkR3je3JZeW0UFxdnJJuZTKZ0Z4xJT2xsrH7//Xf99ddfNi9VFx0dfU99Spbjxbx586wu4ZKWu8eL5NcdtiyhlF6d7L4es3X8dnV1VevWrTVnzhxFRUVpxYoVKZYMmzdvnjGjTceOHa0uuQIAAADAdsxkAQAA8JBJvkRI8iddbeXq6qrff//duNFiNpu1e/du/fLLLxowYIDq1aunzp07a/Lkydm2dnrevHmzpV1bz0fym63pTdX8IEr+GbD1xnHyeuktM5MzZ840y5M/+XkvS3Zk93Fkh/QSm5JuimZWajcgky8ZknzGiiTLli0znlxt0aKFxU3XjLjX+K0tIZE8KSL5k9XWEieS/ms2my3Kk14nn+0iOXuOa7aMO2l9brPjnCeX1ePt119/rTZt2hjvT58+rT///FPvvvuumjdvrsDAQH311VcKCwuzun92H+/9Gr/uRXoxSpZxZuSYkm6iJrl27VqKbRmV1jnP6LHcayz34l6vEbJrfE+Sld/V5OOMq6urTT+n1Fy9elXdunXT6NGjbU6wkGR1NoXM9H0v7j7nGb2GTu/aJLuvYzLymUi+ZMicOXMsysxms8VSIc8884zN7QIAAACwjrRlAACAh8jd663b8gSpNX5+fpo7d642b96sFStWaPv27Tp+/LjMZrPMZrP279+v/fv3a/Lkyfrhhx+yfB1vW6Z7Bh5E6X12k0+rX6BAAfXp0ydL+q1UqZJKly6tY8eO6ciRIzp06JDFzC3JEy+SJ2RkVNKyFVLiMhjpLb9ztzJlyqTYVqRIERUrVkxnz57V3r17FRMTIxcXFyPholixYsbsA+XKlZOHh4euXr2q4OBgtWrVShERETp+/LikxKUJUkt0sfe4llnZcc6TS286/IxydXXV999/rwEDBmjRokUKCQnRvn37jCnzw8LCNHnyZE2fPl0ffPCBevToYbF/8uPt3LmzSpcunaH+q1evfu8H8RhJPiblzp1bAwcOzHAb6S3l9LjIrvE9yYN6bfT5559r//79kqT8+fOre/fuqlu3rooVKyYPDw/lzJnTGGfGjh2rn376Kcv6Tn7OAwIC1LRp0wztb8usMQ+yjHwmqlatqrJly+rw4cMKDg7W2bNnjZmFkt5LicvSZNeMcgAAAMDjhCQLAACAh8jBgwctnsIOCAjIdFsmk0lPPPGEsdb7lStXtGPHDq1bt05LlizR9evXFRERoVdeeUXLly9Pc/34B4WtTzwmn7Lbzc3NoiyjTzmn91R1dkh+wyszx/yg3DB7VI4jueRPxubKlUv9+vXLsrY7dOig7777TpK0YMECiynst23bJinxxl+9evUy3UfyJ2wrVaqUZfHXqVNHZ8+eVVxcnHbs2KGGDRsqJCTEKEvi4OCggIAArVy50kjCSD6jRdKyIqmxx7hmy2c3rc9tdp3z7Obv76933nlHknTr1i3t2bNHmzZt0j///KPw8HDFxcVpxIgRKlmypPHzkCy/I/Xq1VP79u3vd+iPleTn+86dOw/N5ys73Os1QnaO71kteazXr1/X7du35ezsnOF2IiMjtXjxYkmJsyrMmTMnzSV5smKJkOSSH0fJkiXv+Zwn/3lmdOy25kG7junSpYs+++wzmc1mzZs3T2+++aYkMYsFAAAAkA0ezDR5AAAAWDVjxgzjtaurq6pUqZJlbefLl0/NmzfXZ599puXLlxtPv0VFRVldnuBBlHzt7rQcOXLEeH3303zJl1mwZe34c+fO2Rhd1ilRooTx+uDBgzbtk7xeyZIlszqkTMnocZjNZouf8YNyHMkVLlxYuXPnliSFhoZm6dIU7dq1M55qXbhwoTHt/sKFC42EoLZt297T09DJ13+39ftki+SJFMHBwTp27JguXbokKWXiRFLdEydOKCIiwuqyIra6H+OaLecp+QxEd4852XXO76dcuXKpdu3aevvtt7VixQq1aNHCKPvzzz8t6pYqVcp4/bAe78PEyclJxYsXlyTFxcXp2LFjdo7Ifmz5vN24ccN44j9HjhzGuZOyd3zPao6OjsbvSLPZrD179mSqnb179xq/a5588sk0Eywk6ejRo5nqJzVZPT4mv+5Ifi2YmvTqPGjXY+3bt1euXLkkSUFBQUpISFB0dLRWrFghKfH/HVq1apWlfQIAAACPK5IsAAAAHhJ79uzRggULjPc9e/bM1FOJtvDy8rJY2/nkyZMp6jg6/m9SNHuusZ7cpUuXtHPnzjTr/Pfff8bNXQ8PjxQ3PN3c3Izzevr06XSPbePGjenGldXnqkqVKnJycpKUeAPgxIkTadY/c+aMDhw4ICnxplHVqlXvOYaskHxZhM2bN6f7BOzWrVuNJ0Dz5cv3QE537ezsbCxDER8fr6VLl2ZZ24UKFVLt2rUlSREREdqyZYukrFsqREqcWSBpNpcNGzaku168rZInR2zdujXNxInU6jo5OWV4KY3kbBnXMuPff//NUJ27l7vIrnNuL05OTnrppZeM93ef5+SzWixbtsxi+ZAHRfIxO/lyBQ+r+vXrG68XLVpkx0hSdz/O+d69e3X+/Pk066xevdr4PV2uXDmLxMvsHN+zQ/JZjYKCgjLVRvLxKL1ZFy5duqQdO3ak22ZGromqVq2qPHnySJL2799/z+N28vF39erV6dZftWpVmuUP2vWYm5ubkURx7tw5bdq0SYsWLdKtW7ckSW3atDEShQAAAADcG5IsAAAAHgIHDx7U66+/bqx5ny9fPr3wwgv3rf+kp+KSy5s3r/Ha1imS74cxY8akusyH2WzW2LFjjfcdOnSwWB5ESvyjd9myZSUlTrGddCPbmhUrVtj05GJWnysXFxc9+eSTxvvvv/8+zfrffvutcU6aN2+eYokUe/H19VW1atUkSbGxsfrxxx9TrRsfH6/Ro0cb7zt16pTiZ/eg6NWrl/F67Nixunjxos37prdETfIkigULFmjfvn06fvy4JKl8+fLGZzezfHx81LJlS0mJT3SPGjXK5n3Tir1AgQLGE7sHDhwwkg78/Pzk4+NjUdff31+enp6SpPnz5+v06dOSpMqVK1vc8LxX1sa1zNi4caOxXIs127Zt06ZNmyQlJiC0bt3aojy7zvmD4u7zXLVqVVWuXFlS4lI3v/zyi81t3a/jTT5mP+xJL5LUo0cPY7ycOnVqhmYbuF/n3NXV1XidXec8ISHB4hrgbrdv39bPP/9svLeWtJad43tW69atm/FzDwoK0vbt2zPcRvLlOvbv359m3W+//Va3b99Ot82MXBM5OTmpa9eukhJ/fiNGjMhQEs7d57x+/fry8vKSlJjwMH/+/FT3PXv2bLrJKQ/i9dizzz5rvJ4zZ47mzJljtQwAAADAvSHJAgAA4AEWHh6u7777Tl26dNGFCxckSTlz5tT48eMt/vCdEW+//bbGjRtnTIdtzbFjxzR9+nTjfUBAQIo6yWcRSP5Uuj2ZTCZt2bJFw4YNS/GH/tu3b+vTTz81kiZcXFzUp08fq+0k/4P5559/bsx8kdzatWs1ZMgQm+LKjnP18ssvG09Prly5UiNHjkxxzHFxcRo1apSWL18uKfFmxSuvvJIl/WeV119/3Xg9depUTZo0KcVNkZiYGA0aNEi7d++WlPik5vPPP38/w8yQxo0bq3HjxpKkixcvqnv37mne3IqLi9P69es1YMAArVy5Ms22n3zySeMp1JUrV+qvv/4yyu51Fosk77zzjnHDc968eXrvvfd0+fLlVOtfvnxZf/zxhzp27Jhmu0kzVMTHx2vz5s2SUi4VkiRpxo7kM8WktVRIVo1rmWEymfTmm29anY5/7969evPNN433Xbt2NRJIksuuc57Vfv31V33yySdpLj0QFRWl7777znif9OR/ch988IExfv34448aNWpUmksvhIeHa8KECerXr989RG+7B/H3273w9/dXt27dJCWOp717905zBpaEhARt375d7733nqZNm3ZfYkx+zrdu3ZotfZhMJs2dO1c///xzihkUrl+/rrfeesuYiaBAgQJ6+umnU7SRneN7Vitbtqw6deokKXHcffnll9OcfePAgQMpft7VqlUzvqvBwcEWy9YliY2N1eeff66goCCbkh8z+v166aWXVKhQIUmJs1699NJLCg0NTbV+dHS0goKC1LFjR2P2qyROTk7q27ev8f7TTz/V+vXrU7Rx9uxZDRgwQLGxsenG96Bdj9WqVctYlunff//Vvn37JCV+HpIS3AAAAADcO8f0qwAAACC7LFmyxPjjp5T4R9jo6GhdunRJe/fu1YkTJyxuOBcpUkRffvlliunmM+LSpUtasmSJfvzxR5UpU0aVKlVSoUKFlCtXLl25ckWHDh1ScHCwcQOievXqatq0aYp2GjdubDyBPGbMGJ04cUJlypSxWMKkZ8+emY4zM5o1a6ZDhw7pr7/+0oYNG9SsWTN5eXnp0qVLWrVqlcLDw426H374Yapri3ft2lXTpk1TRESETpw4odatW6tVq1YqUqSIoqOjtW3bNu3atUsuLi56/vnnNWXKlDTjatiwoRwcHJSQkKBZs2bpypUrqlSpksWUzR07drR4kjc9ZcuW1ZAhQ/TZZ59JkqZNm6ZVq1apadOm8vb21qVLl7RmzRqLGxHvvfeeypcvb3Mf90PDhg3Vt29fTZ48WZL0zTffaN68eWrUqJE8PDwUHh6u1atXKyIiQlLiTCOff/65ChQoYM+w0/Xdd9+pV69eOnjwoM6ePasePXqoXLlyqlWrlry8vGQ2m3Xt2jUdO3ZMe/bsUVRUlCSle9Pc1dVVzZo106JFixQTE6O///5bUuJ5adu2bZbEXqJECY0ePVoDBw7UrVu3tHDhQq1YsUJ16tRR+fLl5erqqps3byoiIkIHDhzQwYMHlZCQkO4sE3Xr1rVICknaZk2dOnVS3AxMK8kiq8a1zHj++ef1+++/q2vXrmrYsKEqVaokKfGp7/X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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 708,
       "width": 1068
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = df_distribution_pct.index\n",
    "\n",
    "# Plot bars\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "df_distribution_pct.plot(kind=\"barh\", \n",
    "                         stacked=True, \n",
    "                         edgecolor='white',\n",
    "                         width=1.0,\n",
    "                         color=['green', \n",
    "                                'orange', \n",
    "                                'blue'])\n",
    "\n",
    "plt.title(\"Distribution of reviews per sentiment per category\", \n",
    "          fontsize='16')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.04,1), \n",
    "           loc=\"upper left\",\n",
    "           labels=['Positive', \n",
    "                   'Neutral', \n",
    "                   'Negative'])\n",
    "\n",
    "plt.xlabel(\"% Breakdown of sentiments\", fontsize='14')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Do not change the figure name - it is used for grading purposes!\n",
    "plt.savefig('distribution_sentiment_per_category.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-east-1-028209366847/images/distribution_sentiment_per_category.png'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upload image to S3 bucket\n",
    "sess.upload_data(path='distribution_sentiment_per_category.png', bucket=bucket, key_prefix=\"images\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='c1w1-3.6.'></a>\n",
    "### 3.6. Analyze the distribution of review word counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the SQL statement to count the number of the words in each of the reviews:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    SELECT CARDINALITY(SPLIT(review_body, ' ')) as num_words\n",
      "    FROM reviews\n",
      "\n"
     ]
    }
   ],
   "source": [
    "statement_num_words = \"\"\"\n",
    "    SELECT CARDINALITY(SPLIT(review_body, ' ')) as num_words\n",
    "    FROM {}\n",
    "\"\"\".format(table_name)\n",
    "\n",
    "print(statement_num_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query data in Amazon Athena database passing the SQL statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 513 ms, sys: 20.9 ms, total: 534 ms\n",
      "Wall time: 3.59 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_num_words = wr.athena.read_sql_query(\n",
    "    sql=statement_num_words,\n",
    "    database=database_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out and analyse some descriptive statistics: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22626.000000\n",
       "mean        62.709847\n",
       "std         29.993735\n",
       "min          2.000000\n",
       "10%         22.000000\n",
       "20%         33.000000\n",
       "30%         42.000000\n",
       "40%         51.000000\n",
       "50%         61.000000\n",
       "60%         72.000000\n",
       "70%         86.000000\n",
       "80%         97.000000\n",
       "90%        103.000000\n",
       "100%       122.000000\n",
       "max        122.000000\n",
       "Name: num_words, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = df_num_words[\"num_words\"].describe(percentiles=[0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00])\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the words number per review:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 595,
       "width": 907
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_num_words[\"num_words\"].plot.hist(xticks=[0, 16, 32, 64, 128, 256], bins=100, range=[0, 256]).axvline(\n",
    "    x=summary[\"100%\"], c=\"red\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Words number\", fontsize='14')\n",
    "plt.ylabel(\"Frequency\", fontsize='14')\n",
    "plt.savefig('distribution_num_words_per_review.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All Visualizations saved as an image\n",
    "## Raw Data and Tranformed Data available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "instance_type": "ml.t3.medium",
  "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}