Data Analysis/Project - Case Study 1 - Wine Quality/Case Study - Wine Quality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case Study of Wine Quality\n",
    "\n",
    "![wine](wine.jpg)\n",
    "\n",
    "<br/><br/>\n",
    "\n",
    "- **Wine dataset**: https://archive.ics.uci.edu/ml/datasets/Wine+Quality\n",
    "- img credit to [dreamstime](https://www.dreamstime.com/types-wine-glasses-flat-vector-cartoon-illustration-isolated-red-white-rose-wines-image155731509)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "white_df = pd.read_csv(\"Data/winequality-white.csv\", sep=\";\")\n",
    "red_df = pd.read_csv(\"Data/winequality-red.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4898 entries, 0 to 4897\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         4898 non-null   float64\n",
      " 1   volatile acidity      4898 non-null   float64\n",
      " 2   citric acid           4898 non-null   float64\n",
      " 3   residual sugar        4898 non-null   float64\n",
      " 4   chlorides             4898 non-null   float64\n",
      " 5   free sulfur dioxide   4898 non-null   float64\n",
      " 6   total sulfur dioxide  4898 non-null   float64\n",
      " 7   density               4898 non-null   float64\n",
      " 8   pH                    4898 non-null   float64\n",
      " 9   sulphates             4898 non-null   float64\n",
      " 10  alcohol               4898 non-null   float64\n",
      " 11  quality               4898 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 459.3 KB\n"
     ]
    }
   ],
   "source": [
    "white_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "red_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Samples in White Wine dataset:  4898\n",
      "Number of Samples in Red Wine dataset:  1599\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Samples in White Wine dataset: \", white_df.shape[0])\n",
    "print(\"Number of Samples in Red Wine dataset: \", red_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns in White Wine dataset:  12\n",
      "Number of columns in Red Wine dataset:  12\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of columns in White Wine dataset: \", white_df.shape[1])\n",
    "print(\"Number of columns in Red Wine dataset: \", red_df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values in White Wine dataset : \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "fixed acidity           0\n",
       "volatile acidity        0\n",
       "citric acid             0\n",
       "residual sugar          0\n",
       "chlorides               0\n",
       "free sulfur dioxide     0\n",
       "total sulfur dioxide    0\n",
       "density                 0\n",
       "pH                      0\n",
       "sulphates               0\n",
       "alcohol                 0\n",
       "quality                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Number of missing values in White Wine dataset : \")\n",
    "white_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values in Red Wine dataset : \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "fixed acidity           0\n",
       "volatile acidity        0\n",
       "citric acid             0\n",
       "residual sugar          0\n",
       "chlorides               0\n",
       "free sulfur dioxide     0\n",
       "total sulfur dioxide    0\n",
       "density                 0\n",
       "pH                      0\n",
       "sulphates               0\n",
       "alcohol                 0\n",
       "quality                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Number of missing values in Red Wine dataset : \")\n",
    "red_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows in White Wine data set:  937\n",
      "Number of duplicate rows in Red Wine data set:  240\n"
     ]
    }
   ],
   "source": [
    "#check for duplicates\n",
    "print(\"Number of duplicate rows in White Wine data set: \", sum(white_df.duplicated()))\n",
    "print(\"Number of duplicate rows in Red Wine data set: \", sum(red_df.duplicated()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique quality for White:  7\n",
      "\n",
      "Aggregrated per Quality\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6    2198\n",
       "5    1457\n",
       "7     880\n",
       "8     175\n",
       "4     163\n",
       "3      20\n",
       "9       5\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of uniques values in Quality Feature\n",
    "print(\"Unique quality for White: \", white_df[\"quality\"].nunique())\n",
    "print(\"\\nAggregrated per Quality\")\n",
    "white_df[\"quality\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique quality for Red:  6\n",
      "\n",
      "Aggregrated per Quality\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5    681\n",
       "6    638\n",
       "7    199\n",
       "4     53\n",
       "8     18\n",
       "3     10\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of uniques values in Quality Feature\n",
    "print(\"Unique quality for Red: \", red_df[\"quality\"].nunique())\n",
    "print(\"\\nAggregrated per Quality\")\n",
    "red_df[\"quality\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean density of white wine:  0.9940273764801959\n"
     ]
    }
   ],
   "source": [
    "# mean density of the white wine dataset\n",
    "print(\"Mean density of white wine: \", white_df[\"density\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean density of red wine:  0.996746679174484\n"
     ]
    }
   ],
   "source": [
    "# mean density of the red wine dataset\n",
    "print(\"Mean density of red wine: \", red_df[\"density\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appending Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
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       "      <th>density</th>\n",
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       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  color  \n",
       "0      8.8        6  White  \n",
       "1      9.5        6  White  \n",
       "2     10.1        6  White  \n",
       "3      9.9        6  White  \n",
       "4      9.9        6  White  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add color column to each dataset\n",
    "white_df[\"color\"] = \"White\"\n",
    "white_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
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       "      <td>7.4</td>\n",
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       "      <td>7.8</td>\n",
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       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
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       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality color  \n",
       "0      9.4        5   Red  \n",
       "1      9.8        5   Red  \n",
       "2      9.8        5   Red  \n",
       "3      9.8        6   Red  \n",
       "4      9.4        5   Red  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_df[\"color\"] = \"Red\"\n",
    "red_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
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       "      <th>color</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
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       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
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       "      <td>6</td>\n",
       "      <td>White</td>\n",
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       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
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       "      <td>6</td>\n",
       "      <td>White</td>\n",
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       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  color  \n",
       "0      8.8        6  White  \n",
       "1      9.5        6  White  \n",
       "2     10.1        6  White  \n",
       "3      9.9        6  White  \n",
       "4      9.9        6  White  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine both dataset as new df\n",
    "wine_df = white_df.append(red_df, ignore_index=True)\n",
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>fixed acidity</th>\n",
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       "      <th>6492</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>32.0</td>\n",
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       "      <th>6493</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99512</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
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       "      <td>6</td>\n",
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       "      <th>6494</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.076</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99574</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Red</td>\n",
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       "      <th>6495</th>\n",
       "      <td>5.9</td>\n",
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       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99547</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.2</td>\n",
       "      <td>5</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.067</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99549</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "6492            6.2             0.600         0.08             2.0      0.090   \n",
       "6493            5.9             0.550         0.10             2.2      0.062   \n",
       "6494            6.3             0.510         0.13             2.3      0.076   \n",
       "6495            5.9             0.645         0.12             2.0      0.075   \n",
       "6496            6.0             0.310         0.47             3.6      0.067   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "6492                 32.0                  44.0  0.99490  3.45       0.58   \n",
       "6493                 39.0                  51.0  0.99512  3.52       0.76   \n",
       "6494                 29.0                  40.0  0.99574  3.42       0.75   \n",
       "6495                 32.0                  44.0  0.99547  3.57       0.71   \n",
       "6496                 18.0                  42.0  0.99549  3.39       0.66   \n",
       "\n",
       "      alcohol  quality color  \n",
       "6492     10.5        5   Red  \n",
       "6493     11.2        6   Red  \n",
       "6494     11.0        6   Red  \n",
       "6495     10.2        5   Red  \n",
       "6496     11.0        6   Red  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6497 entries, 0 to 6496\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         6497 non-null   float64\n",
      " 1   volatile acidity      6497 non-null   float64\n",
      " 2   citric acid           6497 non-null   float64\n",
      " 3   residual sugar        6497 non-null   float64\n",
      " 4   chlorides             6497 non-null   float64\n",
      " 5   free sulfur dioxide   6497 non-null   float64\n",
      " 6   total sulfur dioxide  6497 non-null   float64\n",
      " 7   density               6497 non-null   float64\n",
      " 8   pH                    6497 non-null   float64\n",
      " 9   sulphates             6497 non-null   float64\n",
      " 10  alcohol               6497 non-null   float64\n",
      " 11  quality               6497 non-null   int64  \n",
      " 12  color                 6497 non-null   object \n",
      "dtypes: float64(11), int64(1), object(1)\n",
      "memory usage: 660.0+ KB\n"
     ]
    }
   ],
   "source": [
    "wine_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Combined Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_df.to_csv(\"Data/winequality_cleaned.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring with Visuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms of various features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  color  \n",
       "0      8.8        6  White  \n",
       "1      9.5        6  White  \n",
       "2     10.1        6  White  \n",
       "3      9.9        6  White  \n",
       "4      9.9        6  White  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df[\"fixed acidity\"].plot(kind=\"hist\", bins=20)\n",
    "\n",
    "#customize with pyplot\n",
    "plt.grid(axis=\"y\", alpha=0.75)\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(\"Fixed Acidity Histogram\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df[\"total sulfur dioxide\"].plot(kind=\"hist\", bins=20)\n",
    "\n",
    "#customize with pyplot\n",
    "plt.grid(axis=\"y\", alpha=0.75)\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(\"Total Sulfur Dioxide Histogram\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df[\"pH\"].plot(kind=\"hist\",bins=20)\n",
    "\n",
    "#customize with pyplot\n",
    "plt.grid(axis=\"y\", alpha=0.75)\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"pH Histogram\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df[\"alcohol\"].plot(kind=\"hist\", bins=20)\n",
    "\n",
    "#customize with pyplot\n",
    "plt.grid(axis=\"y\", alpha=0.75)\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Alcohol Histogram\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplots of various features & quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df.plot(kind=\"scatter\", x=\"volatile acidity\", y=\"quality\")\n",
    "\n",
    "plt.title(\"Volatitle Acidity & Quality correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df.plot(kind=\"scatter\", x=\"residual sugar\", y=\"quality\")\n",
    "\n",
    "plt.title(\"Residual Sugar & Quality correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df.plot(kind=\"scatter\", x=\"pH\", y=\"quality\")\n",
    "\n",
    "plt.title(\"pH & Quality correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df.plot(kind=\"scatter\", x=\"alcohol\", y=\"quality\")\n",
    "\n",
    "plt.title(\"Alcohol & Quality correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drawing Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Q: Is a certain type of wine (red or white) associated with higher quality?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>quality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Red</th>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>5.877909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        quality\n",
       "color          \n",
       "Red    5.636023\n",
       "White  5.877909"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.groupby([\"color\"]).mean()[[\"quality\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion: \n",
    "White Wine has a slightly higher Average Quality Rating than Red Wine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q:What level of acidity (pH value) receives the highest average rating?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6497.000000\n",
       "mean        3.218501\n",
       "std         0.160787\n",
       "min         2.720000\n",
       "25%         3.110000\n",
       "50%         3.210000\n",
       "75%         3.320000\n",
       "max         4.010000\n",
       "Name: pH, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As pH value doesn't have clear catagories, we will create own categories.\n",
    "\n",
    "# View the min, 25%, 50%, 75%, max pH values with Pandas describe\n",
    "wine_df[\"pH\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bin edges that will be used to \"cut\" the data into groups\n",
    "# Fill in this list with five values from descibe\n",
    "bin_edges = [2.72, 3.11, 3.21, 3.32, 4.01]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labels for the four acidity level groups\n",
    "bin_names =  ['High', 'Moderately_High', 'Medium', 'Low'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "      <th>acidity_levels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Moderately_High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Moderately_High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  color   acidity_levels  \n",
       "0      8.8        6  White             High  \n",
       "1      9.5        6  White           Medium  \n",
       "2     10.1        6  White           Medium  \n",
       "3      9.9        6  White  Moderately_High  \n",
       "4      9.9        6  White  Moderately_High  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creates acidity_levels column\n",
    "wine_df[\"acidity_levels\"] = pd.cut(wine_df[\"pH\"], bin_edges, labels=bin_names)\n",
    "\n",
    "# Checks for successful creation of this column\n",
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>quality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acidity_levels</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>5.783343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderately_High</th>\n",
       "      <td>5.784540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>5.850832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>5.859593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  quality\n",
       "acidity_levels           \n",
       "High             5.783343\n",
       "Moderately_High  5.784540\n",
       "Medium           5.850832\n",
       "Low              5.859593"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean quality of each acidity level with groupby\n",
    "wine_df.groupby([\"acidity_levels\"]).mean()[[\"quality\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion: \n",
    "It is found that the higher the quality rating, the lower the acidity level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q: Do wines with higher alcoholic content receive better ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "      <th>acidity_levels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Moderately_High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>Moderately_High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  color   acidity_levels  \n",
       "0      8.8        6  White             High  \n",
       "1      9.5        6  White           Medium  \n",
       "2     10.1        6  White           Medium  \n",
       "3      9.9        6  White  Moderately_High  \n",
       "4      9.9        6  White  Moderately_High  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Alcohol Amount:  10.491800831152842\n"
     ]
    }
   ],
   "source": [
    "# get the median amount of alcohol content\n",
    "median_alcohol_amt = wine_df[\"alcohol\"].mean()\n",
    "print(\"Median Alcohol Amount: \", median_alcohol_amt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select samples with alcohol content less than the median\n",
    "below_median_alcohol_wine_df = wine_df.query('alcohol < {}'.format(median_alcohol_amt))\n",
    "\n",
    "# select samples with alcohol content greater than or equal to the median\n",
    "above_median_alchohol_wine_df = wine_df.query('alcohol >= {}'.format(median_alcohol_amt))\n",
    "\n",
    "# ensure these queries included each sample exactly once\n",
    "wine_df.shape[0] == below_median_alcohol_wine_df.shape[0] + above_median_alchohol_wine_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Quality Rating for Low Alcohol Wine:  5.502721283299914\n",
      "Mean Quality Rating for High Alcohol Wine:  6.184963406520293\n"
     ]
    }
   ],
   "source": [
    "# get mean quality rating for the low alcohol and high alcohol groups\n",
    "mean_below = below_median_alcohol_wine_df[\"quality\"].mean()\n",
    "mean_above = above_median_alchohol_wine_df[\"quality\"].mean()\n",
    "print(\"Mean Quality Rating for Low Alcohol Wine: \", mean_below)\n",
    "print(\"Mean Quality Rating for High Alcohol Wine: \", mean_above)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conculsions: \n",
    "It seems like High Alcohol content wine receives a little bit Better Rating than Low Alcohol content wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create bar chart with proper labels\n",
    "x_positions = [1,2]\n",
    "labels = [\"Low\", \"High\"]\n",
    "\n",
    "heights = [mean_below, mean_above]\n",
    "\n",
    "plt.bar(x_locations, heights, tick_label = labels)\n",
    "plt.title(\"Average Quality Rating by Alcohol Content\")\n",
    "plt.xlabel(\"Alcohol Content\")\n",
    "plt.ylabel(\"Average Quality Rating\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q: Do sweeter wines (more residual sugar) receive better ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Sugar Amount:  5.443235339387409\n"
     ]
    }
   ],
   "source": [
    "# get the median amount of residual sugar\n",
    "median_sugar_amt = wine_df[\"residual sugar\"].mean()\n",
    "print(\"Median Sugar Amount: \",median_sugar_amt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select samples with residual sugar less than the median\n",
    "below_median_sugar_wine_df = wine_df[wine_df[\"residual sugar\"] < median_sugar_amt]\n",
    "\n",
    "# select samples with residual sugar greater than or equal to the median\n",
    "above_median_sugar_win_df = wine_df[wine_df[\"residual sugar\"] > median_sugar_amt]\n",
    "\n",
    "# ensure these queries included each sample exactly once\n",
    "wine_df.shape[0] == below_median_sugar_wine_df.shape[0] + above_median_sugar_win_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Quality Rating for Low Sugar Wine:  5.845544554455445\n",
      "Mean Quality Rating for High Sugar Wine:  5.773707773707773\n"
     ]
    }
   ],
   "source": [
    "# get mean quality rating for the low sugar and high sugar groups\n",
    "below_sugar = below_median_sugar_wine_df[\"quality\"].mean()\n",
    "above_sugar = above_median_sugar_win_df[\"quality\"].mean()\n",
    "\n",
    "print(\"Mean Quality Rating for Low Sugar Wine: \", below_sugar)\n",
    "print(\"Mean Quality Rating for High Sugar Wine: \", above_sugar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conculsions:\n",
    "It seems like Low sugar content wine receive a bit higher quality rating than High sugar content wines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart with proper labels\n",
    "x_positions = [1,2]\n",
    "labels = [\"Low\", \"High\"]\n",
    "heights = [below_sugar, above_sugar]\n",
    "\n",
    "plt.bar(x_positions, heights, tick_label = labels)\n",
    "plt.xlabel(\"Sugar Content\")\n",
    "plt.ylabel(\"Average Quality Rating\")\n",
    "plt.title(\"Average Quality Rating by Sugar Content\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## White Vs Red Wine Proportions by Color & Quality\n",
    "\n",
    "1. Red bar proportions = counts for each quality rating / total # of red samples\n",
    "2. White bar proportions = counts for each quality rating / total # of white samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color  quality\n",
       "Red    3            10\n",
       "       4            53\n",
       "       5           681\n",
       "       6           638\n",
       "       7           199\n",
       "       8            18\n",
       "White  3            20\n",
       "       4           163\n",
       "       5          1457\n",
       "       6          2198\n",
       "       7           880\n",
       "       8           175\n",
       "       9             5\n",
       "Name: pH, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get counts for each rating and color\n",
    "rating_color_counts = wine_df.groupby([\"color\", \"quality\"]).count()[\"pH\"]\n",
    "rating_color_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "Red      1599\n",
       "White    4898\n",
       "Name: pH, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get total counts for each color\n",
    "total_by_color = wine_df.groupby(\"color\").count()[\"pH\"]\n",
    "total_by_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality\n",
       "3    0.006254\n",
       "4    0.033146\n",
       "5    0.425891\n",
       "6    0.398999\n",
       "7    0.124453\n",
       "8    0.011257\n",
       "Name: pH, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get proportions by dividing red rating counts by total # of red samples\n",
    "red_proportions = rating_color_counts[\"Red\"] / total_by_color[\"Red\"]\n",
    "red_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality\n",
       "3    0.006254\n",
       "4    0.033146\n",
       "5    0.425891\n",
       "6    0.398999\n",
       "7    0.124453\n",
       "8    0.011257\n",
       "9    0.000000\n",
       "Name: pH, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#As Red wine doesn't have 9 rating group, so add it in the df. so that we can broadcast later.\n",
    "red_proportions[\"9\"] = 0\n",
    "red_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality\n",
       "3    0.004083\n",
       "4    0.033279\n",
       "5    0.297468\n",
       "6    0.448755\n",
       "7    0.179665\n",
       "8    0.035729\n",
       "9    0.001021\n",
       "Name: pH, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get proportions by dividing white rating counts by total # of white samples\n",
    "white_proportions = rating_color_counts[\"White\"] / total_by_color[\"White\"]\n",
    "white_proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot proportions on a bar chart\n",
    "\n",
    "Set the x coordinate location for each rating group and and width of each bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the x locations for the groups\n",
    "index = np.arange(len(red_proportions))  \n",
    "\n",
    "# the width of the bars\n",
    "width = 0.35       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bars\n",
    "red_bars = plt.bar(index, red_proportions, width, color='r', alpha=.7, label='Red Wine')\n",
    "white_bars = plt.bar(index + width, white_proportions, width, color='w', alpha=.7, label='White Wine')\n",
    "\n",
    "# title and labels\n",
    "plt.ylabel('Proportion')\n",
    "plt.xlabel('Quality')\n",
    "plt.title('Proportion by Wine Color and Quality')\n",
    "locations = index + width / 2  # xtick locations\n",
    "labels = ['3', '4', '5', '6', '7', '8', '9']  # xtick labels\n",
    "plt.xticks(locations, labels)\n",
    "\n",
    "# legend\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv-datascience",
   "language": "python",
   "name": "venv-datascience"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}