Case Study 1 - Analysing Wine Quality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case Study #1: Wine Quality 🍷\n",
    "\n",
    "# 1.0 Introduction\n",
    "\n",
    "In this case study, we will be analyzing two data sets - 1 on red wine samples and 1 on white wine samples from the north of Portugal. \n",
    "\n",
    "Each wine samples come with a quality rating based on the input variables in **2.2 Attribute Information**. \n",
    "\n",
    "# 2.0 Data Set \n",
    "\n",
    "## 2.1 Source\n",
    "\n",
    "> The two datasets are related to **red and white** variants of the Portuguese \"Vinho Verde\" wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).\n",
    "\n",
    "[Reference: https://archive.ics.uci.edu/ml/datasets/Wine+Quality](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)\n",
    "\n",
    "## 2.2 Attribute Information\n",
    "\n",
    "Input variables (based on physicochemical tests): \n",
    "- 1 - fixed acidity \n",
    "- 2 - volatile acidity \n",
    "- 3 - citric acid \n",
    "- 4 - residual sugar \n",
    "- 5 - chlorides \n",
    "- 6 - free sulfur dioxide \n",
    "- 7 - total sulfur dioxide \n",
    "- 8 - density \n",
    "- 9 - pH \n",
    "- 10 - sulphates \n",
    "- 11 - alcohol \n",
    "\n",
    "Output variable (based on sensory data): \n",
    "- 12 - quality (score between 0 and 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "# 3.0 Asking Questions\n",
    "\n",
    "Based on the data set, we have come up with these questions that we would like to answer in our analysis.\n",
    "- Is a certain type of wine (red or white) associated with higher quality?\n",
    "- Do wines with higher alcoholic content receive better ratings?\n",
    "- Do sweeter wines (more residual sugar) receive better ratings?\n",
    "- What level of acidity is associated with the highest quality?\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.0 Assessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries and load csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "red = pd.read_csv('/Users/katiehuang/Documents/Data Analytics/Udacity Data Analyst Nanodegree/Course 2 - Intro to Data Analysis/Case Study 1 - Wine Quality/winequality-red.csv', sep=';')\n",
    "white = pd.read_csv('/Users/katiehuang/Documents/Data Analytics/Udacity Data Analyst Nanodegree/Course 2 - Intro to Data Analysis/Case Study 1 - Wine Quality/winequality-white.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1599, 12), (4898, 12))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of samples in each dataset\n",
    "# number of columns in each dataset\n",
    "red.shape, white.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      "<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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features with missing values\n",
    "red.info(), white.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "937"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duplicate rows in the white wine dataset\n",
    "white.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique values for quality in each dataset\n",
    "red['quality'].nunique(), white['quality'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9967466791744831"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean density of the red wine dataset\n",
    "red['density'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.0 Appending Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Create Color Columns\n",
    "Create two arrays as long as the number of rows in the red and white dataframes that repeat the value “red” or “white.” NumPy offers really easy way to do this. Here’s the documentation for [NumPy’s repeat](https://docs.scipy.org/doc/numpy/reference/generated/numpy.repeat.html) function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create color array for red dataframe\n",
    "color_red = np.repeat('red',red.shape[0])\n",
    "\n",
    "# create color array for white dataframe\n",
    "color_white = np.repeat('white',white.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add arrays to the red and white dataframes. Do this by setting a new column called 'color' to the appropriate array. The cell below does this for the red dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>7.4</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",
       "    <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",
       "      <td>red</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",
       "      <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add red array to red df\n",
    "red['color'] = color_red\n",
    "red.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the same for the white dataframe and use `head()` to confirm the change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "      <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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add white array to white df\n",
    "white['color'] = color_white\n",
    "white.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Rename Columns\n",
    "\n",
    "Use [pandas' rename](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html) function to change the `total_sulfur-dioxide` column label to `total_sulfur_dioxide`. You can check out this [Stack Overflow](https://stackoverflow.com/questions/20868394/changing-a-specific-column-name-in-pandas-dataframe) page to help you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename column\n",
    "red.rename(columns={'total_sulfur-dioxide':'total_sulfur_dioxide'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Combine DataFrames with Append\n",
    "Check the documentation for [Pandas' append](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.append.html) function and see if you can use this to figure out how to combine the dataframes. \n",
    "\n",
    "(Bonus: Why aren't we using the [merge](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html) method to combine the dataframes?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>7.4</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",
       "    <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",
       "      <td>red</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",
       "      <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# append dataframes\n",
    "wine_df = red.append(white)\n",
    "\n",
    "# view dataframe to check for success\n",
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.0 EDA with Visuals\n",
    "Create visualizations to answer the questions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Histograms for Various Features\n",
    "\n",
    "### Question 1\n",
    "Based on histograms of columns in this dataset, which of the following feature variables **appear skewed to the right?**\n",
    "- Fixed Acidity\n",
    "- Total Sulfur Dioxide\n",
    "- pH\n",
    "- Alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E3wrMnztzkXN5T1W9nOQi4KEk3zzFthlQG/g/TlXtBnYDTE9P18zMzCKn+f/Nzs4yyvEGuem2B5Z0fIAdV5zgUwcW+/AtD4vt5fANM6ObzCKM42drHOxj+RllL4ta3qmql7vzY8BX6C3XvJJkLUB3fqzb/Aiwvm/3dcDLi7l/SdLpGTr0k5yT5G0/vAz8HeBp4H5gW7fZNuC+7vL9wNYkZyW5BNgIPD7s/UuSTt9i1gemgK8k+eE4/7GqfjfJHwL7ktwMvAh8GKCqDibZBzwDnABurao3FjV7SdJpGTr0q+qPgHcPqP8ZcO0c++wEdg57n5KkxfETuZLUkNVx+IeatmEMR0sNcnjXByZyv9Ji+Exfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIX7LpjSkk7/dc8cVJ8by95HBb/jU8HymL0kNMfQlqSGGviQ1xNCXpIYY+pLUEI/ekVagpfy7wKc6Csmjhla+VR36g34xxnlYnSQtN2MP/SSbgd8EzgA+V1W7xj0HScNZylcYp+IrjNEZ65p+kjOAfwv8XeBS4CNJLh3nHCSpZeN+pn81cKiq/gggyV5gC/DMmOchaQUZ5hXGSl/KXapXN6mqJRl44J0lfw/YXFX/oLv+UeBvVNXHT9puO7C9u/ou4LkRTuNC4E9HON6krJY+YPX0Yh/Ly2rpA4br5S9X1TtOLo77mX4G1N70v05V7QZ2L8kEkv1VNb0UY4/TaukDVk8v9rG8rJY+YLS9jPs4/SPA+r7r64CXxzwHSWrWuEP/D4GNSS5J8qPAVuD+Mc9Bkpo11uWdqjqR5OPAf6Z3yObdVXVwnHNgiZaNJmC19AGrpxf7WF5WSx8wwl7G+kauJGmy/O4dSWqIoS9JDWkq9JOckeS/J/mdSc9lMZKcl+RLSb6Z5Nkkf3PScxpGkn+U5GCSp5N8McmPTXpOC5Xk7iTHkjzdV7sgyUNJnu/Oz5/kHBdijj7+dfez9Y0kX0ly3gSnuCCD+ui77Z8kqSQXTmJup2uuXpL8QpLnut+ZfzXs+E2FPvAJ4NlJT2IEfhP43ar6q8C7WYE9JbkY+IfAdFVdTu+N/a2TndVpuQfYfFLtNuDhqtoIPNxdX+7u4c19PARcXlU/CfwP4PZxT2oI9/DmPkiyHvgZ4MVxT2gR7uGkXpJsovftBT9ZVZcBvz7s4M2EfpJ1wAeAz016LouR5FzgbwN3AVTV/62qb090UsM7Ezg7yZnAW1lBn9moqkeAb51U3gLs6S7vAa4f55yGMaiPqvq9qjrRXX2M3udplrU5Hg+ATwP/jAEfAl2u5ujlFmBXVb3ebXNs2PGbCX3g39B78H8w4Xks1k8AfwL8h26p6nNJzpn0pE5XVb1E79nKi8BR4DtV9XuTndWiTVXVUYDu/KIJz2cUPgY8OOlJDCPJh4CXqurrk57LCLwTeG+SryX5b0n++rADNRH6ST4IHKuqJyY9lxE4E/hp4M6q+inge6yMZYS/oFvv3gJcAvw4cE6Svz/ZWalfkl8GTgBfmPRcTleStwK/DPzzSc9lRM4EzgeuAf4psC/JoK+1mVcToQ+8B/hQksPAXuB9ST4/2SkN7QhwpKq+1l3/Er3/BFaa9wMvVNWfVNX3gS8Df2vCc1qsV5KsBejOh34JPmlJtgEfBG6olflhnr9C7wnF17vf+3XAk0n+0kRnNbwjwJer53F6KxZDvTHdROhX1e1Vta6qNtB7s/C/VtWKfFZZVf8L+OMk7+pK17Iyv5r6ReCaJG/tnrFcywp8Q/ok9wPbusvbgPsmOJehdX/o6JPAh6rqf096PsOoqgNVdVFVbeh+748AP939/qxE/wl4H0CSdwI/ypDfINpE6K9CvwB8Ick3gCuBfznZ6Zy+7pXKl4AngQP0fhZXzMfmk3wReBR4V5IjSW4GdgE/k+R5ekeMLPu/CjdHH58F3gY8lOSpJP9uopNcgDn6WJHm6OVu4Ce6wzj3AtuGfQXm1zBIUkN8pi9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkP+H+oLjwt6M9JlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fixed Acidity\n",
    "wine_df['fixed_acidity'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# Total Sulfur Dioxide\n",
    "wine_df['total_sulfur_dioxide'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "# pH\n",
    "wine_df['pH'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "# Alcohol\n",
    "wine_df['alcohol'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "Based on histograms of columns in this dataset, **Fixed Acidity** and **Alcohol** feature variables appear skewed to the right.\n",
    "\n",
    "Mentor, Myles C answered [here](https://knowledge.udacity.com/questions/485041). _[Note: This link brings you to a student-only portal in Udacity.]_\n",
    "\n",
    "> The direction of skew is shown by the length of the tails of the distribution. A _**long right tail**_ means _**skewed to the right.**_\n",
    "\n",
    "> What does this mean in practical terms? It means that there are some outliers on the right side of the distribution (i.e. some outliers with large numerical values). The effect is that these outliers, literally \"extreme observations\", will \"drag\" the mean/average up (so that it will be less representative of the data as a whole).\n",
    "\n",
    "\n",
    "## 6.2 Scatterplots of Quality Against Various Features\n",
    "\n",
    "### Question 2\n",
    "Based on scatterplots of quality against different feature variables, which of the following is **most likely to have a positive impact on quality**? \n",
    "- Volatile Acidity\n",
    "- Residual Sugar\n",
    "- pH\n",
    "- Alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "# Volatile Acidity\n",
    "wine_df.plot(y='quality', x='volatile_acidity', kind='scatter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "# Residual Sugar\n",
    "wine_df.plot(y='quality', x='residual_sugar', kind='scatter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "# pH\n",
    "wine_df.plot(y='quality', x='pH', kind='scatter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "# Alcohol\n",
    "wine_df.plot(y='quality', x='alcohol', kind='scatter');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "Based on scatterplots of quality against different feature variables, **Alcohol** is most likely to have a positive impact on quality.\n",
    "\n",
    "Based on mentor Sanjeev Y's explanation [here](https://knowledge.udacity.com/questions/387387) _[Note: This link brings you to a student-only Udacity portal]_:\n",
    "> The question is _**not**_ which variable has the _\"most impact\"_ on quality. The question is which variable is _\"most likely to have a positive impact\"_. This means we are looking for a **positive correlation and high value of correlation coefficient**.\n",
    "Alcohol has a positve correlation and highest among all the positive correlation scatterplots. The higher the alcohol content, the higher the quality.\n",
    "\n",
    "Here, we plot a Heatmap to show the correlation coefficient for the wine's feature variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap for feature variables\n",
    "corr = wine_df.corr()\n",
    "plt.figure(figsize=[10,8])\n",
    "sns.heatmap(corr, annot=True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"Heatmap of Correlation Coefficient for Wine Feature Variables\", size=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to the bottom row of Quality, the feature variable with the most positive impact on Quality is **Alcohol** at 0.44 coefficient. The other features such as **Volatile Acidity, Residual Sugar and pH** have negative or less than 0.1 correlation.\n",
    "\n",
    "***\n",
    "\n",
    "# 7.0 Drawing Conclusions Using Groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 7.1.1 Is a certain type of wine associated with higher quality?\n",
    "\n",
    "For this question, we compare the average quality of red wine with the average quality of white wine with groupby. To do this, group by color and then find the mean quality of each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "red      5.636023\n",
       "white    5.877909\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean quality of each wine type (red and white) with groupby\n",
    "wine_df.groupby('color')['quality'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "\n",
    "White wine is typically associated with higher quality at 5.877."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1.2 What level of acidity receives the highest average rating?\n",
    "\n",
    "This question is tricky becase unlike the previous question, `color` is a categorical variable and can easily be grouped, however level of acidity/ `pH` is a quantitative variable without clear categories.\n",
    "\n",
    "We can create categorical variable by creating our own categories using the quantitative variables.\n",
    "\n",
    "First, create a new column called `acidity_levels` with these categories:\n",
    "\n",
    "Acidity Levels:\n",
    "- High: Lowest 25% of pH values\n",
    "- Moderately High: 25% - 50% of pH values\n",
    "- Medium: 50% - 75% of pH values\n",
    "- Low: 75% - max pH value\n",
    "\n",
    "Here, the data is being split at the 25th, 50th, and 75th percentile. \n",
    "\n",
    "After we create these four categories, we will be able to use groupby to get the mean quality rating for each acidity level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the min, 25%, 50%, 75%, max pH values with Pandas describe\n",
    "wine_df['pH'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bin edges that will be used to \"cut\" the data into groups\n",
    "bin_edges = [2.72, 3.11, 3.21, 3.32, 4.01] # Fill in this list with five values you just found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labels for the four acidity level groups\n",
    "bin_names = ['high','moderately high','medium','low'] # Name each acidity level category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>7.4</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",
       "      <td>low</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",
       "      <td>red</td>\n",
       "      <td>moderately high</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",
       "      <td>red</td>\n",
       "      <td>medium</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",
       "      <td>moderately high</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",
       "      <td>low</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   acidity_levels  \n",
       "0      9.4        5   red              low  \n",
       "1      9.8        5   red  moderately high  \n",
       "2      9.8        5   red           medium  \n",
       "3      9.8        6   red  moderately high  \n",
       "4      9.4        5   red              low  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creates acidity_levels column\n",
    "wine_df['acidity_levels'] = pd.cut(wine_df['pH'], bins=bin_edges, labels=bin_names)\n",
    "\n",
    "# Checks for successful creation of this column\n",
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acidity_levels\n",
       "high               5.783343\n",
       "moderately high    5.784540\n",
       "medium             5.850832\n",
       "low                5.859593\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean quality of each acidity level with groupby\n",
    "wine_df.groupby('acidity_levels')['quality'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "\n",
    "Wine is low acidity level/ `pH` receives the highest average rating, followed closely by medium acidity level. We can conclude that _the lower the acidity level, the higher the quality of wine_.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.2 Drawing Conclusions Using Query"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 7.2.1 Do wines with higher alcoholic content receive better ratings?\n",
    "\n",
    "To answer this question, use query to create two groups of wine samples:\n",
    "\n",
    "- Low alcohol (samples with an alcohol content less than the median)\n",
    "- High alcohol (samples with an alcohol content greater than or equal to the median)\n",
    "\n",
    "Then, find the mean quality rating of each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the median amount of alcohol content\n",
    "wine_df['alcohol'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select samples with alcohol content less than the median\n",
    "low_alcohol = wine_df.query('alcohol < 10.3')\n",
    "\n",
    "# select samples with alcohol content greater than or equal to the median\n",
    "high_alcohol = wine_df.query('alcohol >= 10.3')\n",
    "\n",
    "# ensure these queries included each sample exactly once\n",
    "num_samples = wine_df.shape[0]\n",
    "num_samples == low_alcohol['quality'].count() + high_alcohol['quality'].count() # should be True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.475920679886686"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get mean quality rating for the low alcohol group\n",
    "low_alcohol['quality'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.146084337349397"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get mean quality rating for the high alcohol group\n",
    "high_alcohol['quality'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "\n",
    "Wines with higher alcohol content receives better ratings.\n",
    "\n",
    "## 7.2.2 Do sweeter wines receive better ratings?\n",
    "\n",
    "Similarly, use the median method above to find the answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the median amount of residual sugar\n",
    "wine_df['residual_sugar'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select samples with residual sugar less than the median\n",
    "low_sugar = wine_df.query('residual_sugar < 3.0')\n",
    "\n",
    "# select samples with residual sugar greater than or equal to the median\n",
    "high_sugar = wine_df.query('residual_sugar >= 3.0')\n",
    "\n",
    "# ensure these queries included each sample exactly once\n",
    "num_samples == low_sugar['quality'].count() + high_sugar['quality'].count() # should be True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.808800743724822"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get mean quality rating for the low sugar group\n",
    "low_sugar['quality'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.82782874617737"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get mean quality rating for the high sugar group\n",
    "high_sugar['quality'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "\n",
    "Sweeter wines with higher sugar content receives higher rating although there is a close match with lower sugar content."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "# 8.0 Plotting with Matplotlib\n",
    "Use Matplotlib to create bar charts that visualize the conclusions made with groupby and query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1 Do wines with higher alcoholic content receive better ratings?\n",
    "Create a bar chart with one bar for low alcohol and one bar for high alcohol wine samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use query to select each group and get its mean quality\n",
    "# Get overall median for alcohol\n",
    "median = wine_df['alcohol'].median()\n",
    "\n",
    "# Get subsets for data with < and >= alcohol median\n",
    "low = wine_df.query('alcohol < {}'.format(median))\n",
    "high = wine_df.query('alcohol >= {}'.format(median))\n",
    "\n",
    "# Get mean for quality with low and high alcohol\n",
    "mean_quality_low_alcohol = low['quality'].mean()\n",
    "mean_quality_high_alcohol = high['quality'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
    "locations = [1, 2]\n",
    "heights = [mean_quality_low_alcohol, mean_quality_high_alcohol]\n",
    "labels = ['Low', 'High']\n",
    "\n",
    "plt.bar(locations, heights, tick_label=labels)\n",
    "plt.title('Average Quality Ratings by Alcohol Content')\n",
    "plt.xlabel('Alcohol Content')\n",
    "plt.ylabel('Average Quality Rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "\n",
    "From the bar chart above, wines with higher alcohol content receives a higher quality rating.\n",
    "\n",
    "## 8.2 Do sweeter wines receive higher ratings?\n",
    "Create a bar chart with one bar for low residual sugar and one bar for high residual sugar wine samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use query to select each group and get its mean quality\n",
    "# Get the median for residual sugar\n",
    "median = wine_df['residual_sugar'].median()\n",
    "\n",
    "# Get the subsets for < and >= median\n",
    "low_sugar = wine_df.query('residual_sugar < {}'.format(median))\n",
    "high_sugar = wine_df.query('residual_sugar >= {}'.format(median))\n",
    "\n",
    "# Get the mean for quality\n",
    "mean_quality_low_sugar = low_sugar['quality'].mean()\n",
    "mean_quality_high_sugar = high_sugar['quality'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "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",
    "locations = [1,2]\n",
    "heights = [mean_quality_low_sugar, mean_quality_high_sugar]\n",
    "labels = ['Low', 'High']\n",
    "\n",
    "plt.bar(locations, heights, tick_label = labels)\n",
    "plt.title('Average Quality Ratings by Residual Sugar')\n",
    "plt.xlabel('Residual Sugar')\n",
    "plt.ylabel('Average Quality Rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "From the above bar chart, it is difficult to properly determine the impact of residual sugar on the quality rating. We will create a line chart below to see whether we can get a better conclusion.\n",
    "\n",
    "## 8.3 What level of acidity receives the highest average rating?\n",
    "Create a bar chart with a bar for each of the four acidity levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acidity_levels\n",
       "high               5.783343\n",
       "moderately high    5.784540\n",
       "medium             5.850832\n",
       "low                5.859593\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use groupby to get the mean quality for each acidity level\n",
    "acidity_level_mean = wine_df.groupby('acidity_levels')['quality'].mean()\n",
    "acidity_level_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
    "locations = [2,3,4,1]\n",
    "heights = acidity_level_mean\n",
    "labels = ['low','medium','moderately high','high']\n",
    "\n",
    "plt.bar(locations, heights, tick_label = labels)\n",
    "plt.title('Average Quality Ratings by Acidity Level')\n",
    "plt.xlabel('Acidity Level')\n",
    "plt.ylabel('Average Quality Rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "Similarly, the above bar chart is also not able to provide any meaningful answer as to which level of acidity provides a lower or higher quality rating.\n",
    "\n",
    "## Bonus: Create a line plot for the data in 7.2 and 7.3\n",
    "You can use pyplot's [plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html) function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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 line with proper labels\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "locations = [1,2]\n",
    "heights = [mean_quality_low_sugar, mean_quality_high_sugar]\n",
    "\n",
    "plt.plot(locations, heights)\n",
    "plt.title('Average Quality Ratings by Residual Sugar')\n",
    "plt.xlabel('Residual Sugar')\n",
    "plt.ylabel('Average Quality Rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "With the line chart above, we can see the relationship between residual sugar and quality rating better. _The higher the residual sugar, the higher the quality rating._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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 line chart with proper labels\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "locations = [1,2,3,4]\n",
    "heights = [5.859593, 5.850832, 5.784540, 5.783343]\n",
    "\n",
    "plt.plot(locations, heights)\n",
    "plt.title('Average Quality Ratings by Acidity Level')\n",
    "plt.xlabel('Acidity Level')\n",
    "plt.ylabel('Average Quality Rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer\n",
    "With the line chart above, we can see _the higher the acidity level or `pH`, the lower the quality rating of the wine._\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.0 Conclusion\n",
    "\n",
    "Let's revisit our questions and include our observations from the analysis.\n",
    "\n",
    "**1. Is a certain type of wine (red or white) associated with higher quality?**\n",
    "\n",
    "> White wine is typically associated with higher quality.\n",
    "\n",
    "**2. Do wines with higher alcoholic content receive better ratings?**\n",
    "\n",
    "> Wines with higher alcohol content generally receives better quality ratings.\n",
    "\n",
    "**3. Do sweeter wines (more residual sugar) receive better ratings?**\n",
    "\n",
    "> Sweeter wines with higher sugar content receives higher rating although there is close match with lower sugar content.\n",
    "\n",
    "**4. What level of acidity is associated with the highest quality?**\n",
    "> Wine with low acidity level/`pH` receives the highest average rating, followed closely by medium acidity level. We can conclude that _the lower the acidity level, the higher the quality of wine._\n",
    "\n",
    "Thank you for reading my analysis!"
   ]
  }
 ],
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