eda_visuals_practise_functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA with Visuals\n",
    "Create visualizations to answer the quiz questions below this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6497 entries, 0 to 6496\n",
      "Data columns (total 14 columns):\n",
      "fixed acidity           6497 non-null float64\n",
      "volatile acidity        6497 non-null float64\n",
      "citric acid             6497 non-null float64\n",
      "residual sugar          6497 non-null float64\n",
      "chlorides               6497 non-null float64\n",
      "free sulfur dioxide     6497 non-null float64\n",
      "total sulfur dioxide    6497 non-null float64\n",
      "density                 6497 non-null float64\n",
      "pH                      6497 non-null float64\n",
      "sulphates               6497 non-null float64\n",
      "alcohol                 6497 non-null float64\n",
      "quality                 6497 non-null int64\n",
      "color                   6497 non-null object\n",
      "acidity_levels          6496 non-null object\n",
      "dtypes: float64(11), int64(1), object(2)\n",
      "memory usage: 710.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Load dataset\n",
    "wine_df = pd.read_csv('winequality_edited.csv')\n",
    "wine_df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms for Various Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x111ae98d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10c58bcf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a68e668>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a6f4cf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a757080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a7570b8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a81b4a8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a88fd68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a8f3ba8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a92ff98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a9c0518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a9d1550>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hnat/ErzKhkraWVIPwmT0rHpcg1MfvKfgOF2AlNvxiBEjILodEz6W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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ab02b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a9e5668>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7PaVrXwM81bPuVNcmSRqBVFX/KydvrapnkpwC7AY+BuyqqhN7+rxYVScluRP4F1X1ra59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\nowwASWqUASBJjTIAJKlRBoAkNer/AyZ3iFGazIkcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae787f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df['fixed acidity'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b064080>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae78978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df['pH'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b2904a8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OATOzjDkEzMwy5hAwM8uYQ8DMLGMOATOzjDkEzMwy5hAwM8uYQ8DMLGNth4CkMUkjknZJ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\nmWXs/wHnac3hgvVXwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b2a4588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df['total sulfur dioxide'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b365748>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z2NQ/ydbRZk/LdI7rba/p6j2yvtL5YmahW79vRbneJg4BRcR3gJclvTU1rQGeB3YBG1Lb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\nADAzy5QDwMwsU/8XanZrEGow4wwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b2a42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_df['alcohol'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "- 'pH' and 'total sulfur dioixde' factors almost normal distribution.\n",
    "- Other factors: fixed acidity and alcohol are all skewed to the right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatterplots of Quality Against Various Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b5006d8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b3ba5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = wine_df['quality'], y = wine_df['pH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b64c160>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a71c128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = wine_df['quality'], y = wine_df['volatile acidity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b73c828>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uM6zP3nBpbsdsZXKuqyT9NCJekCTb90m6UdKz7SgMADpZEYvctxLo/ZIOnLB9UNLVrZVz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b58a2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = wine_df['quality'], y = wine_df['residual sugar'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b9027b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = wine_df['quality'], y = wine_df['alcohol'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we see that 'pH' and 'residual_sugar' have somewhat positive relationship [normal distribution] and 'alcohol' has almost true positive relationship [almost linear positive]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QUIZ Q&A**\n",
    "- Based on histograms of columns in this dataset, which feature variables appear skewed to the right?\n",
    "Fixed acidity and Alcohol\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",
    "Alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      "fixed acidity           6497 non-null float64\n",
      "volatile acidity        6497 non-null float64\n",
      "citric acid             6497 non-null float64\n",
      "residual sugar          6497 non-null float64\n",
      "chlorides               6497 non-null float64\n",
      "free sulfur dioxide     6497 non-null float64\n",
      "total sulfur dioxide    6497 non-null float64\n",
      "density                 6497 non-null float64\n",
      "pH                      6497 non-null float64\n",
      "sulphates               6497 non-null float64\n",
      "alcohol                 6497 non-null float64\n",
      "quality                 6497 non-null int64\n",
      "color                   6497 non-null object\n",
      "dtypes: float64(11), int64(1), object(1)\n",
      "memory usage: 659.9+ KB\n"
     ]
    }
   ],
   "source": [
    "wine_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity             7.215307\n",
       "volatile acidity          0.339666\n",
       "citric acid               0.318633\n",
       "residual sugar            5.443235\n",
       "chlorides                 0.056034\n",
       "free sulfur dioxide      30.525319\n",
       "total sulfur dioxide    115.744574\n",
       "density                   0.994697\n",
       "pH                        3.218501\n",
       "sulphates                 0.531268\n",
       "alcohol                  10.491801\n",
       "quality                   5.818378\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.853333</td>\n",
       "      <td>0.517000</td>\n",
       "      <td>0.281000</td>\n",
       "      <td>5.140000</td>\n",
       "      <td>0.077033</td>\n",
       "      <td>39.216667</td>\n",
       "      <td>122.033333</td>\n",
       "      <td>0.995744</td>\n",
       "      <td>3.257667</td>\n",
       "      <td>0.506333</td>\n",
       "      <td>10.215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.288889</td>\n",
       "      <td>0.457963</td>\n",
       "      <td>0.272315</td>\n",
       "      <td>4.153704</td>\n",
       "      <td>0.060056</td>\n",
       "      <td>20.636574</td>\n",
       "      <td>103.432870</td>\n",
       "      <td>0.994833</td>\n",
       "      <td>3.231620</td>\n",
       "      <td>0.505648</td>\n",
       "      <td>10.180093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.326801</td>\n",
       "      <td>0.389614</td>\n",
       "      <td>0.307722</td>\n",
       "      <td>5.804116</td>\n",
       "      <td>0.064666</td>\n",
       "      <td>30.237371</td>\n",
       "      <td>120.839102</td>\n",
       "      <td>0.995849</td>\n",
       "      <td>3.212189</td>\n",
       "      <td>0.526403</td>\n",
       "      <td>9.837783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.177257</td>\n",
       "      <td>0.313863</td>\n",
       "      <td>0.323583</td>\n",
       "      <td>5.549753</td>\n",
       "      <td>0.054157</td>\n",
       "      <td>31.165021</td>\n",
       "      <td>115.410790</td>\n",
       "      <td>0.994558</td>\n",
       "      <td>3.217726</td>\n",
       "      <td>0.532549</td>\n",
       "      <td>10.587553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.128962</td>\n",
       "      <td>0.288800</td>\n",
       "      <td>0.334764</td>\n",
       "      <td>4.731696</td>\n",
       "      <td>0.045272</td>\n",
       "      <td>30.422150</td>\n",
       "      <td>108.498610</td>\n",
       "      <td>0.993126</td>\n",
       "      <td>3.228072</td>\n",
       "      <td>0.547025</td>\n",
       "      <td>11.386006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.835233</td>\n",
       "      <td>0.291010</td>\n",
       "      <td>0.332539</td>\n",
       "      <td>5.382902</td>\n",
       "      <td>0.041124</td>\n",
       "      <td>34.533679</td>\n",
       "      <td>117.518135</td>\n",
       "      <td>0.992514</td>\n",
       "      <td>3.223212</td>\n",
       "      <td>0.512487</td>\n",
       "      <td>11.678756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.420000</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.386000</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>0.027400</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>0.991460</td>\n",
       "      <td>3.308000</td>\n",
       "      <td>0.466000</td>\n",
       "      <td>12.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "quality                                                                 \n",
       "3             7.853333          0.517000     0.281000        5.140000   \n",
       "4             7.288889          0.457963     0.272315        4.153704   \n",
       "5             7.326801          0.389614     0.307722        5.804116   \n",
       "6             7.177257          0.313863     0.323583        5.549753   \n",
       "7             7.128962          0.288800     0.334764        4.731696   \n",
       "8             6.835233          0.291010     0.332539        5.382902   \n",
       "9             7.420000          0.298000     0.386000        4.120000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "quality                                                                   \n",
       "3         0.077033            39.216667            122.033333  0.995744   \n",
       "4         0.060056            20.636574            103.432870  0.994833   \n",
       "5         0.064666            30.237371            120.839102  0.995849   \n",
       "6         0.054157            31.165021            115.410790  0.994558   \n",
       "7         0.045272            30.422150            108.498610  0.993126   \n",
       "8         0.041124            34.533679            117.518135  0.992514   \n",
       "9         0.027400            33.400000            116.000000  0.991460   \n",
       "\n",
       "               pH  sulphates    alcohol  \n",
       "quality                                  \n",
       "3        3.257667   0.506333  10.215000  \n",
       "4        3.231620   0.505648  10.180093  \n",
       "5        3.212189   0.526403   9.837783  \n",
       "6        3.217726   0.532549  10.587553  \n",
       "7        3.228072   0.547025  11.386006  \n",
       "8        3.223212   0.512487  11.678756  \n",
       "9        3.308000   0.466000  12.180000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding mean but for groups on quality parameter\n",
    "wine_df.groupby('quality').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>red</th>\n",
       "      <td>8.360000</td>\n",
       "      <td>0.884500</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>2.635000</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>0.997464</td>\n",
       "      <td>3.398000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>9.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.333250</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>6.392500</td>\n",
       "      <td>0.054300</td>\n",
       "      <td>53.325000</td>\n",
       "      <td>170.600000</td>\n",
       "      <td>0.994884</td>\n",
       "      <td>3.187500</td>\n",
       "      <td>0.474500</td>\n",
       "      <td>10.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>red</th>\n",
       "      <td>7.779245</td>\n",
       "      <td>0.693962</td>\n",
       "      <td>0.174151</td>\n",
       "      <td>2.694340</td>\n",
       "      <td>0.090679</td>\n",
       "      <td>12.264151</td>\n",
       "      <td>36.245283</td>\n",
       "      <td>0.996542</td>\n",
       "      <td>3.381509</td>\n",
       "      <td>0.596415</td>\n",
       "      <td>10.265094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>7.129448</td>\n",
       "      <td>0.381227</td>\n",
       "      <td>0.304233</td>\n",
       "      <td>4.628221</td>\n",
       "      <td>0.050098</td>\n",
       "      <td>23.358896</td>\n",
       "      <td>125.279141</td>\n",
       "      <td>0.994277</td>\n",
       "      <td>3.182883</td>\n",
       "      <td>0.476135</td>\n",
       "      <td>10.152454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>red</th>\n",
       "      <td>8.167254</td>\n",
       "      <td>0.577041</td>\n",
       "      <td>0.243686</td>\n",
       "      <td>2.528855</td>\n",
       "      <td>0.092736</td>\n",
       "      <td>16.983847</td>\n",
       "      <td>56.513950</td>\n",
       "      <td>0.997104</td>\n",
       "      <td>3.304949</td>\n",
       "      <td>0.620969</td>\n",
       "      <td>9.899706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>6.933974</td>\n",
       "      <td>0.302011</td>\n",
       "      <td>0.337653</td>\n",
       "      <td>7.334969</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>36.432052</td>\n",
       "      <td>150.904598</td>\n",
       "      <td>0.995263</td>\n",
       "      <td>3.168833</td>\n",
       "      <td>0.482203</td>\n",
       "      <td>9.808840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>red</th>\n",
       "      <td>8.347179</td>\n",
       "      <td>0.497484</td>\n",
       "      <td>0.273824</td>\n",
       "      <td>2.477194</td>\n",
       "      <td>0.084956</td>\n",
       "      <td>15.711599</td>\n",
       "      <td>40.869906</td>\n",
       "      <td>0.996615</td>\n",
       "      <td>3.318072</td>\n",
       "      <td>0.675329</td>\n",
       "      <td>10.629519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>6.837671</td>\n",
       "      <td>0.260564</td>\n",
       "      <td>0.338025</td>\n",
       "      <td>6.441606</td>\n",
       "      <td>0.045217</td>\n",
       "      <td>35.650591</td>\n",
       "      <td>137.047316</td>\n",
       "      <td>0.993961</td>\n",
       "      <td>3.188599</td>\n",
       "      <td>0.491106</td>\n",
       "      <td>10.575372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>red</th>\n",
       "      <td>8.872362</td>\n",
       "      <td>0.403920</td>\n",
       "      <td>0.375176</td>\n",
       "      <td>2.720603</td>\n",
       "      <td>0.076588</td>\n",
       "      <td>14.045226</td>\n",
       "      <td>35.020101</td>\n",
       "      <td>0.996104</td>\n",
       "      <td>3.290754</td>\n",
       "      <td>0.741256</td>\n",
       "      <td>11.465913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>6.734716</td>\n",
       "      <td>0.262767</td>\n",
       "      <td>0.325625</td>\n",
       "      <td>5.186477</td>\n",
       "      <td>0.038191</td>\n",
       "      <td>34.125568</td>\n",
       "      <td>125.114773</td>\n",
       "      <td>0.992452</td>\n",
       "      <td>3.213898</td>\n",
       "      <td>0.503102</td>\n",
       "      <td>11.367936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>red</th>\n",
       "      <td>8.566667</td>\n",
       "      <td>0.423333</td>\n",
       "      <td>0.391111</td>\n",
       "      <td>2.577778</td>\n",
       "      <td>0.068444</td>\n",
       "      <td>13.277778</td>\n",
       "      <td>33.444444</td>\n",
       "      <td>0.995212</td>\n",
       "      <td>3.267222</td>\n",
       "      <td>0.767778</td>\n",
       "      <td>12.094444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>6.657143</td>\n",
       "      <td>0.277400</td>\n",
       "      <td>0.326514</td>\n",
       "      <td>5.671429</td>\n",
       "      <td>0.038314</td>\n",
       "      <td>36.720000</td>\n",
       "      <td>126.165714</td>\n",
       "      <td>0.992236</td>\n",
       "      <td>3.218686</td>\n",
       "      <td>0.486229</td>\n",
       "      <td>11.636000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>white</th>\n",
       "      <td>7.420000</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.386000</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>0.027400</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>0.991460</td>\n",
       "      <td>3.308000</td>\n",
       "      <td>0.466000</td>\n",
       "      <td>12.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "quality color                                                                 \n",
       "3       red         8.360000          0.884500     0.171000        2.635000   \n",
       "        white       7.600000          0.333250     0.336000        6.392500   \n",
       "4       red         7.779245          0.693962     0.174151        2.694340   \n",
       "        white       7.129448          0.381227     0.304233        4.628221   \n",
       "5       red         8.167254          0.577041     0.243686        2.528855   \n",
       "        white       6.933974          0.302011     0.337653        7.334969   \n",
       "6       red         8.347179          0.497484     0.273824        2.477194   \n",
       "        white       6.837671          0.260564     0.338025        6.441606   \n",
       "7       red         8.872362          0.403920     0.375176        2.720603   \n",
       "        white       6.734716          0.262767     0.325625        5.186477   \n",
       "8       red         8.566667          0.423333     0.391111        2.577778   \n",
       "        white       6.657143          0.277400     0.326514        5.671429   \n",
       "9       white       7.420000          0.298000     0.386000        4.120000   \n",
       "\n",
       "               chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "quality color                                                                   \n",
       "3       red     0.122500            11.000000             24.900000  0.997464   \n",
       "        white   0.054300            53.325000            170.600000  0.994884   \n",
       "4       red     0.090679            12.264151             36.245283  0.996542   \n",
       "        white   0.050098            23.358896            125.279141  0.994277   \n",
       "5       red     0.092736            16.983847             56.513950  0.997104   \n",
       "        white   0.051546            36.432052            150.904598  0.995263   \n",
       "6       red     0.084956            15.711599             40.869906  0.996615   \n",
       "        white   0.045217            35.650591            137.047316  0.993961   \n",
       "7       red     0.076588            14.045226             35.020101  0.996104   \n",
       "        white   0.038191            34.125568            125.114773  0.992452   \n",
       "8       red     0.068444            13.277778             33.444444  0.995212   \n",
       "        white   0.038314            36.720000            126.165714  0.992236   \n",
       "9       white   0.027400            33.400000            116.000000  0.991460   \n",
       "\n",
       "                     pH  sulphates    alcohol  \n",
       "quality color                                  \n",
       "3       red    3.398000   0.570000   9.955000  \n",
       "        white  3.187500   0.474500  10.345000  \n",
       "4       red    3.381509   0.596415  10.265094  \n",
       "        white  3.182883   0.476135  10.152454  \n",
       "5       red    3.304949   0.620969   9.899706  \n",
       "        white  3.168833   0.482203   9.808840  \n",
       "6       red    3.318072   0.675329  10.629519  \n",
       "        white  3.188599   0.491106  10.575372  \n",
       "7       red    3.290754   0.741256  11.465913  \n",
       "        white  3.213898   0.503102  11.367936  \n",
       "8       red    3.267222   0.767778  12.094444  \n",
       "        white  3.218686   0.486229  11.636000  \n",
       "9       white  3.308000   0.466000  12.180000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding mean but for groups on quality and color parameters\n",
    "wine_df.groupby(['quality','color']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality  color\n",
       "3        red      3.398000\n",
       "         white    3.187500\n",
       "4        red      3.381509\n",
       "         white    3.182883\n",
       "5        red      3.304949\n",
       "         white    3.168833\n",
       "6        red      3.318072\n",
       "         white    3.188599\n",
       "7        red      3.290754\n",
       "         white    3.213898\n",
       "8        red      3.267222\n",
       "         white    3.218686\n",
       "9        white    3.308000\n",
       "Name: pH, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding mean but for groups on quality and color parameters\n",
    "#setting INDEX as pH\n",
    "wine_df.groupby(['quality','color'])['pH'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.853333</td>\n",
       "      <td>0.517000</td>\n",
       "      <td>0.281000</td>\n",
       "      <td>5.140000</td>\n",
       "      <td>0.077033</td>\n",
       "      <td>39.216667</td>\n",
       "      <td>122.033333</td>\n",
       "      <td>0.995744</td>\n",
       "      <td>3.257667</td>\n",
       "      <td>0.506333</td>\n",
       "      <td>10.215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.288889</td>\n",
       "      <td>0.457963</td>\n",
       "      <td>0.272315</td>\n",
       "      <td>4.153704</td>\n",
       "      <td>0.060056</td>\n",
       "      <td>20.636574</td>\n",
       "      <td>103.432870</td>\n",
       "      <td>0.994833</td>\n",
       "      <td>3.231620</td>\n",
       "      <td>0.505648</td>\n",
       "      <td>10.180093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.326801</td>\n",
       "      <td>0.389614</td>\n",
       "      <td>0.307722</td>\n",
       "      <td>5.804116</td>\n",
       "      <td>0.064666</td>\n",
       "      <td>30.237371</td>\n",
       "      <td>120.839102</td>\n",
       "      <td>0.995849</td>\n",
       "      <td>3.212189</td>\n",
       "      <td>0.526403</td>\n",
       "      <td>9.837783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.177257</td>\n",
       "      <td>0.313863</td>\n",
       "      <td>0.323583</td>\n",
       "      <td>5.549753</td>\n",
       "      <td>0.054157</td>\n",
       "      <td>31.165021</td>\n",
       "      <td>115.410790</td>\n",
       "      <td>0.994558</td>\n",
       "      <td>3.217726</td>\n",
       "      <td>0.532549</td>\n",
       "      <td>10.587553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.128962</td>\n",
       "      <td>0.288800</td>\n",
       "      <td>0.334764</td>\n",
       "      <td>4.731696</td>\n",
       "      <td>0.045272</td>\n",
       "      <td>30.422150</td>\n",
       "      <td>108.498610</td>\n",
       "      <td>0.993126</td>\n",
       "      <td>3.228072</td>\n",
       "      <td>0.547025</td>\n",
       "      <td>11.386006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.835233</td>\n",
       "      <td>0.291010</td>\n",
       "      <td>0.332539</td>\n",
       "      <td>5.382902</td>\n",
       "      <td>0.041124</td>\n",
       "      <td>34.533679</td>\n",
       "      <td>117.518135</td>\n",
       "      <td>0.992514</td>\n",
       "      <td>3.223212</td>\n",
       "      <td>0.512487</td>\n",
       "      <td>11.678756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.420000</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.386000</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>0.027400</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>0.991460</td>\n",
       "      <td>3.308000</td>\n",
       "      <td>0.466000</td>\n",
       "      <td>12.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "quality                                                                 \n",
       "3             7.853333          0.517000     0.281000        5.140000   \n",
       "4             7.288889          0.457963     0.272315        4.153704   \n",
       "5             7.326801          0.389614     0.307722        5.804116   \n",
       "6             7.177257          0.313863     0.323583        5.549753   \n",
       "7             7.128962          0.288800     0.334764        4.731696   \n",
       "8             6.835233          0.291010     0.332539        5.382902   \n",
       "9             7.420000          0.298000     0.386000        4.120000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "quality                                                                   \n",
       "3         0.077033            39.216667            122.033333  0.995744   \n",
       "4         0.060056            20.636574            103.432870  0.994833   \n",
       "5         0.064666            30.237371            120.839102  0.995849   \n",
       "6         0.054157            31.165021            115.410790  0.994558   \n",
       "7         0.045272            30.422150            108.498610  0.993126   \n",
       "8         0.041124            34.533679            117.518135  0.992514   \n",
       "9         0.027400            33.400000            116.000000  0.991460   \n",
       "\n",
       "               pH  sulphates    alcohol  \n",
       "quality                                  \n",
       "3        3.257667   0.506333  10.215000  \n",
       "4        3.231620   0.505648  10.180093  \n",
       "5        3.212189   0.526403   9.837783  \n",
       "6        3.217726   0.532549  10.587553  \n",
       "7        3.228072   0.547025  11.386006  \n",
       "8        3.223212   0.512487  11.678756  \n",
       "9        3.308000   0.466000  12.180000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding mean but for groups on quality and setting it as index too\n",
    "wine_df.groupby('quality', as_index = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>residual sugar</th>\n",
       "      <th>volatile acidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.647500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.056667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1.185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>4.60</td>\n",
       "      <td>0.355000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>5.10</td>\n",
       "      <td>0.215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>9.50</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>10.60</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>11.10</td>\n",
       "      <td>0.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>15.10</td>\n",
       "      <td>0.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>15.55</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>16.20</td>\n",
       "      <td>0.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.543000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.414000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.343333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>8</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>8</td>\n",
       "      <td>8.10</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>8</td>\n",
       "      <td>8.30</td>\n",
       "      <td>0.415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>8</td>\n",
       "      <td>8.40</td>\n",
       "      <td>0.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>8</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>8</td>\n",
       "      <td>8.65</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>8</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>8</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>8</td>\n",
       "      <td>9.05</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>8</td>\n",
       "      <td>9.10</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>8</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>8</td>\n",
       "      <td>10.05</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>8</td>\n",
       "      <td>10.30</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>8</td>\n",
       "      <td>10.50</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>8</td>\n",
       "      <td>11.30</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>8</td>\n",
       "      <td>11.40</td>\n",
       "      <td>0.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>8</td>\n",
       "      <td>12.60</td>\n",
       "      <td>0.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>8</td>\n",
       "      <td>12.80</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>8</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>8</td>\n",
       "      <td>13.90</td>\n",
       "      <td>0.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>8</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>8</td>\n",
       "      <td>14.10</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>8</td>\n",
       "      <td>14.20</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>8</td>\n",
       "      <td>14.50</td>\n",
       "      <td>0.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>8</td>\n",
       "      <td>14.80</td>\n",
       "      <td>0.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>9</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>9</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>9</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>9</td>\n",
       "      <td>4.20</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>9</td>\n",
       "      <td>10.60</td>\n",
       "      <td>0.270000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>828 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     quality  residual sugar  volatile acidity\n",
       "0          3            0.70          0.230000\n",
       "1          3            1.15          0.330000\n",
       "2          3            1.20          0.647500\n",
       "3          3            1.40          0.170000\n",
       "4          3            1.50          0.440000\n",
       "5          3            1.55          0.250000\n",
       "6          3            1.60          0.590000\n",
       "7          3            1.80          0.760000\n",
       "8          3            2.00          0.490000\n",
       "9          3            2.10          1.056667\n",
       "10         3            2.20          0.580000\n",
       "11         3            2.90          0.260000\n",
       "12         3            3.40          1.020000\n",
       "13         3            3.50          0.240000\n",
       "14         3            4.25          1.185000\n",
       "15         3            4.60          0.355000\n",
       "16         3            5.10          0.215000\n",
       "17         3            5.70          0.875000\n",
       "18         3            8.50          0.240000\n",
       "19         3            9.50          0.200000\n",
       "20         3           10.60          0.640000\n",
       "21         3           11.00          0.320000\n",
       "22         3           11.10          0.230000\n",
       "23         3           15.10          0.260000\n",
       "24         3           15.55          0.550000\n",
       "25         3           16.20          0.260000\n",
       "26         4            0.70          0.220000\n",
       "27         4            0.80          0.543000\n",
       "28         4            0.90          0.414000\n",
       "29         4            1.00          0.343333\n",
       "..       ...             ...               ...\n",
       "798        8            7.80          0.220000\n",
       "799        8            8.10          0.170000\n",
       "800        8            8.30          0.415000\n",
       "801        8            8.40          0.310000\n",
       "802        8            8.60          0.360000\n",
       "803        8            8.65          0.350000\n",
       "804        8            8.80          0.450000\n",
       "805        8            9.00          0.340000\n",
       "806        8            9.05          0.250000\n",
       "807        8            9.10          0.440000\n",
       "808        8            9.20          0.220000\n",
       "809        8           10.05          0.360000\n",
       "810        8           10.30          0.120000\n",
       "811        8           10.50          0.360000\n",
       "812        8           11.30          0.500000\n",
       "813        8           11.40          0.210000\n",
       "814        8           12.60          0.180000\n",
       "815        8           12.80          0.170000\n",
       "816        8           13.30          0.240000\n",
       "817        8           13.90          0.190000\n",
       "818        8           14.00          0.150000\n",
       "819        8           14.10          0.170000\n",
       "820        8           14.20          0.200000\n",
       "821        8           14.50          0.325000\n",
       "822        8           14.80          0.470000\n",
       "823        9            1.60          0.360000\n",
       "824        9            2.00          0.240000\n",
       "825        9            2.20          0.260000\n",
       "826        9            4.20          0.360000\n",
       "827        9           10.60          0.270000\n",
       "\n",
       "[828 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding mean but for groups on quality & sugar parameters \n",
    "wine_df.groupby(['quality', 'residual sugar'], as_index=False)['volatile acidity'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Q1: Is a certain type of wine (red or white) associated with higher quality?**\n",
    "- For this question, 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.\n",
    "- White wine has higher average for quality\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "red      5.636023\n",
       "white    5.877909\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.groupby('color')['quality'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- QUESTION 1 OF 2\n",
    "\n",
    "- Is the mean quality of red wine greater than, less than, or equal to that of white wine? LESS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Q2: What level of acidity (pH value) receives the highest average rating?**\n",
    "-This question is more tricky because unlike color, which has clear categories you can group by (red and white) pH is a quantitative variable without clear categories. However, there is a simple fix to this. You can create a categorical variable from a quantitative variable by creating your own categories. Pandas' cut function let's you \"cut\" data in groups. Using this, create a new column called acidity_levels with these categories:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**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",
    "- Here, the data is being split at the 25th, 50th, and 75th percentile. Remember, you can get these numbers with Pandas' describe()! After you create these four categories, you'll be able to use groupby to get the mean quality rating for each acidity level.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What level of acidity receives the highest average rating?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.215307</td>\n",
       "      <td>0.339666</td>\n",
       "      <td>0.318633</td>\n",
       "      <td>5.443235</td>\n",
       "      <td>0.056034</td>\n",
       "      <td>30.525319</td>\n",
       "      <td>115.744574</td>\n",
       "      <td>0.994697</td>\n",
       "      <td>3.218501</td>\n",
       "      <td>0.531268</td>\n",
       "      <td>10.491801</td>\n",
       "      <td>5.818378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.296434</td>\n",
       "      <td>0.164636</td>\n",
       "      <td>0.145318</td>\n",
       "      <td>4.757804</td>\n",
       "      <td>0.035034</td>\n",
       "      <td>17.749400</td>\n",
       "      <td>56.521855</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.160787</td>\n",
       "      <td>0.148806</td>\n",
       "      <td>1.192712</td>\n",
       "      <td>0.873255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.992340</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>0.994890</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.065000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>0.996990</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    6497.000000       6497.000000  6497.000000     6497.000000   \n",
       "mean        7.215307          0.339666     0.318633        5.443235   \n",
       "std         1.296434          0.164636     0.145318        4.757804   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.400000          0.230000     0.250000        1.800000   \n",
       "50%         7.000000          0.290000     0.310000        3.000000   \n",
       "75%         7.700000          0.400000     0.390000        8.100000   \n",
       "max        15.900000          1.580000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  6497.000000          6497.000000           6497.000000  6497.000000   \n",
       "mean      0.056034            30.525319            115.744574     0.994697   \n",
       "std       0.035034            17.749400             56.521855     0.002999   \n",
       "min       0.009000             1.000000              6.000000     0.987110   \n",
       "25%       0.038000            17.000000             77.000000     0.992340   \n",
       "50%       0.047000            29.000000            118.000000     0.994890   \n",
       "75%       0.065000            41.000000            156.000000     0.996990   \n",
       "max       0.611000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  6497.000000  6497.000000  6497.000000  6497.000000  \n",
       "mean      3.218501     0.531268    10.491801     5.818378  \n",
       "std       0.160787     0.148806     1.192712     0.873255  \n",
       "min       2.720000     0.220000     8.000000     3.000000  \n",
       "25%       3.110000     0.430000     9.500000     5.000000  \n",
       "50%       3.210000     0.510000    10.300000     6.000000  \n",
       "75%       3.320000     0.600000    11.300000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     9.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the min, 25%, 50%, 75%, max pH values with Pandas describe\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",
    "wine_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Meduim</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           Meduim  \n",
       "3      9.8        6   red  Moderately High  \n",
       "4      9.4        5   red              Low  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\n",
    "# Labels for the four acidity level groups\n",
    "bin_names = [ 'High','Moderately High' ,'Meduim' ,'Low' ] # Name each acidity level category\n",
    "# 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": 19,
   "metadata": {},
   "outputs": [
    {
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       "    }\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acidity_levels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>7.543914</td>\n",
       "      <td>0.294683</td>\n",
       "      <td>0.370792</td>\n",
       "      <td>7.088876</td>\n",
       "      <td>0.055131</td>\n",
       "      <td>33.179965</td>\n",
       "      <td>129.897496</td>\n",
       "      <td>0.994708</td>\n",
       "      <td>3.029062</td>\n",
       "      <td>0.503937</td>\n",
       "      <td>10.330208</td>\n",
       "      <td>5.783343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderately High</th>\n",
       "      <td>7.365064</td>\n",
       "      <td>0.318551</td>\n",
       "      <td>0.340548</td>\n",
       "      <td>5.931984</td>\n",
       "      <td>0.054666</td>\n",
       "      <td>33.229154</td>\n",
       "      <td>126.815886</td>\n",
       "      <td>0.994697</td>\n",
       "      <td>3.164833</td>\n",
       "      <td>0.509300</td>\n",
       "      <td>10.391073</td>\n",
       "      <td>5.784540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meduim</th>\n",
       "      <td>7.143566</td>\n",
       "      <td>0.346751</td>\n",
       "      <td>0.313585</td>\n",
       "      <td>4.721159</td>\n",
       "      <td>0.055715</td>\n",
       "      <td>28.983995</td>\n",
       "      <td>111.182138</td>\n",
       "      <td>0.994476</td>\n",
       "      <td>3.267010</td>\n",
       "      <td>0.541287</td>\n",
       "      <td>10.610369</td>\n",
       "      <td>5.850832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>6.769949</td>\n",
       "      <td>0.403815</td>\n",
       "      <td>0.243901</td>\n",
       "      <td>3.848983</td>\n",
       "      <td>0.058777</td>\n",
       "      <td>26.327510</td>\n",
       "      <td>93.244917</td>\n",
       "      <td>0.994899</td>\n",
       "      <td>3.433348</td>\n",
       "      <td>0.574136</td>\n",
       "      <td>10.656057</td>\n",
       "      <td>5.859593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "acidity_levels                                                                  \n",
       "High                  7.543914          0.294683     0.370792        7.088876   \n",
       "Moderately High       7.365064          0.318551     0.340548        5.931984   \n",
       "Meduim                7.143566          0.346751     0.313585        4.721159   \n",
       "Low                   6.769949          0.403815     0.243901        3.848983   \n",
       "\n",
       "                 chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "acidity_levels                                                          \n",
       "High              0.055131            33.179965            129.897496   \n",
       "Moderately High   0.054666            33.229154            126.815886   \n",
       "Meduim            0.055715            28.983995            111.182138   \n",
       "Low               0.058777            26.327510             93.244917   \n",
       "\n",
       "                  density        pH  sulphates    alcohol   quality  \n",
       "acidity_levels                                                       \n",
       "High             0.994708  3.029062   0.503937  10.330208  5.783343  \n",
       "Moderately High  0.994697  3.164833   0.509300  10.391073  5.784540  \n",
       "Meduim           0.994476  3.267010   0.541287  10.610369  5.850832  \n",
       "Low              0.994899  3.433348   0.574136  10.656057  5.859593  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean quality of each acidity level with groupby\n",
    "wine_df.groupby('acidity_levels').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save changes for the next section\n",
    "wine_df.to_csv('winequality_edited.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### QUESTION 1 OF 2\n",
    "- Is the mean quality of red wine greater than, less than, or equal to that of white wine?\n",
    "Less\n",
    "#### QUESTION 2 OF 2\n",
    "- What level of acidity receives the highest average rating?\n",
    "Low\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a18d65908>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7q2pzYH1gyySbjm6TZM8kC5MsXLRo0RTLlSRN1lQD4Rq6D2+q6jbgRmDwQ3x9Hrgz6qRV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\nEmAgSJIaA0GSBMD/BxvRKeiQ+V44AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18c53e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#colors = ['red', 'white']\n",
    "color_means = wine_df.groupby('color')['quality'].mean()\n",
    "color_means.plot(kind = 'bar', title = 'Avg Wine Quality by Color', color='ry', alpha = .7)\n",
    "plt.xlabel('Color', fontsize=18)\n",
    "plt.ylabel('Quality', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality  color\n",
       "3        red        10\n",
       "         white      20\n",
       "4        red        53\n",
       "         white     163\n",
       "5        red       681\n",
       "         white    1457\n",
       "6        red       638\n",
       "         white    2198\n",
       "7        red       199\n",
       "         white     880\n",
       "8        red        18\n",
       "         white     175\n",
       "9        white       5\n",
       "Name: pH, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = wine_df.groupby(['quality', 'color']).count()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a19032da0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D3BsRf6yg/OVJ3xi3IHUNvA5cHxHfKqHstYAnI+IlSQL2BjYG7gVOjZJXka0y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18e9dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting \n",
    "counts = wine_df.groupby(['quality', 'color']).count()['pH']\n",
    "counts.plot(kind='bar', title='Counts by Quality and Color', color='ry')\n",
    "plt.xlabel('Quality & Color', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a19195cc0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tgDdWXM69nV6JqFdbIPVMfoCB9Q+qH4RvHtDpAfjq1BaoV3vqtt10ZR3wnkmX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\nM7PSHEzMzKy0/w81D4K3g/WlgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19120748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting \n",
    "totals = wine_df.groupby('color').count()['pH']\n",
    "proportions = counts / totals\n",
    "counts.plot(kind='bar', title='Proportion by Quality and Color', color='ry')\n",
    "plt.xlabel('Quality & Color', fontsize=16)\n",
    "plt.ylabel('Proportion', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}