wine_visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting with Matplotlib\n",
    "Use Matplotlib to create bar charts that visualize the conclusions you made with groupby and query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary packages and load `winequality_edited.csv`\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "% matplotlib inline\n",
    "\n",
    "df = pd.read_csv('winequality_edited.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### #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. This first one is filled out for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use query to select each group and get its mean quality\n",
    "median = df['alcohol'].median()\n",
    "low = df.query('alcohol < {}'.format(median))\n",
    "high = df.query('alcohol >= {}'.format(median))\n",
    "\n",
    "mean_quality_low = low['quality'].mean()\n",
    "mean_quality_high = high['quality'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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crVHS3pImSZouaYakr/cypg5Jh/Zm30IZp0havi9lWP044dtgcwjpS2OD6ss4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\nE074ZmY14YRvZlYTTvhmZjXx/7NU1K2rbBexAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f182c1647b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart with proper labels\n",
    "locations = [1, 2]\n",
    "heights = [mean_quality_low, mean_quality_high]\n",
    "labels = ['Low', 'High']\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": [
    "### #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": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use query to select each group and get its mean quality\n",
    "median_sugar = df['residual_sugar'].median()\n",
    "low_sugar = df.query('residual_sugar < {}'.format(median_sugar))\n",
    "high_sugar = df.query('residual_sugar >= {}'.format(median_sugar))\n",
    "\n",
    "low_sugar_mean_quality = low_sugar['quality'].mean()\n",
    "low_sugar_mean_quality\n",
    "high_sugar_mean_quality = high_sugar['quality'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8278287461773699"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_sugar_mean_quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f182c164c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart with proper labels\n",
    "locations_sugar = [1,2]\n",
    "points = [low_sugar_mean_quality, high_sugar_mean_quality]\n",
    "labels_sugar = ['Low', 'High']\n",
    "plt.bar(locations_sugar, points, tick_label=labels_sugar)\n",
    "plt.title('Wine Quality for Sugar Content')\n",
    "plt.xlabel('Sugar Content')\n",
    "plt.ylabel('Mean Quality Rating'); #Include semi-colon to avoid text being printed \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### #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": 9,
   "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",
       "    </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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use groupby to get the mean quality for each acidity level\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bin_edges = [ 2.72, 3.11,3.21 , 3.32,4.01 ] # Filling this list with five values of pH\n",
    "bin_names = [ 'High','Moderately High' ,'Medium' ,'Low' ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['acidity_levels'] = pd.cut(df['pH'], bin_edges, labels=bin_names)\n",
    "\n",
    "# Checks for successful creation of this column\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acidity_levels\n",
       "High               5.783343\n",
       "Moderately High    5.784540\n",
       "Meduim             5.850832\n",
       "Low                5.859593\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean quality of each acidity level with groupby\n",
    "quality_acidity_mean = []\n",
    "quality_acidity_mean = df.groupby('acidity_levels').mean()['quality']\n",
    "quality_acidity_mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "acidity_mean = df.groupby('acidity_levels').mean()['pH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17ffe22048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart with proper labels\n",
    "locations_pH = [1,2,3,4]\n",
    "\n",
    "#points = [low_sugar_mean_quality, high_sugar_mean_quality]\n",
    "#plt.bar(locations_sugar, points, tick_label=labels_sugar)\n",
    "\n",
    "plt.bar(locations_pH,quality_acidity_mean, tick_label=bin_names)\n",
    "plt.title('Acidity vs Quality')\n",
    "plt.xlabel('Acidity')\n",
    "plt.ylabel('Quality');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus: Create a line plot for the data in #3\n",
    "You can use pyplot's [plot](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fjl8gact0+HuSnpZ0P7Br0ec7QdJYYFtgWvp5vizppwXzfUXS//9AO9s6nlr3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\ntZvG62UQJdAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17ffd548d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y=quality_acidity_mean, x=acidity_mean,color='grbk')\n",
    "plt.xlabel('Average Acidity')\n",
    "plt.ylabel('Average Quality')\n",
    "plt.title('Quality vs Acidity');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this with the bar chart. How might showing this visual instead of the bar chart affect someone's conclusion about this data?\n",
    "- Bar charts seem almost at same heights as the variances are very slight.\n",
    "- Scatter plot shows the barely discernible differences better "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acidity_levels\n",
       "High               5.783343\n",
       "Moderately High    5.784540\n",
       "Meduim             5.850832\n",
       "Low                5.859593\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quality_acidity_mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i/l7fPmbFZvGaLdz81FI+Wn4Yowf0zDscs2YpS6d37VnykHRS7TkyzIpVRHD1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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17ffbeb4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([5.7833,5.7845, 5.8508, 5.8595])\n",
    "plt.ylabel('Quality Mean for Acidity')\n",
    "plt.xlabel('Acidity Levels')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this line plot, we see clearly that quality rises as does acidity."
   ]
  },
  {
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}