MatPlotting some stuff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "\n",
       "   alcohol  quality  color  \n",
       "0      8.8        6  white  \n",
       "1      9.5        6  white  \n",
       "2     10.1        6  white  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import # Load `winequality_edited.csv`\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "% matplotlib inline\n",
    "\n",
    "df = pd.read_csv('winemerged.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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crVHS3pImSZouaYakr/cypg5Jh/Zm30IZp0havi9lWP044dtgcwjpS2OD6ss4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\nE074ZmY14YRvZlYTTvhmZjXx/7NU1K2rbBexAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbaa66d8>"
      ]
     },
     "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": "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",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual_sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "\n",
       "   alcohol  quality  color  \n",
       "0      8.8        6  white  \n",
       "1      9.5        6  white  \n",
       "2     10.1        6  white  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use query to select each group and get its mean quality\n",
    "df = df.rename(columns={'residual sugar': 'residual_sugar'})\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.443235339387428"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a bar chart one with low residual sugar and one bar for high residual sugar wine samples\n",
    "mean_residual_sugar = df['residual_sugar'].mean()\n",
    "mean_residual_sugar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use query to select each group and get its mean quality\n",
    "median_residual_sugar = df['residual_sugar'].median()\n",
    "low_residual_sugar = df.query('residual_sugar < {}'.format(median_residual_sugar))\n",
    "high_residual_sugar = df.query('residual_sugar >= {}'.format(median_residual_sugar))\n",
    "\n",
    "mean_low_residual_sugar = low_residual_sugar['quality'].mean()\n",
    "mean_high_residual_sugar = high_residual_sugar['quality'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qncLj2cBihelOX/8W27bKA95X56N91S38cm4G9s79g0OBLYC78rLbgWPzOreQ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\nd6Sp1I/1rgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf583c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart with proper labels\n",
    "locations = [1, 2]\n",
    "heights = [mean_residual_sugar, mean_high_residual_sugar]\n",
    "labels = ['low_residual_sugar', 'high_residual_sugar']\n",
    "plt.bar(locations, heights, tick_label=labels)\n",
    "plt.title('Average Quality Ratings by Residual Sugar')\n",
    "plt.xlabel('Residual Sugar')\n",
    "plt.ylabel('Average Quality Rating');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbcbab00>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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66SZiY2P5+OOP+fvf/07nzp0pV64cKSkpAJw7dy7LvFm5N77yyis9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbcbab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = df.groupby(['fixed acidity']).mean()\n",
    "counts.head(3)\n",
    "counts.plot(kind='bar',title=\"Mean by Wine's Fixed Acidity\", color='red',alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd4c1e80>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FpSRZuRJq13Z07pUrq/CVt4Te1cjYvXvhtdfURSYgAHbu9M65DN6nlOrRuysx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\nwfH/AQfOZ0eUDiwcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf192e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = df.groupby(['fixed acidity']).mean()\n",
    "counts.head(3)\n",
    "counts.plot(kind='line',title=\"Mean by Wine's Fixed Acidity\", color='red',alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}