lab_4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lab_4.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ruhullahil/gcolab/blob/master/lab_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51NzQMMiw8c4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzsADo3qwRLt",
        "colab_type": "code",
        "outputId": "a055ebee-0368-41bf-d19a-b122e0ed27ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "#g = pd.read_csv(filename, sep=';').groupby('')\n",
        "#d = {g:x for g,x in pd.DataFrame.groupby('The_evil_column')}\n",
        "wine=pd.read_csv('winequality-red.csv',delimiter=';')\n",
        "\n",
        "wine.tail()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "1594            6.2             0.600         0.08  ...       0.58     10.5        5\n",
              "1595            5.9             0.550         0.10  ...       0.76     11.2        6\n",
              "1596            6.3             0.510         0.13  ...       0.75     11.0        6\n",
              "1597            5.9             0.645         0.12  ...       0.71     10.2        5\n",
              "1598            6.0             0.310         0.47  ...       0.66     11.0        6\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTAhV0-Z2SYt",
        "colab_type": "code",
        "outputId": "01ebf942-418b-4ddc-9802-d101fd953784",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "data = wine.replace('?',np.NaN)\n",
        "print('Number of instances = %d' % (data.shape[0]))\n",
        "print('Number of attributes = %d' % (data.shape[1]))\n",
        "print('Number of missing values:')\n",
        "for col in data.columns:\n",
        " print('\\t%s: %d' % (col,data[col].isna().sum()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of instances = 1599\n",
            "Number of attributes = 12\n",
            "Number of missing values:\n",
            "\tfixed acidity: 0\n",
            "\tvolatile acidity: 0\n",
            "\tcitric acid: 0\n",
            "\tresidual sugar: 0\n",
            "\tchlorides: 0\n",
            "\tfree sulfur dioxide: 0\n",
            "\ttotal sulfur dioxide: 0\n",
            "\tdensity: 0\n",
            "\tpH: 0\n",
            "\tsulphates: 0\n",
            "\talcohol: 0\n",
            "\tquality: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYUl6PTB3H_U",
        "colab_type": "code",
        "outputId": "692a4717-04c1-4dc4-97b9-60731d0dc193",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        }
      },
      "source": [
        " %matplotlib inline\n",
        "data2 = data.drop(['citric acid'],axis=1)\n",
        "data2['alcohol'] = pd.to_numeric(data2['alcohol'])\n",
        "data2.boxplot(figsize=(20,3))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f084c7605f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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E3B0RQ54QRsSXy7j3RsSyhv7HRMTtEXFnRKwp/RrL03T6A5nIiNgV+HvgdQNX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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOwPSCa9C1uy",
        "colab_type": "code",
        "outputId": "af557146-10fb-49e2-bf9f-abb337e62478",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        }
      },
      "source": [
        "Z = (data2-data2.mean())/data2.std()\n",
        "Z[20:40]\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>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>20</th>\n",
              "      <td>0.333332</td>\n",
              "      <td>-1.719094</td>\n",
              "      <td>-0.524002</td>\n",
              "      <td>-0.222383</td>\n",
              "      <td>1.254769</td>\n",
              "      <td>0.411372</td>\n",
              "      <td>0.028252</td>\n",
              "      <td>0.510970</td>\n",
              "      <td>-0.756009</td>\n",
              "      <td>-0.959946</td>\n",
              "      <td>0.450707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>-0.413324</td>\n",
              "      <td>-0.769690</td>\n",
              "      <td>-0.169374</td>\n",
              "      <td>-0.116148</td>\n",
              "      <td>0.681164</td>\n",
              "      <td>0.745766</td>\n",
              "      <td>0.770039</td>\n",
              "      <td>1.353012</td>\n",
              "      <td>-0.048074</td>\n",
              "      <td>-0.678432</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>-0.241019</td>\n",
              "      <td>-0.546301</td>\n",
              "      <td>-0.665853</td>\n",
              "      <td>0.393782</td>\n",
              "      <td>-0.561648</td>\n",
              "      <td>-0.287816</td>\n",
              "      <td>-0.077718</td>\n",
              "      <td>-0.914026</td>\n",
              "      <td>1.485786</td>\n",
              "      <td>-0.866108</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.103591</td>\n",
              "      <td>-0.211217</td>\n",
              "      <td>-0.169374</td>\n",
              "      <td>-0.073654</td>\n",
              "      <td>-0.657248</td>\n",
              "      <td>0.624168</td>\n",
              "      <td>0.028252</td>\n",
              "      <td>-0.914026</td>\n",
              "      <td>-0.756009</td>\n",
              "      <td>-0.959946</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>-0.815370</td>\n",
              "      <td>-0.713843</td>\n",
              "      <td>-0.098449</td>\n",
              "      <td>-0.052407</td>\n",
              "      <td>0.489962</td>\n",
              "      <td>-0.196617</td>\n",
              "      <td>0.028252</td>\n",
              "      <td>0.770060</td>\n",
              "      <td>-0.166063</td>\n",
              "      <td>-0.678432</td>\n",
              "      <td>0.450707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>-1.159980</td>\n",
              "      <td>-0.769690</td>\n",
              "      <td>-0.807705</td>\n",
              "      <td>-0.158642</td>\n",
              "      <td>-0.466047</td>\n",
              "      <td>-0.713408</td>\n",
              "      <td>-0.660550</td>\n",
              "      <td>0.187107</td>\n",
              "      <td>-0.579025</td>\n",
              "      <td>-1.053784</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>-0.413324</td>\n",
              "      <td>-0.657996</td>\n",
              "      <td>-0.524002</td>\n",
              "      <td>-0.158642</td>\n",
              "      <td>-1.135253</td>\n",
              "      <td>-1.078202</td>\n",
              "      <td>-0.289657</td>\n",
              "      <td>-0.201528</td>\n",
              "      <td>-0.402042</td>\n",
              "      <td>-0.866108</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>-0.241019</td>\n",
              "      <td>-0.546301</td>\n",
              "      <td>-0.665853</td>\n",
              "      <td>0.393782</td>\n",
              "      <td>-0.561648</td>\n",
              "      <td>-0.287816</td>\n",
              "      <td>-0.077718</td>\n",
              "      <td>-0.914026</td>\n",
              "      <td>1.485786</td>\n",
              "      <td>-0.866108</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>-0.700500</td>\n",
              "      <td>1.017423</td>\n",
              "      <td>-0.453077</td>\n",
              "      <td>-0.158642</td>\n",
              "      <td>-0.179244</td>\n",
              "      <td>-0.348615</td>\n",
              "      <td>0.240191</td>\n",
              "      <td>1.029150</td>\n",
              "      <td>-0.638020</td>\n",
              "      <td>-0.959946</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>-0.298454</td>\n",
              "      <td>0.654416</td>\n",
              "      <td>-0.382151</td>\n",
              "      <td>-0.116148</td>\n",
              "      <td>-0.752849</td>\n",
              "      <td>-0.926204</td>\n",
              "      <td>-0.183687</td>\n",
              "      <td>0.446197</td>\n",
              "      <td>-0.402042</td>\n",
              "      <td>-0.584594</td>\n",
              "      <td>0.450707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>-0.930240</td>\n",
              "      <td>0.821958</td>\n",
              "      <td>-0.098449</td>\n",
              "      <td>0.032582</td>\n",
              "      <td>0.107558</td>\n",
              "      <td>1.080160</td>\n",
              "      <td>-0.501596</td>\n",
              "      <td>0.251880</td>\n",
              "      <td>-0.697015</td>\n",
              "      <td>-0.303081</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>-0.815370</td>\n",
              "      <td>0.877805</td>\n",
              "      <td>-0.027523</td>\n",
              "      <td>0.372535</td>\n",
              "      <td>0.585563</td>\n",
              "      <td>-0.287816</td>\n",
              "      <td>-0.077718</td>\n",
              "      <td>0.964377</td>\n",
              "      <td>-0.520031</td>\n",
              "      <td>0.166109</td>\n",
              "      <td>0.450707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>-0.011279</td>\n",
              "      <td>0.710263</td>\n",
              "      <td>-0.169374</td>\n",
              "      <td>-0.094901</td>\n",
              "      <td>-0.083643</td>\n",
              "      <td>2.022543</td>\n",
              "      <td>-0.077718</td>\n",
              "      <td>-0.914026</td>\n",
              "      <td>0.010921</td>\n",
              "      <td>-0.584594</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>-0.815370</td>\n",
              "      <td>0.431027</td>\n",
              "      <td>5.788377</td>\n",
              "      <td>-0.307372</td>\n",
              "      <td>2.306378</td>\n",
              "      <td>1.110559</td>\n",
              "      <td>1.352872</td>\n",
              "      <td>0.899605</td>\n",
              "      <td>-0.815004</td>\n",
              "      <td>-0.959946</td>\n",
              "      <td>0.450707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>-1.791766</td>\n",
              "      <td>-1.160621</td>\n",
              "      <td>-0.524002</td>\n",
              "      <td>0.330041</td>\n",
              "      <td>-0.274845</td>\n",
              "      <td>0.107377</td>\n",
              "      <td>-0.554581</td>\n",
              "      <td>0.446197</td>\n",
              "      <td>-0.638020</td>\n",
              "      <td>-1.147622</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>-0.298454</td>\n",
              "      <td>0.654416</td>\n",
              "      <td>2.100245</td>\n",
              "      <td>-0.031160</td>\n",
              "      <td>-1.039652</td>\n",
              "      <td>-0.865405</td>\n",
              "      <td>0.981978</td>\n",
              "      <td>0.575742</td>\n",
              "      <td>-0.638020</td>\n",
              "      <td>-0.772270</td>\n",
              "      <td>0.450707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>-0.298454</td>\n",
              "      <td>0.403103</td>\n",
              "      <td>-0.098449</td>\n",
              "      <td>-0.031160</td>\n",
              "      <td>-1.230854</td>\n",
              "      <td>-0.956604</td>\n",
              "      <td>0.399145</td>\n",
              "      <td>0.705287</td>\n",
              "      <td>-0.343047</td>\n",
              "      <td>0.353785</td>\n",
              "      <td>0.450707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>-0.126149</td>\n",
              "      <td>-0.825538</td>\n",
              "      <td>-0.311225</td>\n",
              "      <td>-0.456101</td>\n",
              "      <td>-0.274845</td>\n",
              "      <td>-0.500612</td>\n",
              "      <td>0.028252</td>\n",
              "      <td>-0.525391</td>\n",
              "      <td>0.423883</td>\n",
              "      <td>-0.678432</td>\n",
              "      <td>1.688991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>-1.504591</td>\n",
              "      <td>3.363010</td>\n",
              "      <td>-0.736779</td>\n",
              "      <td>1.796089</td>\n",
              "      <td>-0.848450</td>\n",
              "      <td>-0.835006</td>\n",
              "      <td>-1.455322</td>\n",
              "      <td>1.223467</td>\n",
              "      <td>-1.050982</td>\n",
              "      <td>-0.584594</td>\n",
              "      <td>-2.025860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>-0.585629</td>\n",
              "      <td>-0.434607</td>\n",
              "      <td>2.383948</td>\n",
              "      <td>-0.286125</td>\n",
              "      <td>-0.370446</td>\n",
              "      <td>1.232157</td>\n",
              "      <td>0.558100</td>\n",
              "      <td>0.122335</td>\n",
              "      <td>1.013829</td>\n",
              "      <td>0.072271</td>\n",
              "      <td>-0.787576</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    fixed acidity  volatile acidity  ...   alcohol   quality\n",
              "20       0.333332         -1.719094  ... -0.959946  0.450707\n",
              "21      -0.413324         -0.769690  ... -0.678432 -0.787576\n",
              "22      -0.241019         -0.546301  ... -0.866108 -0.787576\n",
              "23       0.103591         -0.211217  ... -0.959946 -0.787576\n",
              "24      -0.815370         -0.713843  ... -0.678432  0.450707\n",
              "25      -1.159980         -0.769690  ... -1.053784 -0.787576\n",
              "26      -0.413324         -0.657996  ... -0.866108 -0.787576\n",
              "27      -0.241019         -0.546301  ... -0.866108 -0.787576\n",
              "28      -0.700500          1.017423  ... -0.959946 -0.787576\n",
              "29      -0.298454          0.654416  ... -0.584594  0.450707\n",
              "30      -0.930240          0.821958  ... -0.303081 -0.787576\n",
              "31      -0.815370          0.877805  ...  0.166109  0.450707\n",
              "32      -0.011279          0.710263  ... -0.584594 -0.787576\n",
              "33      -0.815370          0.431027  ... -0.959946  0.450707\n",
              "34      -1.791766         -1.160621  ... -1.147622 -0.787576\n",
              "35      -0.298454          0.654416  ... -0.772270  0.450707\n",
              "36      -0.298454          0.403103  ...  0.353785  0.450707\n",
              "37      -0.126149         -0.825538  ... -0.678432  1.688991\n",
              "38      -1.504591          3.363010  ... -0.584594 -2.025860\n",
              "39      -0.585629         -0.434607  ...  0.072271 -0.787576\n",
              "\n",
              "[20 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVcxMZpQDxdH",
        "colab_type": "code",
        "outputId": "40a1b5e3-aca8-499e-a3bc-cbb76e427036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('Number of rows before discarding outliers = %d' % (Z.shape[0]))\n",
        "Z2 = Z.loc[((Z >-.1).sum(axis=1)==9) & ((Z <=1).sum(axis=1)==9),:]\n",
        "print('Number of rows after discarding missing values = %d' % (Z2.shape[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows before discarding outliers = 1599\n",
            "Number of rows after discarding missing values = 11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5VPukjb6IlZ",
        "colab_type": "code",
        "outputId": "8783d891-338b-4ac1-b739-45a45f1b47e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "#data3.index = pd.to_numeric(data2['chlorides'])\n",
        "data3 = data2['pH']\n",
        "ax = data3.plot(kind='line',figsize=(15,3))\n",
        "ax.set_title('Aggrigation (variance = %.4f)' % (data3.var()))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Aggrigation (variance = 0.0238)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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R2AHc8vHrsP8stv+pzZMDGGl/PgxAXa4KmDEimUieJbXuXiO/NyAIgiAIgiAGJBQCQ/QH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\nKIJ/XbIQK8a4WtOwc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2Bplj+FNSZegB5DhUimY0aPpfxyKoAH7H/rd1QgEEJWAviYUvqK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\n/wEA3KaPT4BjcQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9T8ZBNgk_30L",
        "colab_type": "code",
        "outputId": "34600096-ec17-4987-e0cc-96329d5f8a73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        }
      },
      "source": [
        "sample = data.sample(frac=0.01, replace=True, random_state=1)\n",
        "sample"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1061</th>\n",
              "      <td>9.1</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.071</td>\n",
              "      <td>7.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.99462</td>\n",
              "      <td>3.21</td>\n",
              "      <td>0.69</td>\n",
              "      <td>12.5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.90</td>\n",
              "      <td>0.097</td>\n",
              "      <td>14.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.99675</td>\n",
              "      <td>3.37</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1096</th>\n",
              "      <td>6.6</td>\n",
              "      <td>0.725</td>\n",
              "      <td>0.09</td>\n",
              "      <td>5.50</td>\n",
              "      <td>0.117</td>\n",
              "      <td>9.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.99655</td>\n",
              "      <td>3.35</td>\n",
              "      <td>0.49</td>\n",
              "      <td>10.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>905</th>\n",
              "      <td>9.2</td>\n",
              "      <td>0.580</td>\n",
              "      <td>0.20</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.081</td>\n",
              "      <td>15.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0.99800</td>\n",
              "      <td>3.23</td>\n",
              "      <td>0.59</td>\n",
              "      <td>9.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.490</td>\n",
              "      <td>0.18</td>\n",
              "      <td>2.70</td>\n",
              "      <td>0.069</td>\n",
              "      <td>13.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99670</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0.48</td>\n",
              "      <td>9.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>847</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.680</td>\n",
              "      <td>0.16</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.078</td>\n",
              "      <td>12.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.99770</td>\n",
              "      <td>3.50</td>\n",
              "      <td>0.70</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>960</th>\n",
              "      <td>8.5</td>\n",
              "      <td>0.470</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.90</td>\n",
              "      <td>0.058</td>\n",
              "      <td>18.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.99518</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.85</td>\n",
              "      <td>11.1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>5.2</td>\n",
              "      <td>0.340</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.050</td>\n",
              "      <td>27.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.99160</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0.79</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>6.1</td>\n",
              "      <td>0.380</td>\n",
              "      <td>0.15</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.072</td>\n",
              "      <td>6.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.99550</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.57</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>7.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.18</td>\n",
              "      <td>2.10</td>\n",
              "      <td>0.070</td>\n",
              "      <td>12.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.99768</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.73</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>508</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.590</td>\n",
              "      <td>0.31</td>\n",
              "      <td>2.20</td>\n",
              "      <td>0.090</td>\n",
              "      <td>26.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0.99940</td>\n",
              "      <td>3.18</td>\n",
              "      <td>0.63</td>\n",
              "      <td>10.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1414</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.320</td>\n",
              "      <td>0.59</td>\n",
              "      <td>2.20</td>\n",
              "      <td>0.077</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.99940</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.78</td>\n",
              "      <td>9.6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>7.5</td>\n",
              "      <td>0.610</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.90</td>\n",
              "      <td>0.073</td>\n",
              "      <td>24.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>0.99612</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.53</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1202</th>\n",
              "      <td>8.6</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.068</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.99516</td>\n",
              "      <td>3.35</td>\n",
              "      <td>0.69</td>\n",
              "      <td>11.7</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1300</th>\n",
              "      <td>5.2</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.15</td>\n",
              "      <td>0.080</td>\n",
              "      <td>15.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.99444</td>\n",
              "      <td>3.78</td>\n",
              "      <td>0.61</td>\n",
              "      <td>12.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1278</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.715</td>\n",
              "      <td>0.22</td>\n",
              "      <td>2.30</td>\n",
              "      <td>0.075</td>\n",
              "      <td>13.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0.99688</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.54</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "1061            9.1             0.400         0.50  ...       0.69     12.5        8\n",
              "235             7.2             0.630         0.00  ...       0.58      9.0        6\n",
              "1096            6.6             0.725         0.09  ...       0.49     10.8        6\n",
              "905             9.2             0.580         0.20  ...       0.59      9.5        5\n",
              "715             7.2             0.490         0.18  ...       0.48      9.2        6\n",
              "847             7.4             0.680         0.16  ...       0.70      9.9        6\n",
              "960             8.5             0.470         0.27  ...       0.85     11.1        6\n",
              "144             5.2             0.340         0.00  ...       0.79     14.0        6\n",
              "129             6.1             0.380         0.15  ...       0.57      9.4        5\n",
              "749             7.3             0.510         0.18  ...       0.73      9.5        6\n",
              "508            10.0             0.590         0.31  ...       0.63     10.2        6\n",
              "1414           10.0             0.320         0.59  ...       0.78      9.6        5\n",
              "1305            7.5             0.610         0.26  ...       0.53      9.8        5\n",
              "1202            8.6             0.420         0.39  ...       0.69     11.7        8\n",
              "1300            5.2             0.645         0.00  ...       0.61     12.5        6\n",
              "1278            8.0             0.715         0.22  ...       0.54      9.5        6\n",
              "\n",
              "[16 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    }
  ]
}