lecture3.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/wjdsbs/DAP__2022/blob/main/lecture3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
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      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import ttest_1samp\n",
        "from scipy.stats import ttest_ind"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N73ib2szF4x2"
      },
      "source": [
        "# Cars93 data\n",
        "In this lab, we would like to explore data about 93 number of vehicles.\n"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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        "id": "U-urDSTR9o76",
        "outputId": "93ddcb2a-c049-4bb0-f0e3-b17a885a4d3f"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')\n",
        "df"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Manufacturer    Model     Type  Min.Price  Price  Max.Price  MPG.city  \\\n",
              "0         Acura  Integra    Small       12.9   15.9       18.8      25.0   \n",
              "1           NaN   Legend  Midsize       29.2   33.9       38.7      18.0   \n",
              "2          Audi       90  Compact       25.9   29.1       32.3      20.0   \n",
              "3          Audi      100  Midsize        NaN   37.7       44.6      19.0   \n",
              "4           BMW     535i  Midsize        NaN   30.0        NaN      22.0   \n",
              "..          ...      ...      ...        ...    ...        ...       ...   \n",
              "88   Volkswagen  Eurovan      Van       16.6   19.7       22.7      17.0   \n",
              "89   Volkswagen   Passat  Compact       17.6   20.0       22.4      21.0   \n",
              "90   Volkswagen  Corrado   Sporty       22.9   23.3       23.7      18.0   \n",
              "91        Volvo      240  Compact       21.8   22.7       23.5      21.0   \n",
              "92          NaN      850  Midsize       24.8   26.7       28.5      20.0   \n",
              "\n",
              "    MPG.highway             AirBags DriveTrain  ... Passengers  Length  \\\n",
              "0          31.0                None      Front  ...        5.0   177.0   \n",
              "1          25.0  Driver & Passenger      Front  ...        5.0   195.0   \n",
              "2          26.0         Driver only      Front  ...        5.0   180.0   \n",
              "3          26.0  Driver & Passenger        NaN  ...        6.0   193.0   \n",
              "4          30.0                 NaN       Rear  ...        4.0   186.0   \n",
              "..          ...                 ...        ...  ...        ...     ...   \n",
              "88         21.0                None      Front  ...        7.0   187.0   \n",
              "89         30.0                None      Front  ...        5.0   180.0   \n",
              "90         25.0                None      Front  ...        4.0   159.0   \n",
              "91         28.0         Driver only       Rear  ...        5.0   190.0   \n",
              "92         28.0  Driver & Passenger      Front  ...        5.0   184.0   \n",
              "\n",
              "    Wheelbase  Width  Turn.circle Rear.seat.room  Luggage.room  Weight  \\\n",
              "0       102.0   68.0         37.0           26.5           NaN  2705.0   \n",
              "1       115.0   71.0         38.0           30.0          15.0  3560.0   \n",
              "2       102.0   67.0         37.0           28.0          14.0  3375.0   \n",
              "3       106.0    NaN         37.0           31.0          17.0  3405.0   \n",
              "4       109.0   69.0         39.0           27.0          13.0  3640.0   \n",
              "..        ...    ...          ...            ...           ...     ...   \n",
              "88      115.0   72.0         38.0           34.0           NaN  3960.0   \n",
              "89      103.0   67.0         35.0           31.5          14.0  2985.0   \n",
              "90       97.0   66.0         36.0           26.0          15.0  2810.0   \n",
              "91      104.0   67.0         37.0           29.5          14.0  2985.0   \n",
              "92      105.0   69.0         38.0           30.0          15.0  3245.0   \n",
              "\n",
              "     Origin                Make  \n",
              "0   non-USA       Acura Integra  \n",
              "1   non-USA        Acura Legend  \n",
              "2   non-USA             Audi 90  \n",
              "3   non-USA            Audi 100  \n",
              "4   non-USA            BMW 535i  \n",
              "..      ...                 ...  \n",
              "88      NaN  Volkswagen Eurovan  \n",
              "89  non-USA   Volkswagen Passat  \n",
              "90  non-USA  Volkswagen Corrado  \n",
              "91  non-USA           Volvo 240  \n",
              "92  non-USA           Volvo 850  \n",
              "\n",
              "[93 rows x 27 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Manufacturer</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>Min.Price</th>\n",
              "      <th>Price</th>\n",
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              "      <th>AirBags</th>\n",
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              "      <th>Luggage.room</th>\n",
              "      <th>Weight</th>\n",
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              "      <th>Make</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acura</td>\n",
              "      <td>Integra</td>\n",
              "      <td>Small</td>\n",
              "      <td>12.9</td>\n",
              "      <td>15.9</td>\n",
              "      <td>18.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2705.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Acura Integra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Legend</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>29.2</td>\n",
              "      <td>33.9</td>\n",
              "      <td>38.7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3560.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Acura Legend</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Audi</td>\n",
              "      <td>90</td>\n",
              "      <td>Compact</td>\n",
              "      <td>25.9</td>\n",
              "      <td>29.1</td>\n",
              "      <td>32.3</td>\n",
              "      <td>20.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3375.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Audi 90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Audi</td>\n",
              "      <td>100</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.7</td>\n",
              "      <td>44.6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3405.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Audi 100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BMW</td>\n",
              "      <td>535i</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>3640.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>BMW 535i</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Eurovan</td>\n",
              "      <td>Van</td>\n",
              "      <td>16.6</td>\n",
              "      <td>19.7</td>\n",
              "      <td>22.7</td>\n",
              "      <td>17.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3960.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Volkswagen Eurovan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Passat</td>\n",
              "      <td>Compact</td>\n",
              "      <td>17.6</td>\n",
              "      <td>20.0</td>\n",
              "      <td>22.4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>31.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2985.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Passat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>Corrado</td>\n",
              "      <td>Sporty</td>\n",
              "      <td>22.9</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.7</td>\n",
              "      <td>18.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>None</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2810.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volkswagen Corrado</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Volvo</td>\n",
              "      <td>240</td>\n",
              "      <td>Compact</td>\n",
              "      <td>21.8</td>\n",
              "      <td>22.7</td>\n",
              "      <td>23.5</td>\n",
              "      <td>21.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Rear</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2985.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volvo 240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>NaN</td>\n",
              "      <td>850</td>\n",
              "      <td>Midsize</td>\n",
              "      <td>24.8</td>\n",
              "      <td>26.7</td>\n",
              "      <td>28.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Driver &amp; Passenger</td>\n",
              "      <td>Front</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3245.0</td>\n",
              "      <td>non-USA</td>\n",
              "      <td>Volvo 850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>93 rows × 27 columns</p>\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcgYkKCTZDYc"
      },
      "source": [
        "# Describe Statistics\n",
        "Describe statistics of data using `describe()` method for numeric data (default mode.)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "xVuHOvSrSJOv",
        "outputId": "d8419246-c121-4dc7-8624-f88e78a594c2"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Min.Price      Price  Max.Price   MPG.city  MPG.highway  EngineSize  \\\n",
              "count  86.000000  91.000000  88.000000  84.000000    91.000000   91.000000   \n",
              "mean   17.118605  19.616484  21.459091  22.404762    29.065934    2.658242   \n",
              "std     8.828290   9.724280  10.696563   5.841520     5.370293    1.045845   \n",
              "min     6.700000   7.400000   7.900000  15.000000    20.000000    1.000000   \n",
              "25%    10.825000  12.350000  14.575000  18.000000    26.000000    1.800000   \n",
              "50%    14.600000  17.700000  19.150000  21.000000    28.000000    2.300000   \n",
              "75%    20.250000  23.500000  24.825000  25.000000    31.000000    3.250000   \n",
              "max    45.400000  61.900000  80.000000  46.000000    50.000000    5.700000   \n",
              "\n",
              "       Horsepower          RPM  Rev.per.mile  Fuel.tank.capacity  Passengers  \\\n",
              "count   86.000000    90.000000     87.000000           85.000000   91.000000   \n",
              "mean   144.000000  5276.666667   2355.000000           16.683529    5.076923   \n",
              "std     53.455204   605.554811    486.916616            3.375748    1.045953   \n",
              "min     55.000000  3800.000000   1320.000000            9.200000    2.000000   \n",
              "25%    100.750000  4800.000000   2017.500000           14.500000    4.000000   \n",
              "50%    140.000000  5200.000000   2360.000000           16.500000    5.000000   \n",
              "75%    170.000000  5787.500000   2565.000000           19.000000    6.000000   \n",
              "max    300.000000  6500.000000   3755.000000           27.000000    8.000000   \n",
              "\n",
              "           Length   Wheelbase      Width  Turn.circle  Rear.seat.room  \\\n",
              "count   89.000000   92.000000  87.000000    88.000000       89.000000   \n",
              "mean   182.865169  103.956522  69.448276    38.954545       27.853933   \n",
              "std     14.792651    6.856317   3.778023     3.304157        3.018129   \n",
              "min    141.000000   90.000000  60.000000    32.000000       19.000000   \n",
              "25%    174.000000   98.000000  67.000000    36.000000       26.000000   \n",
              "50%    181.000000  103.000000  69.000000    39.000000       27.500000   \n",
              "75%    192.000000  110.000000  72.000000    42.000000       30.000000   \n",
              "max    219.000000  119.000000  78.000000    45.000000       36.000000   \n",
              "\n",
              "       Luggage.room       Weight  \n",
              "count     74.000000    86.000000  \n",
              "mean      13.986486  3104.593023  \n",
              "std        3.120824   600.129993  \n",
              "min        6.000000  1695.000000  \n",
              "25%       12.000000  2647.500000  \n",
              "50%       14.000000  3085.000000  \n",
              "75%       16.000000  3567.500000  \n",
              "max       22.000000  4105.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b126aae5-ed05-445b-a77b-07dc95ab9e14\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Min.Price</th>\n",
              "      <th>Price</th>\n",
              "      <th>Max.Price</th>\n",
              "      <th>MPG.city</th>\n",
              "      <th>MPG.highway</th>\n",
              "      <th>EngineSize</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>RPM</th>\n",
              "      <th>Rev.per.mile</th>\n",
              "      <th>Fuel.tank.capacity</th>\n",
              "      <th>Passengers</th>\n",
              "      <th>Length</th>\n",
              "      <th>Wheelbase</th>\n",
              "      <th>Width</th>\n",
              "      <th>Turn.circle</th>\n",
              "      <th>Rear.seat.room</th>\n",
              "      <th>Luggage.room</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>86.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>86.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>17.118605</td>\n",
              "      <td>19.616484</td>\n",
              "      <td>21.459091</td>\n",
              "      <td>22.404762</td>\n",
              "      <td>29.065934</td>\n",
              "      <td>2.658242</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>5276.666667</td>\n",
              "      <td>2355.000000</td>\n",
              "      <td>16.683529</td>\n",
              "      <td>5.076923</td>\n",
              "      <td>182.865169</td>\n",
              "      <td>103.956522</td>\n",
              "      <td>69.448276</td>\n",
              "      <td>38.954545</td>\n",
              "      <td>27.853933</td>\n",
              "      <td>13.986486</td>\n",
              "      <td>3104.593023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.828290</td>\n",
              "      <td>9.724280</td>\n",
              "      <td>10.696563</td>\n",
              "      <td>5.841520</td>\n",
              "      <td>5.370293</td>\n",
              "      <td>1.045845</td>\n",
              "      <td>53.455204</td>\n",
              "      <td>605.554811</td>\n",
              "      <td>486.916616</td>\n",
              "      <td>3.375748</td>\n",
              "      <td>1.045953</td>\n",
              "      <td>14.792651</td>\n",
              "      <td>6.856317</td>\n",
              "      <td>3.778023</td>\n",
              "      <td>3.304157</td>\n",
              "      <td>3.018129</td>\n",
              "      <td>3.120824</td>\n",
              "      <td>600.129993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.700000</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>3800.000000</td>\n",
              "      <td>1320.000000</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1695.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10.825000</td>\n",
              "      <td>12.350000</td>\n",
              "      <td>14.575000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>100.750000</td>\n",
              "      <td>4800.000000</td>\n",
              "      <td>2017.500000</td>\n",
              "      <td>14.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>174.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2647.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>14.600000</td>\n",
              "      <td>17.700000</td>\n",
              "      <td>19.150000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>5200.000000</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>16.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>181.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3085.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>20.250000</td>\n",
              "      <td>23.500000</td>\n",
              "      <td>24.825000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>3.250000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>5787.500000</td>\n",
              "      <td>2565.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>192.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>3567.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.400000</td>\n",
              "      <td>61.900000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>6500.000000</td>\n",
              "      <td>3755.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>219.000000</td>\n",
              "      <td>119.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>4105.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b126aae5-ed05-445b-a77b-07dc95ab9e14')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b126aae5-ed05-445b-a77b-07dc95ab9e14 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b126aae5-ed05-445b-a77b-07dc95ab9e14');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8WK2F1CaHgp"
      },
      "source": [
        "For categorical data, specify `include` parametor as `'object'`. \n",
        "(hint, refer [`pd.describe`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "aOoGGImmPSZq",
        "outputId": "6884112e-bfc7-499d-f76d-d3245b39cbb3"
      },
      "source": [
        "df.describe(include='object')"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Manufacturer    Model     Type      AirBags DriveTrain Cylinders  \\\n",
              "count            89       92       90           87         86        88   \n",
              "unique           31       92        6            3          3         6   \n",
              "top            Ford  Integra  Midsize  Driver only      Front         4   \n",
              "freq              8        1       22           39         61        45   \n",
              "\n",
              "       Man.trans.avail Origin           Make  \n",
              "count               88     88             90  \n",
              "unique               2      2             90  \n",
              "top                Yes    USA  Acura Integra  \n",
              "freq                57     46              1  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Manufacturer</th>\n",
              "      <th>Model</th>\n",
              "      <th>Type</th>\n",
              "      <th>AirBags</th>\n",
              "      <th>DriveTrain</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Man.trans.avail</th>\n",
              "      <th>Origin</th>\n",
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              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>89</td>\n",
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              "      <td>86</td>\n",
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              "      <th>unique</th>\n",
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              "      <td>90</td>\n",
              "    </tr>\n",
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              "      <th>top</th>\n",
              "      <td>Ford</td>\n",
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              "      <td>Midsize</td>\n",
              "      <td>Driver only</td>\n",
              "      <td>Front</td>\n",
              "      <td>4</td>\n",
              "      <td>Yes</td>\n",
              "      <td>USA</td>\n",
              "      <td>Acura Integra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>39</td>\n",
              "      <td>61</td>\n",
              "      <td>45</td>\n",
              "      <td>57</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d891c8dd-18cf-47b9-b74f-0f68c514f2c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d891c8dd-18cf-47b9-b74f-0f68c514f2c8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d891c8dd-18cf-47b9-b74f-0f68c514f2c8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJ-FqutCbceB"
      },
      "source": [
        "## Measuring skewness and kurtosis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, make a numericColumns by selecting only columns containing numerical data"
      ],
      "metadata": {
        "id": "aysd9xr1t-hF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numericColumns = df.describe().columns\n",
        "numericColumns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hutWrGbQuFY5",
        "outputId": "50cd1c2a-21a4-4e70-97c8-7d9d9dc1b3e9"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Min.Price', 'Price', 'Max.Price', 'MPG.city', 'MPG.highway',\n",
              "       'EngineSize', 'Horsepower', 'RPM', 'Rev.per.mile', 'Fuel.tank.capacity',\n",
              "       'Passengers', 'Length', 'Wheelbase', 'Width', 'Turn.circle',\n",
              "       'Rear.seat.room', 'Luggage.room', 'Weight'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Measure skewness for all numeric attribute and sort by value."
      ],
      "metadata": {
        "id": "1WdUYr5Ptsyr"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLeRmLyeGyNq",
        "outputId": "c424f504-0512-4786-e281-46a18384e120"
      },
      "source": [
        "skewness = df[numericColumns].skew().sort_values(ascending=False)\n",
        "skewness"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Max.Price             2.248420\n",
              "MPG.city              1.662674\n",
              "Price                 1.510432\n",
              "MPG.highway           1.238671\n",
              "Min.Price             1.227271\n",
              "Horsepower            0.944585\n",
              "EngineSize            0.881670\n",
              "Rev.per.mile          0.354385\n",
              "Width                 0.288760\n",
              "Luggage.room          0.149069\n",
              "Wheelbase             0.108599\n",
              "Fuel.tank.capacity    0.085445\n",
              "Passengers            0.081776\n",
              "Rear.seat.room        0.054352\n",
              "Length               -0.036669\n",
              "Turn.circle          -0.127313\n",
              "RPM                  -0.237511\n",
              "Weight               -0.269695\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Measure kurtosis for all numeric attribute and sort by value."
      ],
      "metadata": {
        "id": "GYLUZg__r5gW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kurtosis = df[numericColumns].kurtosis().sort_values(ascending=False)\n",
        "kurtosis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALooLrm2U1Al",
        "outputId": "84c97864-55b7-43c7-bea9-a79d12cad749"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Max.Price             9.238289\n",
              "MPG.city              3.617956\n",
              "Price                 3.328289\n",
              "MPG.highway           2.596793\n",
              "Min.Price             1.107837\n",
              "Horsepower            1.054818\n",
              "Passengers            0.916041\n",
              "Rear.seat.room        0.831153\n",
              "Length                0.406525\n",
              "EngineSize            0.377147\n",
              "Rev.per.mile          0.331590\n",
              "Luggage.room          0.322472\n",
              "Fuel.tank.capacity    0.017060\n",
              "Width                -0.201852\n",
              "RPM                  -0.482837\n",
              "Wheelbase            -0.821720\n",
              "Weight               -0.828994\n",
              "Turn.circle          -0.832044\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Graphic visualization of skewness and kurtosis"
      ],
      "metadata": {
        "id": "7x_ziCtgyj-t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Store the Attributes that skewness value is the largest, smallest and the closet to zero\n",
        "\n",
        "(hint, df['???'])"
      ],
      "metadata": {
        "id": "0ASULCL4xBeo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "highSkewAttribute = df[skewness.index[0]]\n",
        "lowSkewAttribute = df[skewness.index[-1]]\n",
        "zeroSkewAttribute = df['Length']"
      ],
      "metadata": {
        "id": "-ADWqLkPy-qf"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Draw plots. See how the distribution depends on the Skewness\n",
        "\n",
        "You don't need to enter the code :-)"
      ],
      "metadata": {
        "id": "Ad6OiCNG6Osx"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yP7uyi7RIv9X",
        "outputId": "6da7a6d8-a94c-420f-9308-8c717ce87496"
      },
      "source": [
        "f, axes = plt.subplots(3, 1, figsize=(10, 15))\n",
        "\n",
        "# set the string \n",
        "strInPlot = \"Skewness: %f\"\n",
        "\n",
        "# Plot distplot (highSkew)\n",
        "ax = axes[0]\n",
        "sns.histplot(data=df, x=highSkewAttribute, color=\"skyblue\", ax=ax, kde=True)\n",
        "\n",
        "# Add legend \n",
        "ax.text(x=0.97, y=0.97, transform=ax.transAxes, s=strInPlot % highSkewAttribute.skew(),\\\n",
        "    fontweight='demibold', fontsize=10, verticalalignment='top', horizontalalignment='right')\n",
        "\n",
        "# Plot distplot (zeroSkew)\n",
        "ax = axes[1]\n",
        "sns.histplot(data=df, x=zeroSkewAttribute, color=\"khaki\", ax=ax, kde=True)\n",
        "\n",
        "# Add legend \n",
        "ax.text(x=0.97, y=0.97, transform=ax.transAxes, s=strInPlot % zeroSkewAttribute.skew(),\\\n",
        "    fontweight='demibold', fontsize=10, verticalalignment='top', horizontalalignment='right')\n",
        "\n",
        "# Plot distplot (low\u0010Skew)\n",
        "ax = axes[2]\n",
        "sns.histplot(data=df, x=lowSkewAttribute, color=\"salmon\", ax=ax, kde=True)\n",
        "\n",
        "# Add legend \n",
        "ax.text(x=0.97, y=0.97, transform=ax.transAxes, s=strInPlot % lowSkewAttribute.skew(),\\\n",
        "    fontweight='demibold', fontsize=10, verticalalignment='top', horizontalalignment='right')\n",
        "\n",
        "plt.tight_layout()"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1080 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Store the Attributes that kurtosis value is the largest and smallest\n",
        "\n",
        "(hint, df['???'])"
      ],
      "metadata": {
        "id": "dXchuysq7oDc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "highKurtAttribute = df[kurtosis.index[0]]\n",
        "lowKurtAttribute = df[kurtosis.index[-1]]"
      ],
      "metadata": {
        "id": "WIk5Z1AY77W2"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f, axes = plt.subplots(2, 1, figsize=(10, 10))\n",
        "\n",
        "# set the string \n",
        "strInPlot = \"Kurtosis: %f\"\n",
        "\n",
        "# Plot distplot (highKurtosis)\n",
        "ax = axes[0]\n",
        "sns.histplot(data=df, x=highKurtAttribute, color=\"skyblue\", ax=ax, kde=True)\n",
        "\n",
        "# Add legend \n",
        "ax.text(x=0.97, y=0.97, transform=ax.transAxes, s=strInPlot % highKurtAttribute.kurtosis(),\\\n",
        "    fontweight='demibold', fontsize=10, verticalalignment='top', horizontalalignment='right')\n",
        "\n",
        "# Plot distplot (lowKurtosis)\n",
        "ax = axes[1]\n",
        "sns.histplot(data=df, x=lowKurtAttribute, color=\"salmon\", ax=ax, kde=True)\n",
        "\n",
        "# Add legend \n",
        "ax.text(x=0.97, y=0.97, transform=ax.transAxes, s=strInPlot % lowKurtAttribute.kurtosis(),\\\n",
        "    fontweight='demibold', fontsize=10, verticalalignment='top', horizontalalignment='right')\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "pyM9ijnlVUV1",
        "outputId": "4b9213e6-2728-4a7c-f961-b0ff0f652780"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pa4XwhOtSUhi"
      },
      "source": [
        "# Correlation\n",
        "**[TODO]** Select `Price`, `Horsepower`, `Passengers`, `Length`, `Weight` attribte from dataframe, and compute `pearson` correlation value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pJU5-RFFSWc5",
        "outputId": "28a7dfbf-e75f-48fc-fcce-666b5dd5a606"
      },
      "source": [
        "## Compelete the code\n",
        "selectedDataFrame = df[['Price', 'Horsepower', 'Passengers', 'Length', 'Weight']]\n",
        "selectedDataFrame.corr(method= 'pearson')"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Price  Horsepower  Passengers    Length    Weight\n",
              "Price       1.000000    0.794607    0.040890  0.564704  0.642563\n",
              "Horsepower  0.794607    1.000000   -0.039870  0.552906  0.777764\n",
              "Passengers  0.040890   -0.039870    1.000000  0.473748  0.577401\n",
              "Length      0.564704    0.552906    0.473748  1.000000  0.813305\n",
              "Weight      0.642563    0.777764    0.577401  0.813305  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6497579c-a5f3-4cb2-98c7-f213ac007446\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Price</th>\n",
              "      <th>Horsepower</th>\n",
              "      <th>Passengers</th>\n",
              "      <th>Length</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.794607</td>\n",
              "      <td>0.040890</td>\n",
              "      <td>0.564704</td>\n",
              "      <td>0.642563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>0.794607</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.039870</td>\n",
              "      <td>0.552906</td>\n",
              "      <td>0.777764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Passengers</th>\n",
              "      <td>0.040890</td>\n",
              "      <td>-0.039870</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.473748</td>\n",
              "      <td>0.577401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Length</th>\n",
              "      <td>0.564704</td>\n",
              "      <td>0.552906</td>\n",
              "      <td>0.473748</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.813305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>0.642563</td>\n",
              "      <td>0.777764</td>\n",
              "      <td>0.577401</td>\n",
              "      <td>0.813305</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6497579c-a5f3-4cb2-98c7-f213ac007446')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6497579c-a5f3-4cb2-98c7-f213ac007446 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6497579c-a5f3-4cb2-98c7-f213ac007446');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 903
        },
        "id": "yurFiH3fSeCM",
        "outputId": "86df6e4a-13fa-49f6-aa49-e85023039f6a"
      },
      "source": [
        "g = sns.pairplot(selectedDataFrame)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5BYb2PiUhma"
      },
      "source": [
        "# Statistical Hypothesis Test\n",
        "We would like the test that is there a significant `Passengers` difference between `small` and `midsize` type car.\n",
        "\n",
        "Test our hypothesis with proper statistical test with siginificance level as 0.05.\n",
        "\n",
        "If there is missing value (`nan`) in `pd.Series`, test will not be working properly. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qyc1E17xUmbk",
        "outputId": "4e79fc2e-95be-427a-b3f9-91daeacd3c45"
      },
      "source": [
        "siginificantLevel = 0.05\n",
        "smallCar = df[df['Type']=='Small']['Passengers'].dropna()\n",
        "midsizeCar = df[df['Type']=='Midsize']['Passengers'].dropna()\n",
        "\n",
        "stats, p = ttest_ind(smallCar, midsizeCar)\n",
        "print('P-value', format(p, \".19f\"))\n",
        "\n",
        "if p < siginificantLevel:\n",
        "  print(\"The passengers of the two types shows a significant difference.\")\n",
        "else:\n",
        "  print(\"The passengers of the two types shows no significant difference.\")"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P-value 0.0042894346544763359\n",
            "The passengers of the two types shows a significant difference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then, let's test that is there is a significant `Length` difference between `top 50% weight` and `bottom 50% weight`."
      ],
      "metadata": {
        "id": "1Pw12XqPGNYD"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aTyc-j8VO1cs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80f0cbc5-7e5f-4b0e-f2fe-dc15c95b3f52"
      },
      "source": [
        "significiantLevel = 0.05\n",
        "\n",
        "standard = df['Weight'].quantile(0.5)\n",
        "\n",
        "lightCarLength = df[df['Weight'] < standard]['Length'].dropna()\n",
        "heavyCarLength = df[df['Weight'] >= standard]['Length'].dropna()\n",
        "\n",
        "stats, p = ttest_ind(lightCarLength, heavyCarLength)\n",
        "print('P-value', format(p, \".19f\"))\n",
        "\n",
        "if p < siginificantLevel:\n",
        "  print(\"The Length shows a significant difference.\")\n",
        "else:\n",
        "  print(\"The Length shows no significant difference.\")"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P-value 0.0000000000123944090\n",
            "The Length shows a significant difference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Todo!\n",
        "\n",
        "**[Tip]** \n",
        "\n",
        "1.   과제 중 진행이 어려우시다면 구글링을 시도해보세요. 데이터 분석 프로그래밍은 구글링 하는 능력을 향상시키는 데 목적을 두기도 합니다.\n",
        "\n",
        "2.   그래도 너무 어렵다면 이루리에 질문을 남겨주세요, 저희가 친절하게 답변해드립니다. (조금 늦을수는 있지만…)\n",
        "\n",
        "3.   할 수 있습니다 화이팅!\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "TgZ7NaNgp1LB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. 라면 데이터 준비하기\n",
        "\n"
      ],
      "metadata": {
        "id": "kUVy2b3J8qbp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "이번 실습에서는 한국에서 판매하는 라면에 대해 간단히 분석해볼 것입니다.\n",
        "\n",
        "우선 데이터를 제가 준비해두었습니다!\n",
        "\n",
        "아래의 표를 한번 확인해보세요.\n",
        "\n"
      ],
      "metadata": {
        "id": "qO-XzAcgqxZ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('https://github.com/TA-YSH/DAP/raw/main/lab3/FoodListExcel.xlsx')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "FygXv_y_q8kQ",
        "outputId": "8322a0d3-0320-40aa-fb00-2cf43c6c42fb"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sample_id    name_product name_manufacturer  survey_year class  \\\n",
              "0    P064392ZZAVG      NEW 공화춘곱빼기                팔도         2021  가공식품   \n",
              "1    P064393ZZAVG      NEW 공화춘자장컵                팔도         2021  가공식품   \n",
              "2    P064394ZZAVG  강릉 교동반점 직화짬뽕소컵                팔도         2021  가공식품   \n",
              "3    P064395ZZAVG         꼬꼬누룽지탕면                팔도         2021  가공식품   \n",
              "4    P064396ZZAVG      뉴트리 클로렐라컵면                팔도         2021  가공식품   \n",
              "..            ...             ...               ...          ...   ...   \n",
              "616  P014688ZZAVG         컵누들 우동맛               오뚜기         2017  가공식품   \n",
              "617  P014702ZZAVG          튀김우동 컵                농심         2017  가공식품   \n",
              "618  P014714ZZAVG             하모니                농심         2017  가공식품   \n",
              "619  P014726ZZAVG          후루룩 국수                농심         2017  가공식품   \n",
              "620  P014727ZZAVG         후루룩 칼국수                농심         2017  가공식품   \n",
              "\n",
              "    class_recipe     code     source_data  serving_size    kcal  kj  protein  \\\n",
              "0            유탕면  P064392        식약처('21)           230  745.00 NaN    14.00   \n",
              "1            유탕면  P064393        식약처('21)           200  620.00 NaN    11.00   \n",
              "2            유탕면  P064394        식약처('21)            65  290.00 NaN     6.00   \n",
              "3            유탕면  P064395        식약처('21)           120  505.00 NaN     9.00   \n",
              "4            유탕면  P064396        식약처('21)            65  300.00 NaN     6.00   \n",
              "..           ...      ...             ...           ...     ...  ..      ...   \n",
              "616          유탕면  P014688  식약처 산업체제공('17)            38  119.69 NaN     1.99   \n",
              "617          유탕면  P014702  식약처 산업체제공('17)            80  325.16 NaN     4.13   \n",
              "618          유탕면  P014714  식약처 산업체제공('17)           120  498.25 NaN     7.74   \n",
              "619          유탕면  P014726  식약처 산업체제공('17)           120  544.42 NaN    15.31   \n",
              "620           건면  P014727  식약처 산업체제공('17)           120  505.05 NaN    12.24   \n",
              "\n",
              "       fat  carbohydrate  sugars   sodium  cholesterol  saturated_fat  \\\n",
              "0    36.00         91.00    7.00  1230.00         10.0          15.00   \n",
              "1    31.00         74.00    7.00  1130.00         10.0          12.00   \n",
              "2    12.00         39.00    3.00  1040.00          5.0           6.00   \n",
              "3    13.00         88.00    4.00  1670.00          2.0           6.00   \n",
              "4    12.00         41.00    1.00   940.00          8.0           6.00   \n",
              "..     ...           ...     ...      ...          ...            ...   \n",
              "616   0.50         26.93    2.99   887.66          0.0           0.00   \n",
              "617  15.48         42.32    2.88  1238.70          0.0           5.16   \n",
              "618  19.35         73.53    1.31   870.73          0.0           8.71   \n",
              "619   1.19        117.39    3.01  2909.20          0.0           0.00   \n",
              "620   0.92        111.72    8.82  2356.89          0.0           0.00   \n",
              "\n",
              "     saturated_fat_per  trans_fat  \n",
              "0                  NaN        0.0  \n",
              "1                  NaN        0.0  \n",
              "2                  NaN        0.0  \n",
              "3                  NaN        0.0  \n",
              "4                  NaN        0.0  \n",
              "..                 ...        ...  \n",
              "616                NaN        0.0  \n",
              "617                NaN        0.0  \n",
              "618                NaN        0.0  \n",
              "619                NaN        0.0  \n",
              "620                NaN        0.0  \n",
              "\n",
              "[621 rows x 20 columns]"
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <th>sample_id</th>\n",
              "      <th>name_product</th>\n",
              "      <th>name_manufacturer</th>\n",
              "      <th>survey_year</th>\n",
              "      <th>class</th>\n",
              "      <th>class_recipe</th>\n",
              "      <th>code</th>\n",
              "      <th>source_data</th>\n",
              "      <th>serving_size</th>\n",
              "      <th>kcal</th>\n",
              "      <th>kj</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>carbohydrate</th>\n",
              "      <th>sugars</th>\n",
              "      <th>sodium</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>saturated_fat</th>\n",
              "      <th>saturated_fat_per</th>\n",
              "      <th>trans_fat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>P064392ZZAVG</td>\n",
              "      <td>NEW 공화춘곱빼기</td>\n",
              "      <td>팔도</td>\n",
              "      <td>2021</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P064392</td>\n",
              "      <td>식약처('21)</td>\n",
              "      <td>230</td>\n",
              "      <td>745.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.00</td>\n",
              "      <td>36.00</td>\n",
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              "      <td>10.0</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>P064393ZZAVG</td>\n",
              "      <td>NEW 공화춘자장컵</td>\n",
              "      <td>팔도</td>\n",
              "      <td>2021</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P064393</td>\n",
              "      <td>식약처('21)</td>\n",
              "      <td>200</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>11.00</td>\n",
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              "      <td>1130.00</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>P064394ZZAVG</td>\n",
              "      <td>강릉 교동반점 직화짬뽕소컵</td>\n",
              "      <td>팔도</td>\n",
              "      <td>2021</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P064394</td>\n",
              "      <td>식약처('21)</td>\n",
              "      <td>65</td>\n",
              "      <td>290.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1040.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>P064395ZZAVG</td>\n",
              "      <td>꼬꼬누룽지탕면</td>\n",
              "      <td>팔도</td>\n",
              "      <td>2021</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P064395</td>\n",
              "      <td>식약처('21)</td>\n",
              "      <td>120</td>\n",
              "      <td>505.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.00</td>\n",
              "      <td>13.00</td>\n",
              "      <td>88.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1670.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>P064396ZZAVG</td>\n",
              "      <td>뉴트리 클로렐라컵면</td>\n",
              "      <td>팔도</td>\n",
              "      <td>2021</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P064396</td>\n",
              "      <td>식약처('21)</td>\n",
              "      <td>65</td>\n",
              "      <td>300.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>41.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>940.00</td>\n",
              "      <td>8.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>616</th>\n",
              "      <td>P014688ZZAVG</td>\n",
              "      <td>컵누들 우동맛</td>\n",
              "      <td>오뚜기</td>\n",
              "      <td>2017</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P014688</td>\n",
              "      <td>식약처 산업체제공('17)</td>\n",
              "      <td>38</td>\n",
              "      <td>119.69</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.99</td>\n",
              "      <td>0.50</td>\n",
              "      <td>26.93</td>\n",
              "      <td>2.99</td>\n",
              "      <td>887.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>617</th>\n",
              "      <td>P014702ZZAVG</td>\n",
              "      <td>튀김우동 컵</td>\n",
              "      <td>농심</td>\n",
              "      <td>2017</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P014702</td>\n",
              "      <td>식약처 산업체제공('17)</td>\n",
              "      <td>80</td>\n",
              "      <td>325.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.13</td>\n",
              "      <td>15.48</td>\n",
              "      <td>42.32</td>\n",
              "      <td>2.88</td>\n",
              "      <td>1238.70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>618</th>\n",
              "      <td>P014714ZZAVG</td>\n",
              "      <td>하모니</td>\n",
              "      <td>농심</td>\n",
              "      <td>2017</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P014714</td>\n",
              "      <td>식약처 산업체제공('17)</td>\n",
              "      <td>120</td>\n",
              "      <td>498.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.74</td>\n",
              "      <td>19.35</td>\n",
              "      <td>73.53</td>\n",
              "      <td>1.31</td>\n",
              "      <td>870.73</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>P014726ZZAVG</td>\n",
              "      <td>후루룩 국수</td>\n",
              "      <td>농심</td>\n",
              "      <td>2017</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>P014726</td>\n",
              "      <td>식약처 산업체제공('17)</td>\n",
              "      <td>120</td>\n",
              "      <td>544.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.31</td>\n",
              "      <td>1.19</td>\n",
              "      <td>117.39</td>\n",
              "      <td>3.01</td>\n",
              "      <td>2909.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>620</th>\n",
              "      <td>P014727ZZAVG</td>\n",
              "      <td>후루룩 칼국수</td>\n",
              "      <td>농심</td>\n",
              "      <td>2017</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>건면</td>\n",
              "      <td>P014727</td>\n",
              "      <td>식약처 산업체제공('17)</td>\n",
              "      <td>120</td>\n",
              "      <td>505.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.24</td>\n",
              "      <td>0.92</td>\n",
              "      <td>111.72</td>\n",
              "      <td>8.82</td>\n",
              "      <td>2356.89</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>621 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-931e9587-2998-4d1d-96b7-f6577c6e1976')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-931e9587-2998-4d1d-96b7-f6577c6e1976 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-931e9587-2998-4d1d-96b7-f6577c6e1976');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. 필요없는 열 드랍하기\n",
        "\n"
      ],
      "metadata": {
        "id": "fCQyIXq2vfao"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "분석에 필요 없는 속성값은 모두 제거하는 것이 좋을 것입니다.\n",
        "\n",
        "그 때 제일 유용한 도구는 바로 ‘drop’이라는 함수입니다.\n",
        "\n",
        "`df.drop()`을 이용하면 원하는 columns를 제거할 수 있습니다.\n",
        "\n",
        "제가 필요 없는 속성으로 보이는 것들을 dropColumns라는 리스트 안에 넣어두었습니다.\n",
        "\n",
        "df에서 해당 속성들을 모두 제거해주세요.\n",
        "\n",
        "만약 진행이 어렵다면 아래의 레퍼런스를 참조해주세요. \n",
        "\n",
        "pandas.DataFrame.drop: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html"
      ],
      "metadata": {
        "id": "OQcDu2G-9iYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dropColumns = ['sample_id', 'survey_year', 'code', 'source_data']\n",
        "# Complete the code!\n",
        "df = df.drop(dropColumns, axis=1)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-firWJ7Vw_kc",
        "outputId": "3071157d-016f-499c-9526-bea5652e89c4"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       name_product name_manufacturer class class_recipe  serving_size  \\\n",
              "0        NEW 공화춘곱빼기                팔도  가공식품          유탕면           230   \n",
              "1        NEW 공화춘자장컵                팔도  가공식품          유탕면           200   \n",
              "2    강릉 교동반점 직화짬뽕소컵                팔도  가공식품          유탕면            65   \n",
              "3           꼬꼬누룽지탕면                팔도  가공식품          유탕면           120   \n",
              "4        뉴트리 클로렐라컵면                팔도  가공식품          유탕면            65   \n",
              "..              ...               ...   ...          ...           ...   \n",
              "616         컵누들 우동맛               오뚜기  가공식품          유탕면            38   \n",
              "617          튀김우동 컵                농심  가공식품          유탕면            80   \n",
              "618             하모니                농심  가공식품          유탕면           120   \n",
              "619          후루룩 국수                농심  가공식품          유탕면           120   \n",
              "620         후루룩 칼국수                농심  가공식품           건면           120   \n",
              "\n",
              "       kcal  kj  protein    fat  carbohydrate  sugars   sodium  cholesterol  \\\n",
              "0    745.00 NaN    14.00  36.00         91.00    7.00  1230.00         10.0   \n",
              "1    620.00 NaN    11.00  31.00         74.00    7.00  1130.00         10.0   \n",
              "2    290.00 NaN     6.00  12.00         39.00    3.00  1040.00          5.0   \n",
              "3    505.00 NaN     9.00  13.00         88.00    4.00  1670.00          2.0   \n",
              "4    300.00 NaN     6.00  12.00         41.00    1.00   940.00          8.0   \n",
              "..      ...  ..      ...    ...           ...     ...      ...          ...   \n",
              "616  119.69 NaN     1.99   0.50         26.93    2.99   887.66          0.0   \n",
              "617  325.16 NaN     4.13  15.48         42.32    2.88  1238.70          0.0   \n",
              "618  498.25 NaN     7.74  19.35         73.53    1.31   870.73          0.0   \n",
              "619  544.42 NaN    15.31   1.19        117.39    3.01  2909.20          0.0   \n",
              "620  505.05 NaN    12.24   0.92        111.72    8.82  2356.89          0.0   \n",
              "\n",
              "     saturated_fat  saturated_fat_per  trans_fat  \n",
              "0            15.00                NaN        0.0  \n",
              "1            12.00                NaN        0.0  \n",
              "2             6.00                NaN        0.0  \n",
              "3             6.00                NaN        0.0  \n",
              "4             6.00                NaN        0.0  \n",
              "..             ...                ...        ...  \n",
              "616           0.00                NaN        0.0  \n",
              "617           5.16                NaN        0.0  \n",
              "618           8.71                NaN        0.0  \n",
              "619           0.00                NaN        0.0  \n",
              "620           0.00                NaN        0.0  \n",
              "\n",
              "[621 rows x 16 columns]"
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              "      <th>0</th>\n",
              "      <td>NEW 공화춘곱빼기</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>강릉 교동반점 직화짬뽕소컵</td>\n",
              "      <td>팔도</td>\n",
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              "      <td>65</td>\n",
              "      <td>290.00</td>\n",
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              "      <td>6.00</td>\n",
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              "      <th>3</th>\n",
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              "      <td>120</td>\n",
              "      <td>505.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.00</td>\n",
              "      <td>13.00</td>\n",
              "      <td>88.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1670.00</td>\n",
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              "      <td>6.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>616</th>\n",
              "      <td>컵누들 우동맛</td>\n",
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              "      <td>가공식품</td>\n",
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              "      <td>38</td>\n",
              "      <td>119.69</td>\n",
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              "      <th>617</th>\n",
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              "      <td>가공식품</td>\n",
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              "      <td>80</td>\n",
              "      <td>325.16</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>618</th>\n",
              "      <td>하모니</td>\n",
              "      <td>농심</td>\n",
              "      <td>가공식품</td>\n",
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              "      <td>8.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>후루룩 국수</td>\n",
              "      <td>농심</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>120</td>\n",
              "      <td>544.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.31</td>\n",
              "      <td>1.19</td>\n",
              "      <td>117.39</td>\n",
              "      <td>3.01</td>\n",
              "      <td>2909.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>620</th>\n",
              "      <td>후루룩 칼국수</td>\n",
              "      <td>농심</td>\n",
              "      <td>가공식품</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>621 rows × 16 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47347a01-e0ca-46cd-a4a2-d88fe3a0f564')\"\n",
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              "        \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-47347a01-e0ca-46cd-a4a2-d88fe3a0f564');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2-2. NaN 값이 많은 속성 제거하기"
      ],
      "metadata": {
        "id": "vskStQkdv593"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "이제 NaN 값이 너무 많아서 오히려 방해가 될 것 같은 속성을 지워봅시다.\n",
        "\n",
        "사실 이런 NaN 값을 적당한 값으로 채워넣어주는 방법 또한 분명히 존재합니다.\n",
        "\n",
        "하지만 해당 내용은 추후에 배우게 될 것이므로 이번 실습에서는 제거하는 방향으로 처리합시다.\n",
        "\n",
        "\n",
        "우선, NaN 데이터가 각 속성마다 몇 개 있는지 pd.Series 형식으로 출력해주세요.\n",
        "\n",
        "저번 실습에서 한 번 해보셨기 때문에 쉽게 하실 수 있을 것입니다!"
      ],
      "metadata": {
        "id": "D1nhzCkk_8MX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nullSeries = pd.Series(df.isnull().sum())\n",
        "nullSeries"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGNNl9av1pk6",
        "outputId": "44f6c774-aae1-4eb7-dc0a-993f859e40e1"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name_product           0\n",
              "name_manufacturer      0\n",
              "class                  0\n",
              "class_recipe           0\n",
              "serving_size           0\n",
              "kcal                   0\n",
              "kj                   621\n",
              "protein                0\n",
              "fat                    0\n",
              "carbohydrate           0\n",
              "sugars                 0\n",
              "sodium                 0\n",
              "cholesterol           10\n",
              "saturated_fat          2\n",
              "saturated_fat_per    621\n",
              "trans_fat              1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "다음 조건에 따라 코드를 작성하세요\n",
        "\n",
        "**[조건]**\n",
        "1. `’nullList’`란 전체 데이터의 절반 이상이 NaN인 속성의 이름을 저장한 리스트입니다.\n",
        "2. `’nullList’`의 원소를 코드로 직접 선언하지 마세요.(ex: null_list = [‘kj’, ‘saturated_fat_per’])\n",
        "3. 방금 만들었던 `’nullSeries’`를 반드시 사용하세요.\n",
        "\n",
        "**[힌트]**\n",
        "1. 이 코드는 한줄로 작성이 가능합니다. 하지만 여러 줄로 작성하셔도 무방합니다.\n",
        "2. `’nullSeries’`는 list처럼 각 원소에 접근 가능합니다. (ex: null_series[0])\n"
      ],
      "metadata": {
        "id": "sJN6bPyPBPCx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nullList = nullSeries.index[nullSeries>=len(df)/2].tolist()\n",
        "nullList"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCxBL7QXyDtr",
        "outputId": "2457c1b0-2052-46fb-d818-76a9de834732"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['kj', 'saturated_fat_per']"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`drop()` 함수를 이용해서 `nullList`에 저장된 속성들을 df에서 제거해주세요."
      ],
      "metadata": {
        "id": "_icf6IdAxJJk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(nullList, axis=1)\n",
        "df"
      ],
      "metadata": {
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      "execution_count": 171,
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              "       name_product name_manufacturer class class_recipe  serving_size  \\\n",
              "0        NEW 공화춘곱빼기                팔도  가공식품          유탕면           230   \n",
              "1        NEW 공화춘자장컵                팔도  가공식품          유탕면           200   \n",
              "2    강릉 교동반점 직화짬뽕소컵                팔도  가공식품          유탕면            65   \n",
              "3           꼬꼬누룽지탕면                팔도  가공식품          유탕면           120   \n",
              "4        뉴트리 클로렐라컵면                팔도  가공식품          유탕면            65   \n",
              "..              ...               ...   ...          ...           ...   \n",
              "616         컵누들 우동맛               오뚜기  가공식품          유탕면            38   \n",
              "617          튀김우동 컵                농심  가공식품          유탕면            80   \n",
              "618             하모니                농심  가공식품          유탕면           120   \n",
              "619          후루룩 국수                농심  가공식품          유탕면           120   \n",
              "620         후루룩 칼국수                농심  가공식품           건면           120   \n",
              "\n",
              "       kcal  protein    fat  carbohydrate  sugars   sodium  cholesterol  \\\n",
              "0    745.00    14.00  36.00         91.00    7.00  1230.00         10.0   \n",
              "1    620.00    11.00  31.00         74.00    7.00  1130.00         10.0   \n",
              "2    290.00     6.00  12.00         39.00    3.00  1040.00          5.0   \n",
              "3    505.00     9.00  13.00         88.00    4.00  1670.00          2.0   \n",
              "4    300.00     6.00  12.00         41.00    1.00   940.00          8.0   \n",
              "..      ...      ...    ...           ...     ...      ...          ...   \n",
              "616  119.69     1.99   0.50         26.93    2.99   887.66          0.0   \n",
              "617  325.16     4.13  15.48         42.32    2.88  1238.70          0.0   \n",
              "618  498.25     7.74  19.35         73.53    1.31   870.73          0.0   \n",
              "619  544.42    15.31   1.19        117.39    3.01  2909.20          0.0   \n",
              "620  505.05    12.24   0.92        111.72    8.82  2356.89          0.0   \n",
              "\n",
              "     saturated_fat  trans_fat  \n",
              "0            15.00        0.0  \n",
              "1            12.00        0.0  \n",
              "2             6.00        0.0  \n",
              "3             6.00        0.0  \n",
              "4             6.00        0.0  \n",
              "..             ...        ...  \n",
              "616           0.00        0.0  \n",
              "617           5.16        0.0  \n",
              "618           8.71        0.0  \n",
              "619           0.00        0.0  \n",
              "620           0.00        0.0  \n",
              "\n",
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              "      <th>0</th>\n",
              "      <td>NEW 공화춘곱빼기</td>\n",
              "      <td>팔도</td>\n",
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              "      <td>91.00</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NEW 공화춘자장컵</td>\n",
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              "      <td>11.00</td>\n",
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              "      <td>7.00</td>\n",
              "      <td>1130.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.00</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>강릉 교동반점 직화짬뽕소컵</td>\n",
              "      <td>팔도</td>\n",
              "      <td>가공식품</td>\n",
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              "      <td>65</td>\n",
              "      <td>290.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1040.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>꼬꼬누룽지탕면</td>\n",
              "      <td>팔도</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>120</td>\n",
              "      <td>505.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>13.00</td>\n",
              "      <td>88.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1670.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>뉴트리 클로렐라컵면</td>\n",
              "      <td>팔도</td>\n",
              "      <td>가공식품</td>\n",
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              "      <td>65</td>\n",
              "      <td>300.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>41.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>940.00</td>\n",
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              "      <td>6.00</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>616</th>\n",
              "      <td>컵누들 우동맛</td>\n",
              "      <td>오뚜기</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>38</td>\n",
              "      <td>119.69</td>\n",
              "      <td>1.99</td>\n",
              "      <td>0.50</td>\n",
              "      <td>26.93</td>\n",
              "      <td>2.99</td>\n",
              "      <td>887.66</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>가공식품</td>\n",
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              "      <td>80</td>\n",
              "      <td>325.16</td>\n",
              "      <td>4.13</td>\n",
              "      <td>15.48</td>\n",
              "      <td>42.32</td>\n",
              "      <td>2.88</td>\n",
              "      <td>1238.70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.16</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>618</th>\n",
              "      <td>하모니</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>8.71</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>후루룩 국수</td>\n",
              "      <td>농심</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>120</td>\n",
              "      <td>544.42</td>\n",
              "      <td>15.31</td>\n",
              "      <td>1.19</td>\n",
              "      <td>117.39</td>\n",
              "      <td>3.01</td>\n",
              "      <td>2909.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>620</th>\n",
              "      <td>후루룩 칼국수</td>\n",
              "      <td>농심</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>건면</td>\n",
              "      <td>120</td>\n",
              "      <td>505.05</td>\n",
              "      <td>12.24</td>\n",
              "      <td>0.92</td>\n",
              "      <td>111.72</td>\n",
              "      <td>8.82</td>\n",
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              "      <td>0.0</td>\n",
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              "<p>621 rows × 14 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. 최솟값, 최댓값 그리고 사분위수\n",
        "\n",
        "아래의 df의 대략적인 내용을 `describe()` 함수를 이용해서 출력해주세요. 그리고 출력값의 `min`, `25%`, `50%`, `75%`, `max` columns와 df2의 kcal 속성을 주목하세요."
      ],
      "metadata": {
        "id": "Svs1WAZQyP5H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "P6z6aFuQzQhh",
        "outputId": "262719a0-58e3-49c6-9e4d-b7d833a47d24"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       serving_size        kcal     protein         fat  carbohydrate  \\\n",
              "count    621.000000  621.000000  621.000000  621.000000    621.000000   \n",
              "mean     108.958132  439.725894    8.729662   15.035781     67.157407   \n",
              "std       31.667601  106.782258    2.520399    5.692365     16.776216   \n",
              "min       20.000000   13.630000    0.000000    0.000000      3.400000   \n",
              "25%       92.000000  380.000000    7.000000   12.000000     57.000000   \n",
              "50%      110.000000  470.000000    9.000000   16.000000     70.000000   \n",
              "75%      120.000000  500.000000   10.000000   18.000000     77.000000   \n",
              "max      253.000000  745.000000   20.000000   36.000000    117.390000   \n",
              "\n",
              "           sugars       sodium  cholesterol  saturated_fat   trans_fat  \n",
              "count  621.000000   621.000000   611.000000     619.000000  620.000000  \n",
              "mean     5.523800  1370.753559     3.323273       7.054443    0.014677  \n",
              "std      3.629177   420.397811     6.393039       2.824633    0.322291  \n",
              "min      0.000000     0.000000     0.000000       0.000000    0.000000  \n",
              "25%      3.000000  1080.000000     0.000000       6.000000    0.000000  \n",
              "50%      4.610000  1450.000000     0.000000       8.000000    0.000000  \n",
              "75%      7.000000  1690.000000     5.000000       9.000000    0.000000  \n",
              "max     24.000000  3035.500000    67.000000      15.000000    8.000000  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>serving_size</th>\n",
              "      <th>kcal</th>\n",
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              "      <th>count</th>\n",
              "      <td>621.000000</td>\n",
              "      <td>621.000000</td>\n",
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              "      <td>621.000000</td>\n",
              "      <td>621.000000</td>\n",
              "      <td>611.000000</td>\n",
              "      <td>619.000000</td>\n",
              "      <td>620.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>108.958132</td>\n",
              "      <td>439.725894</td>\n",
              "      <td>8.729662</td>\n",
              "      <td>15.035781</td>\n",
              "      <td>67.157407</td>\n",
              "      <td>5.523800</td>\n",
              "      <td>1370.753559</td>\n",
              "      <td>3.323273</td>\n",
              "      <td>7.054443</td>\n",
              "      <td>0.014677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>31.667601</td>\n",
              "      <td>106.782258</td>\n",
              "      <td>2.520399</td>\n",
              "      <td>5.692365</td>\n",
              "      <td>16.776216</td>\n",
              "      <td>3.629177</td>\n",
              "      <td>420.397811</td>\n",
              "      <td>6.393039</td>\n",
              "      <td>2.824633</td>\n",
              "      <td>0.322291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>13.630000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>92.000000</td>\n",
              "      <td>380.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1080.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>110.000000</td>\n",
              "      <td>470.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>4.610000</td>\n",
              "      <td>1450.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>120.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1690.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>253.000000</td>\n",
              "      <td>745.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>117.390000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>3035.500000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e427b5be-61bf-4ba0-9b3a-6b3def09d277')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e427b5be-61bf-4ba0-9b3a-6b3def09d277 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e427b5be-61bf-4ba0-9b3a-6b3def09d277');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "다음 조건에 따라 df2를 완성하세요.\n",
        "\n",
        "**[조건]**\n",
        "1. `df2`는 `df`에 속한 라면들 중, ‘kcal’이 최솟값, 일사분위수, 이사분위수, 삼사분위수, 최댓값과 일치하는 라면을 저장한 데이터프레임입니다. \n",
        "2. `df.describe()`를 선언하거나 사용하지 마세요.\n",
        "3. 최솟값, 최댓값, 사분위수들을 직접 선언하지 마세요. (ex: list = [13.63, 380, 470, 500, 745])\n"
      ],
      "metadata": {
        "id": "-S-ruAvFLRQs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##enter your code####\n",
        "kcalList = [df['kcal'].min(), df['kcal'].quantile(.25), df['kcal'].quantile(.50), df['kcal'].quantile(.75), df['kcal'].max()]\n",
        "\n",
        "####################\n",
        "df2 = df[df['kcal'].isin(kcalList)]\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "W9-5uNo75cTV",
        "outputId": "fd91f64a-7e27-405c-b85d-6e88134ea1ea"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    name_product name_manufacturer class class_recipe  serving_size    kcal  \\\n",
              "0     NEW 공화춘곱빼기                팔도  가공식품          유탕면           230  745.00   \n",
              "14     일품해물라면 왕컵                팔도  가공식품          유탕면           110  470.00   \n",
              "19      팔도 김치왕뚜껑                팔도  가공식품          유탕면           110  470.00   \n",
              "23      팔도탄탄면 왕컵                팔도  가공식품          유탕면           116  500.00   \n",
              "28         김치사발면                농심  가공식품          유탕면            86  380.00   \n",
              "..           ...               ...   ...          ...           ...     ...   \n",
              "592      옛날 자른당면               오뚜기  가공식품           건면            20   13.63   \n",
              "593        열떡볶이면               오뚜기  가공식품          유탕면           140  470.00   \n",
              "594       진라면 용기               오뚜기  가공식품          유탕면           110  500.00   \n",
              "605      진라면 매운맛               오뚜기  가공식품          유탕면           120  500.00   \n",
              "606      진라면 순한맛               오뚜기  가공식품          유탕면           120  500.00   \n",
              "\n",
              "     protein   fat  carbohydrate  sugars   sodium  cholesterol  saturated_fat  \\\n",
              "0       14.0  36.0          91.0    7.00  1230.00         10.0           15.0   \n",
              "14      11.0  15.0          73.0    4.00  1620.00          9.0            7.0   \n",
              "19       8.0  17.0          71.0    4.00  1790.00          0.0            9.0   \n",
              "23      11.0  22.0          65.0    4.00  1700.00          2.0           10.0   \n",
              "28       7.0  14.0          56.0    5.00  1520.00          0.0            7.0   \n",
              "..       ...   ...           ...     ...      ...          ...            ...   \n",
              "592      0.0   0.0           3.4    0.01     0.12          0.0            0.0   \n",
              "593      9.0  10.0          86.0   13.00   800.00          0.0            4.9   \n",
              "594     10.0  18.0          47.0    6.00  1540.00          0.0           10.0   \n",
              "605     11.0  15.0          80.0    4.60  1860.00          0.0            8.0   \n",
              "606     12.0  15.0          79.0    4.00  1880.00          0.0            8.0   \n",
              "\n",
              "     trans_fat  \n",
              "0          0.0  \n",
              "14         0.0  \n",
              "19         0.0  \n",
              "23         0.0  \n",
              "28         0.0  \n",
              "..         ...  \n",
              "592        0.0  \n",
              "593        0.0  \n",
              "594        0.0  \n",
              "605        0.0  \n",
              "606        0.0  \n",
              "\n",
              "[73 rows x 14 columns]"
            ],
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              "  <div id=\"df-f0ca9d05-fa77-49be-87ee-04f955b81492\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name_product</th>\n",
              "      <th>name_manufacturer</th>\n",
              "      <th>class</th>\n",
              "      <th>class_recipe</th>\n",
              "      <th>serving_size</th>\n",
              "      <th>kcal</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>carbohydrate</th>\n",
              "      <th>sugars</th>\n",
              "      <th>sodium</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>saturated_fat</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NEW 공화춘곱빼기</td>\n",
              "      <td>팔도</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>230</td>\n",
              "      <td>745.00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>7.00</td>\n",
              "      <td>1230.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>일품해물라면 왕컵</td>\n",
              "      <td>팔도</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>110</td>\n",
              "      <td>470.00</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1620.00</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>팔도 김치왕뚜껑</td>\n",
              "      <td>팔도</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>110</td>\n",
              "      <td>470.00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1790.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>팔도탄탄면 왕컵</td>\n",
              "      <td>팔도</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>116</td>\n",
              "      <td>500.00</td>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1700.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>김치사발면</td>\n",
              "      <td>농심</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>86</td>\n",
              "      <td>380.00</td>\n",
              "      <td>7.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>5.00</td>\n",
              "      <td>1520.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>592</th>\n",
              "      <td>옛날 자른당면</td>\n",
              "      <td>오뚜기</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>건면</td>\n",
              "      <td>20</td>\n",
              "      <td>13.63</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>열떡볶이면</td>\n",
              "      <td>오뚜기</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>140</td>\n",
              "      <td>470.00</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>13.00</td>\n",
              "      <td>800.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>진라면 용기</td>\n",
              "      <td>오뚜기</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>110</td>\n",
              "      <td>500.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>1540.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>605</th>\n",
              "      <td>진라면 매운맛</td>\n",
              "      <td>오뚜기</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>120</td>\n",
              "      <td>500.00</td>\n",
              "      <td>11.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>1860.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>606</th>\n",
              "      <td>진라면 순한맛</td>\n",
              "      <td>오뚜기</td>\n",
              "      <td>가공식품</td>\n",
              "      <td>유탕면</td>\n",
              "      <td>120</td>\n",
              "      <td>500.00</td>\n",
              "      <td>12.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1880.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>73 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0ca9d05-fa77-49be-87ee-04f955b81492')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f0ca9d05-fa77-49be-87ee-04f955b81492');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. 관계성 분석하기\n"
      ],
      "metadata": {
        "id": "C9R6zpkm5mVX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "df에 대해 **`kcal`**, **`protein`**, **`fat`**, **`carbohydrate`** 의 관계성을 파악해보세요.\n",
        "\n",
        "method는 pearson을 사용해주세요."
      ],
      "metadata": {
        "id": "hDySlC6LPyv9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfNutri = df[['kcal', 'protein', 'fat', 'carbohydrate']]\n",
        "\n",
        "dfNutri.corr(method= 'pearson')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "d1TvdX2F5Ynv",
        "outputId": "663278a7-9ea0-43a2-a98e-14b16ddfa8c4"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  kcal   protein       fat  carbohydrate\n",
              "kcal          1.000000  0.816188  0.777269      0.870106\n",
              "protein       0.816188  1.000000  0.513249      0.757226\n",
              "fat           0.777269  0.513249  1.000000      0.396166\n",
              "carbohydrate  0.870106  0.757226  0.396166      1.000000"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>kcal</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>carbohydrate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>kcal</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.816188</td>\n",
              "      <td>0.777269</td>\n",
              "      <td>0.870106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>protein</th>\n",
              "      <td>0.816188</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.513249</td>\n",
              "      <td>0.757226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fat</th>\n",
              "      <td>0.777269</td>\n",
              "      <td>0.513249</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.396166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carbohydrate</th>\n",
              "      <td>0.870106</td>\n",
              "      <td>0.757226</td>\n",
              "      <td>0.396166</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d893950c-7d32-46e2-9d15-dfd0f567c7bf')\"\n",
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              "        \n",
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              "      </button>\n",
              "      \n",
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              "\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d893950c-7d32-46e2-9d15-dfd0f567c7bf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d893950c-7d32-46e2-9d15-dfd0f567c7bf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "아래의 그래프 출력 코드는 따로 입력하지 않으셔도 괜찮습니다."
      ],
      "metadata": {
        "id": "WA-upCne9Nh6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g = sns.pairplot(dfNutri)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "id": "9PCTNiRC7jrg",
        "outputId": "67a8b847-46a2-4d29-f72c-7d6de628d878"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. T-TEST\n",
        "\n",
        "가설을 세우고 검증해봅시다!\n",
        "\n",
        "이번 실습에서 세울 가설은 바로, **`‘특정 제조사의 소듐 함량은 데이터 전체 라면에 대한 소듐 함량의 평균보다 많은 편일 것이다.’`** 입니다."
      ],
      "metadata": {
        "id": "--4a9J_WQylC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "적절한 가설 검증법을 사용해서 특정 제조사의 소듐 함량이 전체 라면들의 소듐 함량 **평균치**보다 많을지 확인해보세요.\n",
        "\n",
        "**[힌트]**\n",
        "  \n",
        "1. `totalSodiumMean` 에는 전체 라면에 대한 소듐 함량의 평균이 들어갑니다.\n",
        "2. 어떤 가설 검증법이 제일 적절할지 수업자료를 참조해서 파악해보세요. (심지어 구현 방법까지 다 나와 있습니다.)\n",
        "3. **어떤 가설 검증법을 사용해야할지 알 것 같다면, 해당 검증법의 api 문서에서 추가로 필요한 arguments가 없는지 파악하세요.**"
      ],
      "metadata": {
        "id": "EMDH8xdkSw_U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for manu in df['name_manufacturer'].unique():\n",
        "  yourManuSodium = df[df['name_manufacturer']==manu]['sodium']\n",
        "  siginificantLevel = 0.05\n",
        "\n",
        "  totalSodiumMean = df['sodium'].mean()\n",
        "\n",
        "  #######enter your code######\n",
        "  _, p_value = ttest_1samp(yourManuSodium, totalSodiumMean, alternative='greater')\n",
        "  print(\"P Value \", p_value)\n",
        "  ############################\n",
        "\n",
        "  if p < siginificantLevel:\n",
        "    print(f'sodium of {manu} is significant greater than all the ramens')\n",
        "  else:\n",
        "    print(f'sodium of {manu} is not significant greater than all the ramens')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGMUyf0ODY7b",
        "outputId": "a8f652e9-c4f9-420b-f613-023d9cb80889"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P Value  0.0007049430141221484\n",
            "sodium of 팔도 is significant greater than all the ramens\n",
            "P Value  0.8284706946331513\n",
            "sodium of 농심 is significant greater than all the ramens\n",
            "P Value  0.8761935419228963\n",
            "sodium of 오뚜기 is significant greater than all the ramens\n",
            "P Value  nan\n",
            "sodium of 롯데 is significant greater than all the ramens\n"
          ]
        }
      ]
    }
  ]
}