Day2_data_ch2.ipynb

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        "<a href=\"https://colab.research.google.com/github/esay2077/bigdata_analysis/blob/main/Day2_data_ch2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
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      "source": [
        "df = pd.read_csv('/content/titanic.csv')\n",
        "df"
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              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
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              "3              4         1       1   \n",
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              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
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              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
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              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
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              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
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              "3        0            113803  53.1000  C123        S  \n",
              "4        0            373450   8.0500   NaN        S  \n",
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              "888      2        W./C. 6607  23.4500   NaN        S  \n",
              "889      0            111369  30.0000  C148        C  \n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "AG_l3gbnQYFC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b3964a4-2f70-4ad7-d959-953a3d163976"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Survived\"] = df[\"Survived\"].astype(str)\n",
        "df[\"Pclass\"] = df[\"Pclass\"].astype(str)"
      ],
      "metadata": {
        "id": "sNQpULcUQYGk"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "id": "HZhiUFPgQYIe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "e6ac3930-8968-4c37-ca44-62150df7dca0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId Survived Pclass  \\\n",
              "0            1        0      3   \n",
              "1            2        1      1   \n",
              "2            3        1      3   \n",
              "3            4        1      1   \n",
              "4            5        0      3   \n",
              "5            6        0      3   \n",
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              "7            8        0      3   \n",
              "8            9        1      3   \n",
              "9           10        1      2   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "5                                   Moran, Mr. James    male   NaN      0   \n",
              "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
              "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
              "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
              "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  \n",
              "5      0            330877   8.4583   NaN        Q  \n",
              "6      0             17463  51.8625   E46        S  \n",
              "7      1            349909  21.0750   NaN        S  \n",
              "8      2            347742  11.1333   NaN        S  \n",
              "9      0            237736  30.0708   NaN        C  "
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4d758534-ca43-4b7d-9e96-61c0a21cd2eb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"3\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "d9-44yrgQYKp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b10e85c-6965-48a6-aa18-1f835295d3dd"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    object \n",
            " 2   Pclass       891 non-null    object \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(3), object(7)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped = df.groupby(\"Survived\")\n",
        "grouped.size()"
      ],
      "metadata": {
        "id": "FjLMjqrdQYNl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09b5a4c5-499d-43a2-e623-7bf015e28d64"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived\n",
              "0    549\n",
              "1    342\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df[\"Fare\"])\n",
        "plt.show()\n",
        "\n",
        "data_0 = df[df[\"Survived\"]==\"0\"][\"Fare\"] #사망자 요금\n",
        "data_1 = df[df[\"Survived\"]==\"1\"][\"Fare\"] #생존자 요금\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot([data_0, data_1])\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "SE0yKelRQYPv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "outputId": "8a7906ac-ad5b-4b34-f2c0-754542335ff7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped = df.groupby(\"Sex\")\n",
        "print(grouped.size())"
      ],
      "metadata": {
        "id": "x8O8eANOQYRx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cffe3a2d-ab1d-453a-bf19-3992d12ac24f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex\n",
            "female    314\n",
            "male      577\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_0 = df[df[\"Sex\"]==\"female\"][\"Survived\"]\n",
        "grouped = pd.DataFrame(data_0).groupby(\"Survived\")\n",
        "print(grouped.size())"
      ],
      "metadata": {
        "id": "LZ1bzqaIQYT8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3d3232d-1d7e-4385-c070-5f39ed7889f6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Survived\n",
            "0     81\n",
            "1    233\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_1 = df[df[\"Sex\"]==\"male\"]['Survived']\n",
        "grouped = pd.DataFrame(data_1).groupby(\"Survived\")\n",
        "print(grouped.size())"
      ],
      "metadata": {
        "id": "eKryFOVsQYWJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6185cf63-949f-403d-8878-4e8ddfade563"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Survived\n",
            "0    468\n",
            "1    109\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "데이터 전처리\n",
        "문자 - 숫자\n",
        "월 - 1 수치 -정규화\n",
        "동일 대상 분포 환산\n",
        "\n",
        "결측치\n",
        "삭제\n",
        "대체\n",
        "NaN\n",
        "\n",
        "틀린값\n",
        "버림\n",
        "대체\n",
        "분석보류\n",
        "\n",
        "변환\n",
        "범주형\n",
        "일반 정규화\n",
        "z-score 정규화 평균 0 표준편차 1\n",
        "로그변환 로그정규분포\n",
        "역수변환\n",
        "\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "Wcf86i73QYYR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#표준 정규화\n",
        "meat_buy_kr = 5*np.random.randn(1000) +53.9\n",
        "meat_buy_jp = 4*np.random.randn(1000) +32.7\n",
        "\n",
        "meat_buy = pd.DataFrame({\"한국인\":meat_buy_kr,\"일본인\":meat_buy_jp})\n",
        "\n",
        "meat_buy.head()\n"
      ],
      "metadata": {
        "id": "BKntCy1MQYaW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "42e73376-a04b-42d4-d3e6-6bed40ac8af6"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         한국인        일본인\n",
              "0  57.287410  31.460125\n",
              "1  56.882813  28.533231\n",
              "2  49.952845  38.563345\n",
              "3  47.079940  37.536193\n",
              "4  50.885495  28.920289"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>한국인</th>\n",
              "      <th>일본인</th>\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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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "meat_buy",
              "summary": "{\n  \"name\": \"meat_buy\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.042572841417663,\n        \"min\": 37.099009598233096,\n        \"max\": 71.99183626199189,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          54.45819098308084,\n          61.51527432998414,\n          49.94716325844553\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.039320124102997,\n        \"min\": 20.87247713742204,\n        \"max\": 44.58328185771026,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          41.48115801388499,\n          34.61908327054302,\n          38.01784975393825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(meat_buy_kr)\n",
        "plt.xlabel('korea')\n",
        "plt.show()\n",
        "\n",
        "plt.hist(meat_buy_jp)\n",
        "plt.xlabel('japan')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "-Yo-HlS7QYcl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "924737fc-82d7-4fac-9522-0083dd660c29"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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eT7JkyRI98cQT+v3vfy9JuvXWW4M+t2vXLk2fPl1Op1PV1dV64oknFAgElJ2drdLS0qD7UgAAwLUt5IAyffp0WZbVZ/ul2iTptttu0969e0P9WgAAcA3hb/EAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABgn5ICyZ88ezZ8/X5mZmXI4HNq6dWtQu2VZWr16tTIyMjR48GDNnDlTH330UVCfU6dOafHixXK5XEpJSVFRUZHOnDlzVQcCAADiR8gBpaOjQ5MmTdK6det6bX/qqaf0/PPPa8OGDdq3b5+uu+46zZ49W+fOnbP7LF68WB988IFqamq0bds27dmzR/fff/+VHwUAAIgriaF+oKCgQAUFBb22WZaltWvXatWqVfrKV74iSXr55ZeVnp6urVu36u6779aHH36o7du3a//+/Zo6daok6Sc/+Ynmzp2rZ555RpmZmVdxOAAAIB6E9R6UI0eOqLW1VTNnzrS3ud1u5ebmqq6uTpJUV1enlJQUO5xI0syZM5WQkKB9+/b1ut9AICC/3x+0AACA+BXWgNLa2ipJSk9PD9qenp5ut7W2tiotLS2oPTExUR6Px+7zSRUVFXK73faSlZUVzrIBAIBhYuIpnvLycvl8Pns5duxYtEsCAAARFNaA4vV6JUltbW1B29va2uw2r9erEydOBLV3dXXp1KlTdp9PcjqdcrlcQQsAAIhfYQ0o2dnZ8nq92rFjh73N7/dr3759ysvLkyTl5eWpvb1d9fX1dp+dO3equ7tbubm54SwHAADEqJCf4jlz5owOHz5srx85ckQNDQ3yeDwaNWqUVqxYoR/+8Ie68cYblZ2drR/84AfKzMzUggULJEnjx4/XnDlztGzZMm3YsEGdnZ0qKSnR3XffzRM8AABA0hUElAMHDmjGjBn2ellZmSRpyZIl2rRpkx599FF1dHTo/vvvV3t7u+666y5t375dgwYNsj+zefNmlZSUKD8/XwkJCSosLNTzzz8fhsMBAADxwGFZlhXtIkLl9/vldrvl8/m4HwX9asxjb0S7BCBsjlbOi3YJuMaE8vs7Jp7iAQAA1xYCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHESo10AACA6xjz2RrRLCNnRynnRLgH9hDMoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADG4TFjRE0sPuIIAOgfYT+DMmbMGDkcjh5LcXGxJGn69Ok92h588MFwlwEAAGJY2M+g7N+/XxcuXLDX33//ff3bv/2bvv71r9vbli1bpieffNJeHzJkSLjLAAAAMSzsAWXEiBFB65WVlRo7dqy+8IUv2NuGDBkir9cb7q8GAABxIqI3yZ4/f16//vWvdd9998nhcNjbN2/erOHDhysnJ0fl5eU6e/bsJfcTCATk9/uDFgAAEL8iepPs1q1b1d7erqVLl9rb7rnnHo0ePVqZmZk6dOiQVq5cqcbGRr3yyit97qeiokJr1qyJZKkAAMAgDsuyrEjtfPbs2UpKStLrr7/eZ5+dO3cqPz9fhw8f1tixY3vtEwgEFAgE7HW/36+srCz5fD65XK6w143+wVM8AELFHwuMbX6/X263+1P9/o7YGZS//e1vevvtty95ZkSScnNzJemSAcXpdMrpdIa9RgAAYKaI3YOyceNGpaWlad68S6fdhoYGSVJGRkakSgEAADEmImdQuru7tXHjRi1ZskSJif//K5qamlRVVaW5c+cqNTVVhw4dUmlpqaZNm6aJEydGohQAABCDIhJQ3n77bTU3N+u+++4L2p6UlKS3335ba9euVUdHh7KyslRYWKhVq1ZFogwAABCjIhJQZs2apd7uvc3KylJtbW0kvhIAAMQR/lggAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4YQ8oTzzxhBwOR9Aybtw4u/3cuXMqLi5Wamqqhg4dqsLCQrW1tYW7DAAAEMMicgbl5ptvVktLi7288847dltpaalef/11bdmyRbW1tTp+/LgWLlwYiTIAAECMSozIThMT5fV6e2z3+Xz61a9+paqqKn3xi1+UJG3cuFHjx4/X3r17dccdd/S6v0AgoEAgYK/7/f5IlA0AAAwRkTMoH330kTIzM3X99ddr8eLFam5uliTV19ers7NTM2fOtPuOGzdOo0aNUl1dXZ/7q6iokNvttpesrKxIlA0AAAwR9oCSm5urTZs2afv27Vq/fr2OHDmiz3/+8zp9+rRaW1uVlJSklJSUoM+kp6ertbW1z32Wl5fL5/PZy7Fjx8JdNgAAMEjYL/EUFBTYP0+cOFG5ubkaPXq0fvvb32rw4MFXtE+n0ymn0xmuEgEAgOEi/phxSkqKPvOZz+jw4cPyer06f/682tvbg/q0tbX1es8KAAC4NkU8oJw5c0ZNTU3KyMjQlClTNHDgQO3YscNub2xsVHNzs/Ly8iJdCgAAiBFhv8Tzve99T/Pnz9fo0aN1/PhxPf744xowYIAWLVokt9utoqIilZWVyePxyOVyafny5crLy+vzCR4AAHDtCXtA+fvf/65Fixbp5MmTGjFihO666y7t3btXI0aMkCQ999xzSkhIUGFhoQKBgGbPnq2f/exn4S4DAADEMIdlWVa0iwiV3++X2+2Wz+eTy+WKdjm4QmMeeyPaJQCIMUcr50W7BFyFUH5/87d4AACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxwv6qe0QHb2UFAMQTzqAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIzDe1AAADEjFt/5dLRyXrRLiEmcQQEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOGEPKBUVFbr99tuVnJystLQ0LViwQI2NjUF9pk+fLofDEbQ8+OCD4S4FAADEqLAHlNraWhUXF2vv3r2qqalRZ2enZs2apY6OjqB+y5YtU0tLi7089dRT4S4FAADEqMRw73D79u1B65s2bVJaWprq6+s1bdo0e/uQIUPk9XrD/fUAACAORPweFJ/PJ0nyeDxB2zdv3qzhw4crJydH5eXlOnv2bJ/7CAQC8vv9QQsAAIhfYT+D8q+6u7u1YsUK3XnnncrJybG333PPPRo9erQyMzN16NAhrVy5Uo2NjXrllVd63U9FRYXWrFkTyVIBAIBBHJZlWZHa+UMPPaQ333xT77zzjkaOHNlnv507dyo/P1+HDx/W2LFje7QHAgEFAgF73e/3KysrSz6fTy6XKyK1x5oxj70R7RIAAL04Wjkv2iUYw+/3y+12f6rf3xE7g1JSUqJt27Zpz549lwwnkpSbmytJfQYUp9Mpp9MZkToBAIB5wh5QLMvS8uXL9eqrr2r37t3Kzs6+7GcaGhokSRkZGeEuBwAAxKCwB5Ti4mJVVVXptddeU3JyslpbWyVJbrdbgwcPVlNTk6qqqjR37lylpqbq0KFDKi0t1bRp0zRx4sRwlwMAAGJQ2APK+vXrJf3fy9j+1caNG7V06VIlJSXp7bff1tq1a9XR0aGsrCwVFhZq1apV4S4FAADEqIhc4rmUrKws1dbWhvtrAQBAHOFv8QAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMkRrsAE4157I1olwAAwDWNgAIAQATF6n96j1bOi+r3c4kHAAAYh4ACAACMQ0ABAADGIaAAAADjRDWgrFu3TmPGjNGgQYOUm5urP/3pT9EsBwAAGCJqAeU3v/mNysrK9Pjjj+vgwYOaNGmSZs+erRMnTkSrJAAAYIioBZRnn31Wy5Yt07333qsJEyZow4YNGjJkiF588cVolQQAAAwRlfegnD9/XvX19SovL7e3JSQkaObMmaqrq+vRPxAIKBAI2Os+n0+S5Pf7I1Jfd+BsRPYLAECsiMTv2Iv7tCzrsn2jElD+8Y9/6MKFC0pPTw/anp6err/+9a89+ldUVGjNmjU9tmdlZUWsRgAArmXutZHb9+nTp+V2uy/ZJybeJFteXq6ysjJ7vbu7W6dOnVJqaqocDoe93e/3KysrS8eOHZPL5YpGqdcsxj56GPvoYeyjh7GPnqsZe8uydPr0aWVmZl62b1QCyvDhwzVgwAC1tbUFbW9ra5PX6+3R3+l0yul0Bm1LSUnpc/8ul4sJGyWMffQw9tHD2EcPYx89Vzr2lztzclFUbpJNSkrSlClTtGPHDntbd3e3duzYoby8vGiUBAAADBK1SzxlZWVasmSJpk6dqs9+9rNau3atOjo6dO+990arJAAAYIioBZRvfvOb+vjjj7V69Wq1trbq1ltv1fbt23vcOBsKp9Opxx9/vMflIEQeYx89jH30MPbRw9hHT3+NvcP6NM/6AAAA9CP+Fg8AADAOAQUAABiHgAIAAIxDQAEAAMaJuYBSUVGh22+/XcnJyUpLS9OCBQvU2NgY1OfcuXMqLi5Wamqqhg4dqsLCwh4vhUPoPs3YT58+XQ6HI2h58MEHo1RxfFm/fr0mTpxovxwpLy9Pb775pt3OvI+cy409875/VFZWyuFwaMWKFfY25n3/6G3sIz3vYy6g1NbWqri4WHv37lVNTY06Ozs1a9YsdXR02H1KS0v1+uuva8uWLaqtrdXx48e1cOHCKFYdHz7N2EvSsmXL1NLSYi9PPfVUlCqOLyNHjlRlZaXq6+t14MABffGLX9RXvvIVffDBB5KY95F0ubGXmPeRtn//fv385z/XxIkTg7Yz7yOvr7GXIjzvrRh34sQJS5JVW1trWZZltbe3WwMHDrS2bNli9/nwww8tSVZdXV20yoxLnxx7y7KsL3zhC9Z3v/vd6BV1jRk2bJj1y1/+knkfBRfH3rKY95F2+vRp68Ybb7RqamqCxpp5H3l9jb1lRX7ex9wZlE/y+XySJI/HI0mqr69XZ2enZs6cafcZN26cRo0apbq6uqjUGK8+OfYXbd68WcOHD1dOTo7Ky8t19uzZaJQX1y5cuKDq6mp1dHQoLy+Ped+PPjn2FzHvI6e4uFjz5s0Lmt8S/973h77G/qJIzvuY+GvGfenu7taKFSt05513KicnR5LU2tqqpKSkHn9MMD09Xa2trVGoMj71NvaSdM8992j06NHKzMzUoUOHtHLlSjU2NuqVV16JYrXx47333lNeXp7OnTunoUOH6tVXX9WECRPU0NDAvI+wvsZeYt5HUnV1tQ4ePKj9+/f3aOPf+8i61NhLkZ/3MR1QiouL9f777+udd96JdinXnL7G/v7777d/vuWWW5SRkaH8/Hw1NTVp7Nix/V1m3LnpppvU0NAgn8+n3/3ud1qyZIlqa2ujXdY1oa+xnzBhAvM+Qo4dO6bvfve7qqmp0aBBg6JdzjXl04x9pOd9zF7iKSkp0bZt27Rr1y6NHDnS3u71enX+/Hm1t7cH9W9ra5PX6+3nKuNTX2Pfm9zcXEnS4cOH+6O0uJeUlKQbbrhBU6ZMUUVFhSZNmqQf//jHzPt+0NfY94Z5Hx719fU6ceKEbrvtNiUmJioxMVG1tbV6/vnnlZiYqPT0dOZ9hFxu7C9cuNDjM+Ge9zEXUCzLUklJiV599VXt3LlT2dnZQe1TpkzRwIEDtWPHDntbY2Ojmpubg64XI3SXG/veNDQ0SJIyMjIiXN21qbu7W4FAgHkfBRfHvjfM+/DIz8/Xe++9p4aGBnuZOnWqFi9ebP/MvI+My439gAEDenwm3PM+5i7xFBcXq6qqSq+99pqSk5Pt64xut1uDBw+W2+1WUVGRysrK5PF45HK5tHz5cuXl5emOO+6IcvWx7XJj39TUpKqqKs2dO1epqak6dOiQSktLNW3atF4fT0NoysvLVVBQoFGjRun06dOqqqrS7t279dZbbzHvI+xSY8+8j5zk5OSge9wk6brrrlNqaqq9nXkfGZcb+36Z9xF7PihCJPW6bNy40e7zv//7v9bDDz9sDRs2zBoyZIj11a9+1WppaYle0XHicmPf3NxsTZs2zfJ4PJbT6bRuuOEG65FHHrF8Pl90C48T9913nzV69GgrKSnJGjFihJWfn2/94Q9/sNuZ95FzqbFn3vevTz7ayrzvP/869v0x7x2WZVnhiToAAADhEXP3oAAAgPhHQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAiBsli5dqgULFkS7DABxgDfJAggbn88ny7KUkpIS7VIAxDgCCgAAMA6XeACEzb9e4tm+fbvuuusupaSkKDU1VV/60pfU1NRk9z169KgcDoeqq6v1uc99ToMGDVJOTo5qa2vtPhcuXFBRUZGys7M1ePBg3XTTTfrxj3/c63c+88wzysjIUGpqqoqLi9XZ2dkvxwwgMggoACKio6NDZWVlOnDggHbs2KGEhAR99atfVXd3d1C/Rx55RP/xH/+hP//5z8rLy9P8+fN18uRJSVJ3d7dGjhypLVu26C9/+YtWr16t73//+/rtb38btI9du3apqalJu3bt0ksvvaRNmzZp06ZN/XWoACKASzwAwmbp0qVqb2/X1q1be7T94x//0IgRI/Tee+8pJydHR48eVXZ2tiorK7Vy5UpJUldXl7Kzs7V8+XI9+uijvX5HSUmJWltb9bvf/c7+zt27d6upqUkDBgyQJH3jG99QQkKCqqurI3OgACKOMygAIuKjjz7SokWLdP3118vlcmnMmDGSpObm5qB+eXl59s+JiYmaOnWqPvzwQ3vbunXrNGXKFI0YMUJDhw7VCy+80GMfN998sx1OJCkjI0MnTpyIwFEB6C8EFAARMX/+fJ06dUq/+MUvtG/fPu3bt0+SdP78+U+9j+rqan3ve99TUVGR/vCHP6ihoUH33ntvj30MHDgwaN3hcPS4lAQgthBQAITdyZMn1djYqFWrVik/P1/jx4/XP//5z1777t271/65q6tL9fX1Gj9+vCTpf/7nf/S5z31ODz/8sCZPnqwbbrgh6EZbAPErMdoFAIg/w4YNU2pqql544QVlZGSoublZjz32WK99161bpxtvvFHjx4/Xc889p3/+85+67777JEk33nijXn75Zb311lvKzs7Wf/3Xf2n//v3Kzs7uz8MBEAWcQQEQdhdvUK2vr1dOTo5KS0v19NNP99q3srJSlZWVmjRpkt555x39/ve/1/DhwyVJDzzwgBYuXKhvfvObys3N1cmTJ/Xwww/356EAiBKe4gEQNosWLdKAAQP061//+rJ9Lz7F8+c//1m33npr5IsDEFM4gwLgqnV1dekvf/mL6urqdPPNN0e7HABxgIAC4Kq9//77mjp1qm6++WY9+OCD0S4HQBzgEg8AADAOZ1AAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOP8P1GsonXQHgPkAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#z-표준화\n",
        "df1 = pd.read_csv(\"/content/df_sample.csv\")\n",
        "\n",
        "mid_avg = np.mean(df1[\"중간\"]) #평균\n",
        "mid_std = np.std(df1[\"중간\"]) #표준편차\n",
        "\n",
        "df1[\"중간_z점수\"] = (df1[\"중간\"]-mid_avg) / mid_std\n",
        "\n",
        "df1.head()"
      ],
      "metadata": {
        "id": "5jGP7_H1QYeo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "738504f5-b26e-4f42-d8bf-cfa3e9ec5d6c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    학번  중간  기말  리포트  퀴즈    중간_z점수\n",
              "0  S01  90  95   20  20  1.231167\n",
              "1  S02  82  83   18  18  0.436866\n",
              "2  S03  80  78   18  18  0.238290\n",
              "3  S04  78  75   10  10  0.039715\n",
              "4  S05  93  91   12  12  1.529030"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-357249c6-ce39-41da-8109-3fb986980d67\" 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>학번</th>\n",
              "      <th>중간</th>\n",
              "      <th>기말</th>\n",
              "      <th>리포트</th>\n",
              "      <th>퀴즈</th>\n",
              "      <th>중간_z점수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>S01</td>\n",
              "      <td>90</td>\n",
              "      <td>95</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>1.231167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S02</td>\n",
              "      <td>82</td>\n",
              "      <td>83</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>0.436866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>S03</td>\n",
              "      <td>80</td>\n",
              "      <td>78</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>0.238290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>S04</td>\n",
              "      <td>78</td>\n",
              "      <td>75</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0.039715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>S05</td>\n",
              "      <td>93</td>\n",
              "      <td>91</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1.529030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      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-357249c6-ce39-41da-8109-3fb986980d67 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-357249c6-ce39-41da-8109-3fb986980d67');\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",
              "\n",
              "\n",
              "<div id=\"df-c264cab4-3772-4f6c-aa16-f8d520bd3a72\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c264cab4-3772-4f6c-aa16-f8d520bd3a72')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c264cab4-3772-4f6c-aa16-f8d520bd3a72 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"\\ud559\\ubc88\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"S09\",\n          \"S02\",\n          \"S06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc911\\uac04\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 60,\n        \"max\": 93,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          65,\n          82,\n          71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uae30\\ub9d0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 65,\n        \"max\": 95,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          95,\n          83,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub9ac\\ud3ec\\ud2b8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 10,\n        \"max\": 20,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          20,\n          18,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud034\\uc988\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 10,\n        \"max\": 20,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          20,\n          18,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc911\\uac04_z\\uc810\\uc218\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0540925533894596,\n        \"min\": -1.7474632357369821,\n        \"max\": 1.5290303312698605,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -1.2510248164935212,\n          0.43686580893424626,\n          -0.6552987134013679\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as ss\n",
        "\n",
        "meat_buy[\"한국인 정규화\"] = ss.zscore(meat_buy_kr)\n",
        "meat_buy[\"일본인 정규화\"] = ss.zscore(meat_buy_jp)\n",
        "\n",
        "meat_buy.head()"
      ],
      "metadata": {
        "id": "IyUnhc5qQYg2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "fc97a4e8-b057-4f4b-de01-20ebb00bdf3f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         한국인        일본인   한국인 정규화   일본인 정규화\n",
              "0  57.287410  31.460125  0.689364 -0.285626\n",
              "1  56.882813  28.533231  0.609087 -1.010589\n",
              "2  49.952845  38.563345 -0.765892  1.473773\n",
              "3  47.079940  37.536193 -1.335907  1.219357\n",
              "4  50.885495  28.920289 -0.580844 -0.914718"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca8a6626-9489-4eb7-9c0e-4ca45e39f9cb\" 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>한국인</th>\n",
              "      <th>일본인</th>\n",
              "      <th>한국인 정규화</th>\n",
              "      <th>일본인 정규화</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57.287410</td>\n",
              "      <td>31.460125</td>\n",
              "      <td>0.689364</td>\n",
              "      <td>-0.285626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56.882813</td>\n",
              "      <td>28.533231</td>\n",
              "      <td>0.609087</td>\n",
              "      <td>-1.010589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49.952845</td>\n",
              "      <td>38.563345</td>\n",
              "      <td>-0.765892</td>\n",
              "      <td>1.473773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47.079940</td>\n",
              "      <td>37.536193</td>\n",
              "      <td>-1.335907</td>\n",
              "      <td>1.219357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.885495</td>\n",
              "      <td>28.920289</td>\n",
              "      <td>-0.580844</td>\n",
              "      <td>-0.914718</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca8a6626-9489-4eb7-9c0e-4ca45e39f9cb')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-ca8a6626-9489-4eb7-9c0e-4ca45e39f9cb 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-ca8a6626-9489-4eb7-9c0e-4ca45e39f9cb');\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",
              "\n",
              "\n",
              "<div id=\"df-f2bf8d05-95db-4b2a-8d06-edb59d3b1af9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2bf8d05-95db-4b2a-8d06-edb59d3b1af9')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "meat_buy[\"한국인 정규화2\"] = (meat_buy_kr - np.mean(meat_buy_kr))/np.std(meat_buy_kr)\n",
        "meat_buy[\"일본인 정규화2\"] = (meat_buy_jp - np.mean(meat_buy_jp))/np.std(meat_buy_jp)\n",
        "\n",
        "meat_buy.head()\n"
      ],
      "metadata": {
        "id": "v09qjgyzQYi-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ba91f2a2-08ff-47ff-c554-b40b29d5e124"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         한국인        일본인   한국인 정규화   일본인 정규화  한국인 정규화2  일본인 정규화2\n",
              "0  57.287410  31.460125  0.689364 -0.285626  0.689364 -0.285626\n",
              "1  56.882813  28.533231  0.609087 -1.010589  0.609087 -1.010589\n",
              "2  49.952845  38.563345 -0.765892  1.473773 -0.765892  1.473773\n",
              "3  47.079940  37.536193 -1.335907  1.219357 -1.335907  1.219357\n",
              "4  50.885495  28.920289 -0.580844 -0.914718 -0.580844 -0.914718"
            ],
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              "      <th></th>\n",
              "      <th>한국인</th>\n",
              "      <th>일본인</th>\n",
              "      <th>한국인 정규화</th>\n",
              "      <th>일본인 정규화</th>\n",
              "      <th>한국인 정규화2</th>\n",
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              "      <th>0</th>\n",
              "      <td>57.287410</td>\n",
              "      <td>31.460125</td>\n",
              "      <td>0.689364</td>\n",
              "      <td>-0.285626</td>\n",
              "      <td>0.689364</td>\n",
              "      <td>-0.285626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56.882813</td>\n",
              "      <td>28.533231</td>\n",
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              "      <td>-1.010589</td>\n",
              "      <td>0.609087</td>\n",
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              "      <th>2</th>\n",
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              "      <td>38.563345</td>\n",
              "      <td>-0.765892</td>\n",
              "      <td>1.473773</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47.079940</td>\n",
              "      <td>37.536193</td>\n",
              "      <td>-1.335907</td>\n",
              "      <td>1.219357</td>\n",
              "      <td>-1.335907</td>\n",
              "      <td>1.219357</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.885495</td>\n",
              "      <td>28.920289</td>\n",
              "      <td>-0.580844</td>\n",
              "      <td>-0.914718</td>\n",
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              "    async function quickchart(key) {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "meat_buy",
              "summary": "{\n  \"name\": \"meat_buy\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.042572841417663,\n        \"min\": 37.099009598233096,\n        \"max\": 71.99183626199189,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          54.45819098308084,\n          61.51527432998414,\n          49.94716325844553\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.039320124102997,\n        \"min\": 20.87247713742204,\n        \"max\": 44.58328185771026,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          41.48115801388499,\n          34.61908327054302,\n          38.01784975393825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778 \\uc815\\uaddc\\ud654\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -3.3162306871330793,\n        \"max\": 3.6068792550922857,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.1280165328594633,\n          1.5282173349573982,\n          -0.76701960711925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778 \\uc815\\uaddc\\ud654\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -2.90808327520434,\n        \"max\": 2.9648528360142,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          2.196486879700423,\n          0.4968175924927015,\n          1.338659050402041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778 \\uc815\\uaddc\\ud6542\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -3.3162306871330793,\n        \"max\": 3.6068792550922857,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.1280165328594633,\n          1.5282173349573982,\n          -0.76701960711925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778 \\uc815\\uaddc\\ud6542\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -2.90808327520434,\n        \"max\": 2.9648528360142,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          2.196486879700423,\n          0.4968175924927015,\n          1.338659050402041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(meat_buy['한국인 정규화'])\n",
        "plt.xlabel('korea')\n",
        "plt.show()\n",
        "\n",
        "plt.hist(meat_buy['일본인 정규화'])\n",
        "plt.xlabel('japen')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3AZlpWV0QYlI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "96898a6e-cad1-4e42-e352-2805b277dc88"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "meat_buy['한국인 정규화3'] = scaler.fit_transform(meat_buy[['한국인']])\n",
        "\n",
        "scaler = StandardScaler()\n",
        "meat_buy['일본인 정규화3'] = scaler.fit_transform(meat_buy[['일본인']])\n"
      ],
      "metadata": {
        "id": "aF3EAlzeQYnF"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "meat_buy.head()"
      ],
      "metadata": {
        "id": "gW9sZ2y4QYpN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "bb33419e-bbc5-48f0-8c29-0fa441d5d61a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         한국인        일본인   한국인 정규화   일본인 정규화  한국인 정규화2  일본인 정규화2  한국인 정규화3  \\\n",
              "0  57.287410  31.460125  0.689364 -0.285626  0.689364 -0.285626  0.689364   \n",
              "1  56.882813  28.533231  0.609087 -1.010589  0.609087 -1.010589  0.609087   \n",
              "2  49.952845  38.563345 -0.765892  1.473773 -0.765892  1.473773 -0.765892   \n",
              "3  47.079940  37.536193 -1.335907  1.219357 -1.335907  1.219357 -1.335907   \n",
              "4  50.885495  28.920289 -0.580844 -0.914718 -0.580844 -0.914718 -0.580844   \n",
              "\n",
              "   일본인 정규화3  \n",
              "0 -0.285626  \n",
              "1 -1.010589  \n",
              "2  1.473773  \n",
              "3  1.219357  \n",
              "4 -0.914718  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-23c279f2-69bc-43a2-8e9c-7a971ade06db\" 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>한국인</th>\n",
              "      <th>일본인</th>\n",
              "      <th>한국인 정규화</th>\n",
              "      <th>일본인 정규화</th>\n",
              "      <th>한국인 정규화2</th>\n",
              "      <th>일본인 정규화2</th>\n",
              "      <th>한국인 정규화3</th>\n",
              "      <th>일본인 정규화3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57.287410</td>\n",
              "      <td>31.460125</td>\n",
              "      <td>0.689364</td>\n",
              "      <td>-0.285626</td>\n",
              "      <td>0.689364</td>\n",
              "      <td>-0.285626</td>\n",
              "      <td>0.689364</td>\n",
              "      <td>-0.285626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56.882813</td>\n",
              "      <td>28.533231</td>\n",
              "      <td>0.609087</td>\n",
              "      <td>-1.010589</td>\n",
              "      <td>0.609087</td>\n",
              "      <td>-1.010589</td>\n",
              "      <td>0.609087</td>\n",
              "      <td>-1.010589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49.952845</td>\n",
              "      <td>38.563345</td>\n",
              "      <td>-0.765892</td>\n",
              "      <td>1.473773</td>\n",
              "      <td>-0.765892</td>\n",
              "      <td>1.473773</td>\n",
              "      <td>-0.765892</td>\n",
              "      <td>1.473773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47.079940</td>\n",
              "      <td>37.536193</td>\n",
              "      <td>-1.335907</td>\n",
              "      <td>1.219357</td>\n",
              "      <td>-1.335907</td>\n",
              "      <td>1.219357</td>\n",
              "      <td>-1.335907</td>\n",
              "      <td>1.219357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.885495</td>\n",
              "      <td>28.920289</td>\n",
              "      <td>-0.580844</td>\n",
              "      <td>-0.914718</td>\n",
              "      <td>-0.580844</td>\n",
              "      <td>-0.914718</td>\n",
              "      <td>-0.580844</td>\n",
              "      <td>-0.914718</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23c279f2-69bc-43a2-8e9c-7a971ade06db')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "    }\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-23c279f2-69bc-43a2-8e9c-7a971ade06db 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-23c279f2-69bc-43a2-8e9c-7a971ade06db');\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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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2dd6bbe4-9461-4370-8b7d-63ce765c20da button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "meat_buy",
              "summary": "{\n  \"name\": \"meat_buy\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.042572841417663,\n        \"min\": 37.099009598233096,\n        \"max\": 71.99183626199189,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          54.45819098308084,\n          61.51527432998414,\n          49.94716325844553\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.039320124102997,\n        \"min\": 20.87247713742204,\n        \"max\": 44.58328185771026,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          41.48115801388499,\n          34.61908327054302,\n          38.01784975393825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778 \\uc815\\uaddc\\ud654\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -3.3162306871330793,\n        \"max\": 3.6068792550922857,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.1280165328594633,\n          1.5282173349573982,\n          -0.76701960711925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778 \\uc815\\uaddc\\ud654\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -2.90808327520434,\n        \"max\": 2.9648528360142,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          2.196486879700423,\n          0.4968175924927015,\n          1.338659050402041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778 \\uc815\\uaddc\\ud6542\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -3.3162306871330793,\n        \"max\": 3.6068792550922857,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.1280165328594633,\n          1.5282173349573982,\n          -0.76701960711925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778 \\uc815\\uaddc\\ud6542\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -2.90808327520434,\n        \"max\": 2.9648528360142,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          2.196486879700423,\n          0.4968175924927015,\n          1.338659050402041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778 \\uc815\\uaddc\\ud6543\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -3.3162306871330793,\n        \"max\": 3.6068792550922857,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.1280165328594633,\n          1.5282173349573982,\n          -0.76701960711925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778 \\uc815\\uaddc\\ud6543\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0005003753127737,\n        \"min\": -2.90808327520434,\n        \"max\": 2.9648528360142,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          2.196486879700423,\n          0.4968175924927015,\n          1.338659050402041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fit_min = np.min(df1['기말'])\n",
        "fit_max = np.max(df1['기말'])\n",
        "\n",
        "df1['기말 정규화'] = (df1['기말']-fit_min)/(fit_max - fit_min)\n",
        "\n",
        "df1.head()"
      ],
      "metadata": {
        "id": "5X15iCFPQYrn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "559b22be-cf11-416d-cbe9-5301136c619e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    학번  중간  기말  리포트  퀴즈    중간_z점수    기말 정규화\n",
              "0  S01  90  95   20  20  1.231167  1.000000\n",
              "1  S02  82  83   18  18  0.436866  0.600000\n",
              "2  S03  80  78   18  18  0.238290  0.433333\n",
              "3  S04  78  75   10  10  0.039715  0.333333\n",
              "4  S05  93  91   12  12  1.529030  0.866667"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-320a57b5-9c8c-4103-9b2d-d310d2af002b\" 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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>학번</th>\n",
              "      <th>중간</th>\n",
              "      <th>기말</th>\n",
              "      <th>리포트</th>\n",
              "      <th>퀴즈</th>\n",
              "      <th>중간_z점수</th>\n",
              "      <th>기말 정규화</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>S01</td>\n",
              "      <td>90</td>\n",
              "      <td>95</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>1.231167</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S02</td>\n",
              "      <td>82</td>\n",
              "      <td>83</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>0.436866</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>S03</td>\n",
              "      <td>80</td>\n",
              "      <td>78</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>0.238290</td>\n",
              "      <td>0.433333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>S04</td>\n",
              "      <td>78</td>\n",
              "      <td>75</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0.039715</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>S05</td>\n",
              "      <td>93</td>\n",
              "      <td>91</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1.529030</td>\n",
              "      <td>0.866667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-320a57b5-9c8c-4103-9b2d-d310d2af002b 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-320a57b5-9c8c-4103-9b2d-d310d2af002b');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d4cf91cd-de0b-4f9b-9faa-599aaf3efa45')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d4cf91cd-de0b-4f9b-9faa-599aaf3efa45 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "  </div>\n"
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"\\ud559\\ubc88\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"S09\",\n          \"S02\",\n          \"S06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc911\\uac04\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 60,\n        \"max\": 93,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          65,\n          82,\n          71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uae30\\ub9d0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 65,\n        \"max\": 95,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          95,\n          83,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub9ac\\ud3ec\\ud2b8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 10,\n        \"max\": 20,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          20,\n          18,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud034\\uc988\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 10,\n        \"max\": 20,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          20,\n          18,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc911\\uac04_z\\uc810\\uc218\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0540925533894596,\n        \"min\": -1.7474632357369821,\n        \"max\": 1.5290303312698605,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -1.2510248164935212,\n          0.43686580893424626,\n          -0.6552987134013679\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uae30\\ub9d0 \\uc815\\uaddc\\ud654\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32212642254053836,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          0.6,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "meat_buy['한국인_mm']  = scaler.fit_transform(meat_buy[['한국인']])\n",
        "meat_buy['일본인_mm'] = scaler.fit_transform(meat_buy[['일본인']])\n",
        "\n",
        "meat_buy[['한국인','일본인','한국인_mm','일본인_mm']].head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nWZm9hID0rOy",
        "outputId": "b2ccec01-9959-46ac-aa31-c298428282a5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         한국인        일본인    한국인_mm    일본인_mm\n",
              "0  57.287410  31.460125  0.578583  0.446533\n",
              "1  56.882813  28.533231  0.566988  0.323091\n",
              "2  49.952845  38.563345  0.368380  0.746110\n",
              "3  47.079940  37.536193  0.286045  0.702790\n",
              "4  50.885495  28.920289  0.395109  0.339415"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d817af3-951e-444b-832d-6f278033b8d3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>한국인</th>\n",
              "      <th>일본인</th>\n",
              "      <th>한국인_mm</th>\n",
              "      <th>일본인_mm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57.287410</td>\n",
              "      <td>31.460125</td>\n",
              "      <td>0.578583</td>\n",
              "      <td>0.446533</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56.882813</td>\n",
              "      <td>28.533231</td>\n",
              "      <td>0.566988</td>\n",
              "      <td>0.323091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49.952845</td>\n",
              "      <td>38.563345</td>\n",
              "      <td>0.368380</td>\n",
              "      <td>0.746110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47.079940</td>\n",
              "      <td>37.536193</td>\n",
              "      <td>0.286045</td>\n",
              "      <td>0.702790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.885495</td>\n",
              "      <td>28.920289</td>\n",
              "      <td>0.395109</td>\n",
              "      <td>0.339415</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d817af3-951e-444b-832d-6f278033b8d3')\"\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 -960 960 960\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      display: none;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5d817af3-951e-444b-832d-6f278033b8d3 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-5d817af3-951e-444b-832d-6f278033b8d3');\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",
              "\n",
              "\n",
              "<div id=\"df-d92e6aaa-ae94-4a09-adc7-d664397e8cf5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d92e6aaa-ae94-4a09-adc7-d664397e8cf5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d92e6aaa-ae94-4a09-adc7-d664397e8cf5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Min = np.min(meat_buy_kr)\n",
        "Max = np.max(meat_buy_kr)\n",
        "meat_buy['한국인_mm2'] = (meat_buy[['한국인']]- Min)/(Max - Min)\n",
        "\n",
        "Min = np.min(meat_buy_jp)\n",
        "Max = np.max(meat_buy_jp)\n",
        "meat_buy['일본인_mm2'] = (meat_buy[['일본인']]-Min)/(Max - Min)\n",
        "\n",
        "meat_buy[['한국인_mm','일본인_mm','한국인_mm2','일본인_mm2']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RdxsG7hW0rSR",
        "outputId": "d87e08ff-935e-48b8-facf-fb66164fd05a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     한국인_mm    일본인_mm   한국인_mm2   일본인_mm2\n",
              "0  0.578583  0.446533  0.578583  0.446533\n",
              "1  0.566988  0.323091  0.566988  0.323091\n",
              "2  0.368380  0.746110  0.368380  0.746110\n",
              "3  0.286045  0.702790  0.286045  0.702790\n",
              "4  0.395109  0.339415  0.395109  0.339415"
            ],
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              "      <th></th>\n",
              "      <th>한국인_mm</th>\n",
              "      <th>일본인_mm</th>\n",
              "      <th>한국인_mm2</th>\n",
              "      <th>일본인_mm2</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.578583</td>\n",
              "      <td>0.446533</td>\n",
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              "      <td>0.446533</td>\n",
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              "      <td>0.566988</td>\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"meat_buy[['\\ud55c\\uad6d\\uc778_mm','\\uc77c\\ubcf8\\uc778_mm','\\ud55c\\uad6d\\uc778_mm2','\\uc77c\\ubcf8\\uc778_mm2']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12862049169359563,\n        \"min\": 0.2860453497389006,\n        \"max\": 0.5785831281717058,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5669876944484513,\n          0.39510946001128056,\n          0.3683804443016263\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20060245442733637,\n        \"min\": 0.3230912714392624,\n        \"max\": 0.7461099592353919,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3230912714392624,\n          0.33941539455319725,\n          0.7461099592353919\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ud55c\\uad6d\\uc778_mm2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12862049169359568,\n        \"min\": 0.28604534973890067,\n        \"max\": 0.5785831281717059,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5669876944484514,\n          0.39510946001128056,\n          0.3683804443016263\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc77c\\ubcf8\\uc778_mm2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20060245442733635,\n        \"min\": 0.3230912714392623,\n        \"max\": 0.7461099592353918,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3230912714392623,\n          0.33941539455319725,\n          0.7461099592353918\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#정규분포변환\n",
        "\"\"\"\n",
        "왜도란 대칭정도\n",
        "양수 : 평균>중앙>최빈\n",
        "0 : 평 = 중 = 최빈\n",
        "음수 : 평 < 중 < 최빈"
      ],
      "metadata": {
        "id": "uf1pF-Sh0rVl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/USJudgeRatings.csv\")\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7Y3u9vdL0rZP",
        "outputId": "27dba94d-9ef9-477b-957a-c1edde30f03c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  CONT  INTG  DMNR  DILG  CFMG  DECI  PREP  FAMI  ORAL  WRIT  \\\n",
              "0   AARONSON,L.H.   5.7   7.9   7.7   7.3   7.1   7.4   7.1   7.1   7.1   7.0   \n",
              "1  ALEXANDER,J.M.   6.8   8.9   8.8   8.5   7.8   8.1   8.0   8.0   7.8   7.9   \n",
              "2  ARMENTANO,A.J.   7.2   8.1   7.8   7.8   7.5   7.6   7.5   7.5   7.3   7.4   \n",
              "3     BERDON,R.I.   6.8   8.8   8.5   8.8   8.3   8.5   8.7   8.7   8.4   8.5   \n",
              "4    BRACKEN,J.J.   7.3   6.4   4.3   6.5   6.0   6.2   5.7   5.7   5.1   5.3   \n",
              "\n",
              "   PHYS  RTEN  \n",
              "0   8.3   7.8  \n",
              "1   8.5   8.7  \n",
              "2   7.9   7.8  \n",
              "3   8.8   8.7  \n",
              "4   5.5   4.8  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>CONT</th>\n",
              "      <th>INTG</th>\n",
              "      <th>DMNR</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AARONSON,L.H.</td>\n",
              "      <td>5.7</td>\n",
              "      <td>7.9</td>\n",
              "      <td>7.7</td>\n",
              "      <td>7.3</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.4</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>7.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ALEXANDER,J.M.</td>\n",
              "      <td>6.8</td>\n",
              "      <td>8.9</td>\n",
              "      <td>8.8</td>\n",
              "      <td>8.5</td>\n",
              "      <td>7.8</td>\n",
              "      <td>8.1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.8</td>\n",
              "      <td>7.9</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ARMENTANO,A.J.</td>\n",
              "      <td>7.2</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.8</td>\n",
              "      <td>7.8</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.6</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.3</td>\n",
              "      <td>7.4</td>\n",
              "      <td>7.9</td>\n",
              "      <td>7.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BERDON,R.I.</td>\n",
              "      <td>6.8</td>\n",
              "      <td>8.8</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.7</td>\n",
              "      <td>8.7</td>\n",
              "      <td>8.4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>8.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BRACKEN,J.J.</td>\n",
              "      <td>7.3</td>\n",
              "      <td>6.4</td>\n",
              "      <td>4.3</td>\n",
              "      <td>6.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>5.7</td>\n",
              "      <td>5.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>5.3</td>\n",
              "      <td>5.5</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1953980-75fa-49b1-bb79-a270bfb24e16')\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [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-f1953980-75fa-49b1-bb79-a270bfb24e16 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-f1953980-75fa-49b1-bb79-a270bfb24e16');\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",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e3c3de19-d817-4d11-93ab-ae1e5255cc3e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 43,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 43,\n        \"samples\": [\n          \"STAPLETON,J.F.\",\n          \"MULVEY,H.M.\",\n          \"NARUK,H.J.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CONT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9408768088518368,\n        \"min\": 5.7,\n        \"max\": 10.6,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          6.5,\n          6.6,\n          5.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INTG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7701447419725982,\n        \"min\": 5.9,\n        \"max\": 9.2,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          7.4,\n          7.5,\n          7.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DMNR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1437053884212822,\n        \"min\": 4.3,\n        \"max\": 9.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          6.7,\n          6.9,\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DILG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9008977926377795,\n        \"min\": 5.1,\n        \"max\": 9.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          7.4,\n          6.2,\n          7.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CFMG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8601102198292417,\n        \"min\": 5.4,\n        \"max\": 8.7,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          6.6,\n          7.3,\n          7.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DECI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8029361953332913,\n        \"min\": 5.7,\n        \"max\": 8.8,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          7.1,\n          8.4,\n          7.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PREP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9533701949831296,\n        \"min\": 4.8,\n        \"max\": 9.1,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          8.4,\n          6.7,\n          7.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9489867534640706,\n        \"min\": 5.1,\n        \"max\": 9.1,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          8.1,\n          7.8,\n          7.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ORAL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0100436927930654,\n        \"min\": 4.7,\n        \"max\": 8.9,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          7.0,\n          6.7,\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WRIT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9611328218400211,\n        \"min\": 4.9,\n        \"max\": 9.0,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          7.8,\n          6.8,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PHYS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9395753146350426,\n        \"min\": 4.7,\n        \"max\": 9.1,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          7.4,\n          6.9,\n          8.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RTEN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1009710804759119,\n        \"min\": 4.8,\n        \"max\": 9.2,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          7.7,\n          6.7,\n          7.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as ss\n",
        "\n",
        "print(ss.skew(df['CONT'])) #양수\n",
        "print(ss.skew(df[\"PHYS\"])) #음수\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fRfjNQO0rb3",
        "outputId": "ea957b42-13cc-48b4-f8bc-c18341498e30"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0859724796276253\n",
            "-1.5582154642293153\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 왜도 양수: sqrt log 1/x\n",
        "# 왜도 음수: sqrt(max(x+1)-x) log(max(x+1)-x) 1/(max(x+1)-x)"
      ],
      "metadata": {
        "id": "Xq50JtIJ0rfe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['CONT1'] = np.log(df[\"CONT\"])\n",
        "df['PHYS1'] = np.log(np.max(df['PHYS']+1) -df['PHYS'])\n",
        "\n",
        "print(ss.skew(df['CONT1']))\n",
        "print(ss.skew(df[\"PHYS1\"]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8xMpTTnz0riY",
        "outputId": "9e66cec1-3d92-4442-856e-35244689a8fe"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6555571886692603\n",
            "0.5824357748750443\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['CONT2'] = np.log10(df[\"CONT\"])\n",
        "df['PHYS2'] = np.log10(np.max(df['PHYS']+1) -df['PHYS'])\n",
        "\n",
        "print(ss.skew(df['CONT2']))\n",
        "print(ss.skew(df[\"PHYS2\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4lB278m8b6k",
        "outputId": "7bb9723b-5e5b-4a45-c25a-203894d74b57"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6555571886692441\n",
            "0.5824357748750438\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#범주화\n",
        "\n",
        "data = [[\"철수\",52], [\"영희\",92], [\"미영\",84], [\"시완\",71], [\"미경\",65], [\"영환\",81], [\"숙경\",66], [\"부영\",77], [\"민섭\",73], [\"보연\",74]]\n",
        "df = pd.DataFrame(data,columns=['이름','수학점수'])\n",
        "\n",
        "print(np.mean(df['수학점수']))\n",
        "\n",
        "plt.hist(df['수학점수'],bins=5, range=[50,100], rwidth=0.9)\n",
        "plt.show()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        },
        "id": "V5JvZ4HP0rld",
        "outputId": "718d64fd-c2aa-45f6-cea0-5cf37b0c89eb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "73.5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          "metadata": {}
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   이름  수학점수\n",
              "0  철수    52\n",
              "1  영희    92\n",
              "2  미영    84\n",
              "3  시완    71\n",
              "4  미경    65\n",
              "5  영환    81\n",
              "6  숙경    66\n",
              "7  부영    77\n",
              "8  민섭    73\n",
              "9  보연    74"
            ],
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              "      <th>이름</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>철수</td>\n",
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              "      <td>영희</td>\n",
              "      <td>92</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>미영</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>시완</td>\n",
              "      <td>71</td>\n",
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              "      <th>4</th>\n",
              "      <td>미경</td>\n",
              "      <td>65</td>\n",
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              "      <td>영환</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>숙경</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>부영</td>\n",
              "      <td>77</td>\n",
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              "    <tr>\n",
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              "      <td>민섭</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>보연</td>\n",
              "      <td>74</td>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-757429c2-aaac-4d4a-80be-9cde5c24c1b0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                    [key], {});\n",
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              "\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",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fc71b96e-a55f-4cae-b671-11e6c94b5c52 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_5a37be9a-3f58-4f19-85f3-dd5bb079fc14\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5a37be9a-3f58-4f19-85f3-dd5bb079fc14 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"\\uc774\\ub984\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"\\ubbfc\\uc12d\",\n          \"\\uc601\\ud76c\",\n          \"\\uc601\\ud658\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc218\\ud559\\uc810\\uc218\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 52,\n        \"max\": 92,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          73,\n          92,\n          81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['등급'] = 0\n",
        "\n",
        "df.loc[(df['수학점수']<60),'등급'] = 'F'\n",
        "df.loc[(df['수학점수']>=60) & (df['수학점수']<=70),'등급'] = 'D'\n",
        "df.loc[(df['수학점수']>=70) & (df['수학점수']<=80),'등급'] = 'C'\n",
        "df.loc[(df['수학점수']>=80)& (df['수학점수']<=90),'등급'] = 'B'\n",
        "df.loc[(df['수학점수']>=90)& (df['수학점수']<=100),'등급'] = \"A\"\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "hZno5DxHCC7V",
        "outputId": "dddc641f-70b1-4087-fa0c-c56d45f7d556"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   이름  수학점수 등급\n",
              "0  철수    52  F\n",
              "1  영희    92  A\n",
              "2  미영    84  B\n",
              "3  시완    71  C\n",
              "4  미경    65  D\n",
              "5  영환    81  B\n",
              "6  숙경    66  D\n",
              "7  부영    77  C\n",
              "8  민섭    73  C\n",
              "9  보연    74  C"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d470ea85-4af0-4427-85d7-91afa7519a18\" 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>이름</th>\n",
              "      <th>수학점수</th>\n",
              "      <th>등급</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>철수</td>\n",
              "      <td>52</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>영희</td>\n",
              "      <td>92</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>미영</td>\n",
              "      <td>84</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>시완</td>\n",
              "      <td>71</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>미경</td>\n",
              "      <td>65</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>영환</td>\n",
              "      <td>81</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>숙경</td>\n",
              "      <td>66</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>부영</td>\n",
              "      <td>77</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>민섭</td>\n",
              "      <td>73</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>보연</td>\n",
              "      <td>74</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d470ea85-4af0-4427-85d7-91afa7519a18')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d470ea85-4af0-4427-85d7-91afa7519a18 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-d470ea85-4af0-4427-85d7-91afa7519a18');\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",
              "\n",
              "\n",
              "<div id=\"df-9cec1eb2-3ac5-402f-8d20-aba062c01788\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9cec1eb2-3ac5-402f-8d20-aba062c01788')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9cec1eb2-3ac5-402f-8d20-aba062c01788 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c883ca6d-6bbe-4162-b358-3b2dc1471c43\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"\\uc774\\ub984\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"\\ubbfc\\uc12d\",\n          \"\\uc601\\ud76c\",\n          \"\\uc601\\ud658\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc218\\ud559\\uc810\\uc218\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 52,\n        \"max\": 92,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          73,\n          92,\n          81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub4f1\\uae09\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"A\",\n          \"D\",\n          \"B\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['등급'] = pd.cut(x=df['수학점수'],\n",
        "                  bins = [0,60,70,80,90,100],\n",
        "                  labels = [\"F\",\"D\",'C','B','A'],\n",
        "                  include_lowest = True)\n",
        "df\n",
        "#cut()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "zoblckFm0roy",
        "outputId": "9267fd52-8e53-4947-94b9-d007f251b35e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   이름  수학점수 등급\n",
              "0  철수    52  F\n",
              "1  영희    92  A\n",
              "2  미영    84  B\n",
              "3  시완    71  C\n",
              "4  미경    65  D\n",
              "5  영환    81  B\n",
              "6  숙경    66  D\n",
              "7  부영    77  C\n",
              "8  민섭    73  C\n",
              "9  보연    74  C"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ba847ec-edc0-42c0-8b98-edab74ccdb36\" class=\"colab-df-container\">\n",
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              "      <th>등급</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>철수</td>\n",
              "      <td>52</td>\n",
              "      <td>F</td>\n",
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              "      <th>1</th>\n",
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              "      <th>3</th>\n",
              "      <td>시완</td>\n",
              "      <td>71</td>\n",
              "      <td>C</td>\n",
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              "      <th>4</th>\n",
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              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>부영</td>\n",
              "      <td>77</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>민섭</td>\n",
              "      <td>73</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>보연</td>\n",
              "      <td>74</td>\n",
              "      <td>C</td>\n",
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              "        const element = document.querySelector('#df-7ba847ec-edc0-42c0-8b98-edab74ccdb36');\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#qcut()\n",
        "df['등급_qcut'] = pd.qcut(x=df['수학점수'],q=5, labels = ['F','D','C','B','A'])\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "zohD4WjQ0rr6",
        "outputId": "52a025fc-d18c-4134-ec13-41c170cc478c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   이름  수학점수 등급 등급_qcut\n",
              "0  철수    52  F       F\n",
              "1  영희    92  A       A\n",
              "2  미영    84  B       A\n",
              "3  시완    71  C       D\n",
              "4  미경    65  D       F\n",
              "5  영환    81  B       B\n",
              "6  숙경    66  D       D\n",
              "7  부영    77  C       B\n",
              "8  민섭    73  C       C\n",
              "9  보연    74  C       C"
            ],
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              "      <td>65</td>\n",
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              "      <td>F</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
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              "      <td>81</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>숙경</td>\n",
              "      <td>66</td>\n",
              "      <td>D</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>부영</td>\n",
              "      <td>77</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>민섭</td>\n",
              "      <td>73</td>\n",
              "      <td>C</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>보연</td>\n",
              "      <td>74</td>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"\\uc774\\ub984\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"\\ubbfc\\uc12d\",\n          \"\\uc601\\ud76c\",\n          \"\\uc601\\ud658\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc218\\ud559\\uc810\\uc218\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 52,\n        \"max\": 92,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          73,\n          92,\n          81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub4f1\\uae09\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"A\",\n          \"D\",\n          \"B\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\ub4f1\\uae09_qcut\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"A\",\n          \"C\",\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#차원 축소 (PCA)\n",
        "#주성분 분석 (여러 변수들의 변량)"
      ],
      "metadata": {
        "id": "Brlk3yUi0ru7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "sTdfpUovYPZC"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris = pd.read_csv('/content/iris.csv')"
      ],
      "metadata": {
        "id": "l3cLCaBzYPbh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = iris.drop(['species'],axis=1)\n",
        "df_species = iris['species']\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6WuLN1BQYPdz",
        "outputId": "ed143241-7a21-47c4-9740-f7f760e2fd7e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width\n",
              "0           5.1          3.5           1.4          0.2\n",
              "1           4.9          3.0           1.4          0.2\n",
              "2           4.7          3.2           1.3          0.2\n",
              "3           4.6          3.1           1.5          0.2\n",
              "4           5.0          3.6           1.4          0.2"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "df['sepal_length'] = StandardScaler().fit_transform(df[['sepal_length']])\n",
        "df['petal_length'] = StandardScaler().fit_transform(df[['petal_length']])\n",
        "df['sepal_width'] = StandardScaler().fit_transform(df[['sepal_width']])\n",
        "df['petal_length'] = StandardScaler().fit_transform(df[['petal_width']])\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components = 4)\n",
        "p_score = pca.fit_transform(df)\n",
        "print(p_score.shape)\n",
        "print(pca.explained_variance_ratio_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDV9DTWvYPgW",
        "outputId": "e926309c-796c-41c1-da80-9541904989ed"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 4)\n",
            "[6.94875022e-01 2.56024652e-01 4.91003259e-02 2.04553654e-32]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = [14,15,13,14,None,None,19,11,12,18]\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Create a Series object from the list x\n",
        "new_column = pd.Series(x)\n",
        "\n",
        "# Assign the Series object to a new column named \"토론\" in df\n",
        "df[\"토론\"] = new_column\n",
        "\n",
        "# Print the information about the DataFrame\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGc5nLEMYPip",
        "outputId": "5e753e68-8236-4ffa-8a06-7ab3825c677e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal_length  150 non-null    float64\n",
            " 1   sepal_width   150 non-null    float64\n",
            " 2   petal_length  150 non-null    float64\n",
            " 3   petal_width   150 non-null    float64\n",
            " 4   토론            8 non-null      float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 6.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df =pd.read_csv('/content/titanic.csv')\n",
        "df.isnull()\n",
        "#결측치"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "fUPb3JuYYPkr",
        "outputId": "43248bb5-d271-4a8d-bf3e-1fbbba55411c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
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              "1          False     False   False  False  False  False  False  False   False   \n",
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              "890        False     False   False  False  False  False  False  False   False   \n",
              "\n",
              "      Fare  Cabin  Embarked  \n",
              "0    False   True     False  \n",
              "1    False  False     False  \n",
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              "\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5edb2b51-3354-4db9-96df-ebaaa0b6fd9e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "        document.querySelector('#df-5edb2b51-3354-4db9-96df-ebaaa0b6fd9e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\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",
              "        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",
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              "\n",
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              "            title=\"Suggest charts\"\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-21b201aa-f5cc-4764-9d73-4bc1ee265666 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"#\\uacb0\\uce21\\uce58\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nH0peOFEYPm_",
        "outputId": "cceb59e8-c7db-4182-f739-94a863a9272d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId      0\n",
            "Survived         0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age            177\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Ticket           0\n",
            "Fare             0\n",
            "Cabin          687\n",
            "Embarked         2\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LZyQSjAYPrX",
        "outputId": "9ed85930-70f1-47fc-fc94-f4dfd2e96dd3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 완전 분석 - 삭제\n",
        "# 평균 대치 - 평균\n",
        "# 단순확률 - 확률분포를 이용한 추정값으로 대치 - 주변값"
      ],
      "metadata": {
        "id": "vDF374uyYPvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_1 = df.dropna(axis=0) # 삭제~~\n",
        "\n",
        "print(df_1.isnull().sum().sum())\n",
        "df_1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KUvqgvDYPx7",
        "outputId": "65be4139-958d-4da6-ac2a-6db9529e8c9a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(183, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Age'].isnull().sum())\n",
        "age_mean = df['Age'].mean()\n",
        "\n",
        "df['Age'].fillna(age_mean, inplace = True)\n",
        "print(df['Age'].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02i7AnCMYP0W",
        "outputId": "95b2ee68-9773-4336-fb73-7b4ec3f9c67a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "177\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import mode\n",
        "\n",
        "print(df['Embarked'].isnull().sum())\n",
        "embarked_mode = df['Embarked'].mode() # 최빈값\n",
        "\n",
        "df[\"Embarked\"].fillna(embarked_mode[0], inplace = True) #대체\n",
        "print(df[\"Embarked\"].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9kpJbd2YP2q",
        "outputId": "19010bcd-c59e-4069-a7fa-0408f35d147b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Embarked'].fillna(method='ffill', inplace = True) # 직전행\n",
        "print(df[\"Embarked\"].isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ho-LzuH-YP5U",
        "outputId": "a24a31bd-c267-4777-f79a-95140d68797a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/titanic.csv')\n",
        "\n",
        "print(df.groupby('Sex')[\"Age\"].mean())\n",
        "print(df.groupby(\"Pclass\")[\"Age\"].mean())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuXrEtcsYP73",
        "outputId": "6a80ea2e-3d9d-4d2f-ec46-b030dd148af8"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex\n",
            "female    27.915709\n",
            "male      30.726645\n",
            "Name: Age, dtype: float64\n",
            "Pclass\n",
            "1    38.233441\n",
            "2    29.877630\n",
            "3    25.140620\n",
            "Name: Age, dtype: float64\n"
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        "df.tail()"
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      "execution_count": 32,
      "outputs": [
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          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "886          887         0       2                     Montvila, Rev. Juozas   \n",
              "887          888         1       1              Graham, Miss. Margaret Edith   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1       1                     Behr, Mr. Karl Howell   \n",
              "890          891         0       3                       Dooley, Mr. Patrick   \n",
              "\n",
              "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
              "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
              "887  female  19.0      0      0      112053  30.00   B42        S  \n",
              "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
              "889    male  26.0      0      0      111369  30.00  C148        C  \n",
              "890    male  32.0      0      0      370376   7.75   NaN        Q  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 887,\n        \"max\": 891,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          888,\n          891,\n          889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Graham, Miss. Margaret Edith\",\n          \"Dooley, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.354126134736337,\n        \"min\": 19.0,\n        \"max\": 32.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          19.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"112053\",\n          \"370376\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.09253436952285,\n        \"min\": 7.75,\n        \"max\": 30.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          30.0,\n          7.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"C148\",\n          \"B42\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Age\"].fillna(df.groupby('Pclass')[\"Age\"].transform('mean'),inplace = True)\n",
        "\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IViMPxJ7YQBV",
        "outputId": "c7b421b2-6359-4e44-cc29-317213b58355"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "886          887         0       2                     Montvila, Rev. Juozas   \n",
              "887          888         1       1              Graham, Miss. Margaret Edith   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1       1                     Behr, Mr. Karl Howell   \n",
              "890          891         0       3                       Dooley, Mr. Patrick   \n",
              "\n",
              "        Sex       Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
              "886    male  27.00000      0      0      211536  13.00   NaN        S  \n",
              "887  female  19.00000      0      0      112053  30.00   B42        S  \n",
              "888  female  25.14062      1      2  W./C. 6607  23.45   NaN        S  \n",
              "889    male  26.00000      0      0      111369  30.00  C148        C  \n",
              "890    male  32.00000      0      0      370376   7.75   NaN        Q  "
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.00</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>25.14062</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc2d3795-62fd-419f-909c-c0090438559d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-bc2d3795-62fd-419f-909c-c0090438559d 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-bc2d3795-62fd-419f-909c-c0090438559d');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-66fd558b-5504-40c6-8d41-0191cd5eaf66 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 887,\n        \"max\": 891,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          888,\n          891,\n          889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Graham, Miss. Margaret Edith\",\n          \"Dooley, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.652709629206573,\n        \"min\": 19.0,\n        \"max\": 32.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          19.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"112053\",\n          \"370376\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.09253436952285,\n        \"min\": 7.75,\n        \"max\": 30.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          30.0,\n          7.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"C148\",\n          \"B42\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 정규 분포 평균 50 표준편차 10 데이터 200개\n",
        "data = 10 * np.random.randn(200) + 50\n",
        "df = pd.DataFrame({'값':data}) # 데이터 프레임 변환\n",
        "\n",
        "# 이상치 삽입\n",
        "df.loc[201] = 2\n",
        "df.loc[202] = 100\n",
        "df.loc[203] = 10\n",
        "df.loc[204] = 110\n",
        "\n",
        "#시각화\n",
        "plt.hist(df['값'],bins=20,rwidth=0.8)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "lf8FMS4B0ryG",
        "outputId": "f6c1c08f-801f-4b72-923b-a7a6e5353812"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# IQR 방법\n",
        "plt.boxplot(df['값'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "rIjJnjxSQYuj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "c92f33fd-080c-4600-ec22-37a7eaaa5897"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df['값'].quantile(.25)\n",
        "Q2 = df['값'].quantile(.5)\n",
        "Q3 = df['값'].quantile(.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "print(\"Q1=\", Q1)\n",
        "print('Q2=', Q2)\n",
        "print(\"Q3=\",Q3)\n",
        "print(\"IQR = \", IQR)\n"
      ],
      "metadata": {
        "id": "yulzH_S-QYw_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a96bb190-a029-40ec-e3dc-bdb5a332c247"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q1= 43.07782093053916\n",
            "Q2= 48.97642197561946\n",
            "Q3= 57.74876669487682\n",
            "IQR =  14.67094576433766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df['값'].describe()['25%']\n",
        "Q2 = df['값'].describe()['50%']\n",
        "Q3 = df[\"값\"].describe()['75%']\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "print(\"Q1 = \",Q1)\n",
        "print(\"Q2= \",Q2)\n",
        "print(\"Q3= \", Q3)\n",
        "print(\"IQR=\",IQR)"
      ],
      "metadata": {
        "id": "n-v-NLFaQYzy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e3b3eeb-4294-4ea0-b973-1c56f80ca362"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q1 =  43.07782093053916\n",
            "Q2=  48.97642197561946\n",
            "Q3=  57.74876669487682\n",
            "IQR= 14.67094576433766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#이상치 검출\n",
        "condition = df['값'] > (Q3 + IQR * 1.5)\n",
        "upperOutlier = df[condition]\n",
        "\n",
        "condition = df['값'] < (Q1 - IQR * 1.5)\n",
        "lowerOutlier = df[condition]\n",
        "\n",
        "print(lowerOutlier)\n",
        "print(upperOutlier)"
      ],
      "metadata": {
        "id": "CPgr6xDAQY3X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11897294-f6c8-4f81-8cd6-faee94eacef1"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        값\n",
            "201   2.0\n",
            "203  10.0\n",
            "         값\n",
            "202  100.0\n",
            "204  110.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 평활화\n",
        "df = pd.read_csv('/content/lynx.csv')\n",
        "\n",
        "print(df.head(10))\n",
        "print(df.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4FcDl_7jW3g",
        "outputId": "550bbf85-1df0-493e-f17b-b54b1dd56226"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Unnamed: 0  time  value\n",
            "0           1  1821    269\n",
            "1           2  1822    321\n",
            "2           3  1823    585\n",
            "3           4  1824    871\n",
            "4           5  1825   1475\n",
            "5           6  1826   2821\n",
            "6           7  1827   3928\n",
            "7           8  1828   5943\n",
            "8           9  1829   4950\n",
            "9          10  1830   2577\n",
            "       Unnamed: 0         time        value\n",
            "count  114.000000   114.000000   114.000000\n",
            "mean    57.500000  1877.500000  1538.017544\n",
            "std     33.052988    33.052988  1585.843914\n",
            "min      1.000000  1821.000000    39.000000\n",
            "25%     29.250000  1849.250000   348.250000\n",
            "50%     57.500000  1877.500000   771.000000\n",
            "75%     85.750000  1905.750000  2566.750000\n",
            "max    114.000000  1934.000000  6991.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sma'] = df['value'].rolling(10).mean()\n",
        "# 10년 단순 이동 평균\n",
        "plt.plot(df['value'])\n",
        "plt.plot(df['sma'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "QSAJ_0hMjW57",
        "outputId": "658291cf-2e9e-4304-cd4a-48708a680681"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ewm'] = df['value'].ewm(10).mean()\n",
        "# 10년 지수가중이동평균\n",
        "plt.plot(df['value'])\n",
        "plt.plot(df['ewm'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "7xt5p1TsjW8Z",
        "outputId": "1b2f8a2f-f048-4adf-bd9e-68510acb7840"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FY6JQFMmjW_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rp-IOuiNjXBV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BuP0Ko4gjXDe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KJxAkm9KjXGI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g4r1xSoKjXIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PSUxsp4ujXLH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5zrr0PCTjXNW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fuXklDeJjXPz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_XlbDzLEjXSI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zyGtWmrWjXUQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nZ4umsQLjXWn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mlTgt--jjXfc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KX11E5_hjXmr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ht1Rzf0MjXpt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4Y4w8ZbFQY6S"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}