04_5_데이터_전처리.ipynb

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  "metadata": {
    "colab": {
      "name": "04_5_데이터_전처리.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/heptacode/SunrinBigDatas/blob/master/04_5_%EB%8D%B0%EC%9D%B4%ED%84%B0_%EC%A0%84%EC%B2%98%EB%A6%AC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7iiDN_dZ4mV",
        "colab_type": "text"
      },
      "source": [
        "### Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KyWJF7DKIzLM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#24\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NogtlgpGYcQM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "items = ['TV','냉장고','전자렌지','컴퓨터','선풍기','선풍기','믹서','믹서']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FnLn_G-7YrTL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a2f080f3-8b9c-4905-8726-dcf88877d2aa"
      },
      "source": [
        "# LabelEncoder를 객체로 생성\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "# fit( ) 과 transform( ) 으로 label 인코딩 수행. \n",
        "encoder.fit(items)\n",
        "labels = encoder.transform(items)\n",
        "\n",
        "print('인코딩 변환값:',labels)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "인코딩 변환값: [0 1 4 5 3 3 2 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXhHpDtzYtO_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a8601656-f184-4fc8-d57e-ac8cd05a133a"
      },
      "source": [
        "#25\n",
        "print('인코딩 클래스:',encoder.classes_)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "인코딩 클래스: ['TV' '냉장고' '믹서' '선풍기' '전자렌지' '컴퓨터']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIktJLl1Y7Cj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "31427ef2-c4fe-4f67-9313-889b0f22d02e"
      },
      "source": [
        "#26\n",
        "print('디코딩 원본 값:',encoder.inverse_transform([4, 5, 2, 0]))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "디코딩 원본 값: ['전자렌지' '컴퓨터' '믹서' 'TV']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tubkdL7vZ9gG",
        "colab_type": "text"
      },
      "source": [
        "### One-Hot Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BayOMC3Y-VU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#27\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "import numpy as np\n",
        "\n",
        "items = ['TV','냉장고','전자렌지','컴퓨터','선풍기','선풍기','믹서','믹서']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTZQhOPBaEbl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 먼저 숫자값으로 변환을 위해 LabelEncoder로 변환합니다. \n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(items)\n",
        "labels = encoder.transform(items)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mef0dI3acbc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 2차원 데이터로 변환합니다. \n",
        "labels = labels.reshape(-1, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGIvBVuOaebM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 원-핫 인코딩을 적용합니다. \n",
        "oh_encoder = OneHotEncoder()\n",
        "oh_encoder.fit(labels)\n",
        "oh_labels = oh_encoder.transform(labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQGQRTeyag08",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "0ad5c960-162e-4165-a302-fc1a7b91480d"
      },
      "source": [
        "print('원-핫 인코딩 데이터')\n",
        "print(oh_labels.toarray())\n",
        "print('원-핫 인코딩 데이터 차원')\n",
        "print(oh_labels.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "원-핫 인코딩 데이터\n",
            "[[1. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0.]]\n",
            "원-핫 인코딩 데이터 차원\n",
            "(8, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YEhpXj9bKP3",
        "colab_type": "text"
      },
      "source": [
        "### get_dummies()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoTsfOnDai-S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "1d23cf19-ae9d-4d4d-8e55-03cd8479709e"
      },
      "source": [
        "#28\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame({'item':['TV','냉장고','전자렌지','컴퓨터', '선풍기','선풍기','믹서','믹서'] })\n",
        "pd.get_dummies(df)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   item_TV  item_냉장고  item_믹서  item_선풍기  item_전자렌지  item_컴퓨터\n",
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              "7        0         0        1         0          0         0"
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          "metadata": {
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          "execution_count": 11
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      ]
    }
  ]
}