data_generating.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "범죄 지수 계산<br>\n",
        "0~4<br>\n",
        "ex) 2021년 서울 종로 아파트 일요일 0시 일 때 절도 일어날 위험 지수<br>\n",
        "1+ 0.04 + 0.34 + 0 + 0 = 1.38 <br>\n",
        "<br>\n",
        "* 2014 ~ 2021 (8가지)\n",
        "* 장소 (7가지)\n",
        "* 요일 (7가지)\n",
        "* 시간대 (8가지)\n",
        "* 지역 (128가지)\n",
        "* 인구수 (1가지) -> 정규화"
      ],
      "metadata": {
        "id": "9OtYeGqAqxHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6w3jw16uriH",
        "outputId": "293ccadf-e187-49ea-fc54-f33cb3b89b5f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "56u81Ic2ux4n"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', None) # 전체 열 보기\n",
        "pd.set_option('display.max_rows', None) # 전체 행 보기"
      ],
      "metadata": {
        "id": "IPLZbn09uufb"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "year = 2014"
      ],
      "metadata": {
        "id": "n3kYqaZ4sHGk"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 데이터 생성"
      ],
      "metadata": {
        "id": "mycyFTo3sCqg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(year)+'-시간대별.csv', index_col = 0)\n",
        "df2 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(year)+'-장소별.csv', index_col = 0)\n",
        "df3 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(year)+'-요일별.csv', index_col = 0)\n",
        "df4 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(year)+'-지역별.csv', index_col = 0)\n",
        "df5 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(year)+'-인구수.csv', index_col = 0)"
      ],
      "metadata": {
        "id": "FnH1xuYlu2pC"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_time = df1.columns # 라벨 인코딩\n",
        "_place = df2.columns # 라벨 인코딩\n",
        "_day = df3.columns # 라벨 인코딩\n",
        "_region = df4.columns # 라벨 인코딩"
      ],
      "metadata": {
        "id": "oVy6RejOvHOw"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(_region)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ln3J45KyTZg",
        "outputId": "bd8f1257-cb10-409c-cfa8-a80d3e431946"
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      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "128"
            ]
          },
          "metadata": {},
          "execution_count": 118
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      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "VRphmoZrqIzn"
      },
      "outputs": [],
      "source": [
        "column = [\n",
        "    '위치','장소','요일','시간대','인구수','절도','살인','강도','성폭력','폭행',\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = []\n",
        "for r in _region:\n",
        "  for p in _place:\n",
        "    for d in _day:\n",
        "      for t in _time:\n",
        "        \"\"\"\n",
        "        df1[t].values[]\n",
        "        df2[p].values[]\n",
        "        df3[d].values[]\n",
        "        df4[r].values[]\n",
        "        df5[r].values[0]\n",
        "        \"\"\"\n",
        "\n",
        "        juldo = df1[t].values[0] + df2[p].values[0] + df3[d].values[0] + df4[r].values[0]\n",
        "        salin = df1[t].values[1] + df2[p].values[1] + df3[d].values[1] + df4[r].values[1]\n",
        "        gangdo = df1[t].values[2] + df2[p].values[2] + df3[d].values[2] + df4[r].values[2]\n",
        "        sungpok = df1[t].values[3] + df2[p].values[3] + df3[d].values[3] + df4[r].values[3]\n",
        "        pokang = df1[t].values[4] + df2[p].values[4] + df3[d].values[4] + df4[r].values[4]\n",
        "\n",
        "        data.append([r,p,d,t,df5[r].values[0],juldo,salin,gangdo,sungpok,pokang])"
      ],
      "metadata": {
        "id": "UMsyEH47X_ZB"
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      "execution_count": 114,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data, columns=column)"
      ],
      "metadata": {
        "id": "kIvDfd_Wxlo7"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[20000:20010]"
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        "outputId": "84277cf5-6847-44c6-db9a-fce59fb7a038"
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      "execution_count": 116,
      "outputs": [
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              "          위치          장소 요일          시간대     인구수    절도    살인    강도   성폭력    폭행\n",
              "20000  대구달성군  아파트ㆍ연립ㆍ다세대  월  00:00-02:59  184902  0.52  1.74  2.16  0.69  0.70\n",
              "20001  대구달성군  아파트ㆍ연립ㆍ다세대  월  03:00-05:59  184902  0.51  1.78  2.39  0.76  0.54\n",
              "20002  대구달성군  아파트ㆍ연립ㆍ다세대  월  06:00-08:59  184902  0.36  1.55  1.39  0.59  0.23\n",
              "20003  대구달성군  아파트ㆍ연립ㆍ다세대  월  09:00-11:59  184902  0.86  1.87  1.42  0.55  0.28\n",
              "20004  대구달성군  아파트ㆍ연립ㆍ다세대  월  12:00-14:59  184902  0.94  1.84  1.55  0.61  0.29\n",
              "20005  대구달성군  아파트ㆍ연립ㆍ다세대  월  15:00-17:59  184902  1.11  1.95  1.53  0.94  0.39\n",
              "20006  대구달성군  아파트ㆍ연립ㆍ다세대  월  18:00-20:59  184902  1.36  2.33  1.66  1.17  0.64\n",
              "20007  대구달성군  아파트ㆍ연립ㆍ다세대  월  21:00-23:59  184902  1.12  2.55  1.95  1.55  1.23\n",
              "20008  대구달성군  아파트ㆍ연립ㆍ다세대  화  00:00-02:59  184902  0.70  2.26  1.16  1.58  0.94\n",
              "20009  대구달성군  아파트ㆍ연립ㆍ다세대  화  03:00-05:59  184902  0.69  2.30  1.39  1.65  0.78"
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              "      <th>20005</th>\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6b5eba9-ad6c-4c43-9093-f7c21163b12a 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-f6b5eba9-ad6c-4c43-9093-f7c21163b12a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('/content/drive/MyDrive/산업인공지능 대회/merged_data/'+str(year)+'.csv', index = False,encoding='utf-8')"
      ],
      "metadata": {
        "id": "y4DkxRmewp9T"
      },
      "execution_count": 117,
      "outputs": []
    }
  ]
}