data_preprocessing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "K-bm8MazM7pP"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODxDwARGhtyD",
        "outputId": "55026c9c-92db-4ef0-d553-4a32f7e9c45d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "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": "Ri1tJlw52ha6"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "N9jQ3H9hh8DE"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "year = str(2014)"
      ],
      "metadata": {
        "id": "b-jSzNuNG0ik"
      },
      "execution_count": 417,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crime = ['절도', '살인', '강도', '성폭력', '폭행']"
      ],
      "metadata": {
        "id": "-5pMsoF7B145"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 범죄 위험 지수 계산하기 <br>\n",
        "![스크린샷 2023-08-04 오후 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)"
      ],
      "metadata": {
        "id": "I1U0Rr7CCGcA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 시간대별 데이터 병합 (한 해)"
      ],
      "metadata": {
        "id": "XIpqxDUnG3KA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_excel('/content/drive/MyDrive/산업인공지능 대회/data/'+year+'-시간대별.xlsx', index_col = 0)\n",
        "df1.columns = df1.loc['범죄별(1)'].values\n",
        "df1 = df1.drop(['범죄별(1)'])\n",
        "df1 = df1.drop(['계','미상'],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URUHqvbqiBL9",
        "outputId": "9a1aa023-9450-4e9e-c23d-8e58a06fa2ef"
      },
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "njw3ZMXRBo8R",
        "outputId": "88fbd5c0-747a-46a0-c9ae-bba2a9f9e307"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       00:00-02:59 03:00-05:59 06:00-08:59 09:00-11:59 12:00-14:59  \\\n",
              "범죄별(1)                                                               \n",
              "절도           21567       21195       17918       29081       30903   \n",
              "살인              82          86          65          94          91   \n",
              "강도             249         297          86          92         120   \n",
              "성폭력           2511        2651        2275        2197        2330   \n",
              "폭행           20789       16233        7791        9147        9464   \n",
              "\n",
              "       15:00-17:59 18:00-20:59 21:00-23:59  \n",
              "범죄별(1)                                      \n",
              "절도           34679       40322       35015  \n",
              "살인             101         135         155  \n",
              "강도             115         142         204  \n",
              "성폭력           3059        3576        4405  \n",
              "폭행           12069       18917       35198  "
            ],
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              "      <th></th>\n",
              "      <th>00:00-02:59</th>\n",
              "      <th>03:00-05:59</th>\n",
              "      <th>06:00-08:59</th>\n",
              "      <th>09:00-11:59</th>\n",
              "      <th>12:00-14:59</th>\n",
              "      <th>15:00-17:59</th>\n",
              "      <th>18:00-20:59</th>\n",
              "      <th>21:00-23:59</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>범죄별(1)</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>절도</th>\n",
              "      <td>21567</td>\n",
              "      <td>21195</td>\n",
              "      <td>17918</td>\n",
              "      <td>29081</td>\n",
              "      <td>30903</td>\n",
              "      <td>34679</td>\n",
              "      <td>40322</td>\n",
              "      <td>35015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>살인</th>\n",
              "      <td>82</td>\n",
              "      <td>86</td>\n",
              "      <td>65</td>\n",
              "      <td>94</td>\n",
              "      <td>91</td>\n",
              "      <td>101</td>\n",
              "      <td>135</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>강도</th>\n",
              "      <td>249</td>\n",
              "      <td>297</td>\n",
              "      <td>86</td>\n",
              "      <td>92</td>\n",
              "      <td>120</td>\n",
              "      <td>115</td>\n",
              "      <td>142</td>\n",
              "      <td>204</td>\n",
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              "    <tr>\n",
              "      <th>성폭력</th>\n",
              "      <td>2511</td>\n",
              "      <td>2651</td>\n",
              "      <td>2275</td>\n",
              "      <td>2197</td>\n",
              "      <td>2330</td>\n",
              "      <td>3059</td>\n",
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              "      <td>4405</td>\n",
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              "    <tr>\n",
              "      <th>폭행</th>\n",
              "      <td>20789</td>\n",
              "      <td>16233</td>\n",
              "      <td>7791</td>\n",
              "      <td>9147</td>\n",
              "      <td>9464</td>\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\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",
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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",
              "    [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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a4531ff-8118-49c0-84bb-a7aa277605a6 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-0a4531ff-8118-49c0-84bb-a7aa277605a6');\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": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_max = df1.loc[[crime[0]]].values.max()\n",
        "_min = df1.loc[[crime[0]]].values.min()\n",
        "my_value = df1.loc[[crime[0]]]\n",
        "new_df1 = (my_value-_min)/(_max-_min)\n",
        "\n",
        "for i in range(1,len(crime)):\n",
        "  _max = df1.loc[[crime[i]]].values.max()\n",
        "  _min = df1.loc[[crime[i]]].values.min()\n",
        "  my_value = df1.loc[[crime[i]]]\n",
        "  new_df1 = pd.concat([new_df1,(my_value-_min)/(_max-_min)])\n",
        "\n",
        "new_df1 = new_df1.astype(float).round(2)"
      ],
      "metadata": {
        "id": "-1pXaxN-DfGO"
      },
      "execution_count": 245,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df1.to_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+year+'-시간대별.csv',encoding='utf-8')"
      ],
      "metadata": {
        "id": "jmHWDH8iHHO-"
      },
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 시간대별 데이터 병합 (2014~2021)"
      ],
      "metadata": {
        "id": "K-bm8MazM7pP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for _year in range(2014,2022):\n",
        "  df1 = pd.read_excel('/content/drive/MyDrive/산업인공지능 대회/data/'+str(_year)+'-시간대별.xlsx', index_col = 0)\n",
        "  df1.columns = df1.loc['범죄별(1)'].values\n",
        "  df1 = df1.drop(['범죄별(1)'])\n",
        "  df1 = df1.drop(['계','미상'],axis=1)\n",
        "\n",
        "  _max = df1.loc[[crime[0]]].values.max()\n",
        "  _min = df1.loc[[crime[0]]].values.min()\n",
        "  my_value = df1.loc[[crime[0]]]\n",
        "  new_df1 = (my_value-_min)/(_max-_min)\n",
        "\n",
        "  for i in range(1,len(crime)):\n",
        "    _max = df1.loc[[crime[i]]].values.max()\n",
        "    _min = df1.loc[[crime[i]]].values.min()\n",
        "    my_value = df1.loc[[crime[i]]]\n",
        "    new_df1 = pd.concat([new_df1,(my_value-_min)/(_max-_min)])\n",
        "\n",
        "  new_df1 = new_df1.astype(float).round(2)\n",
        "  new_df1.to_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(_year)+'-시간대별.csv',encoding='utf-8')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNUjpN7tNBGu",
        "outputId": "9e82909d-4ff2-49b3-cc36-087f396e5fa1"
      },
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 요일별 데이터 병합 (2014~2021)"
      ],
      "metadata": {
        "id": "8b7GbFKWG6Tg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.read_excel('/content/drive/MyDrive/산업인공지능 대회/data/'+str(2021)+'-요일별.xlsx', index_col = 0)\n",
        "df2.columns = df2.loc['범죄별(1)'].values\n",
        "df2 = df2.drop(['범죄별(1)'])\n",
        "df2 = df2.drop(['합계'],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJi0pJ0TOkZ9",
        "outputId": "0ffac200-fb18-4546-83e1-64e96b0154de"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "VaXhd6dXOtes",
        "outputId": "343b8f96-c3d3-4d3d-e081-4bec95b3e7db"
      },
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            일      월      화      수      목      금      토\n",
              "범죄별(1)                                                 \n",
              "절도      21737  23478  23516  23794  24036  25275  24946\n",
              "살인         95    108     97     92     88    118     94\n",
              "강도         89     54     72     72     69     88     67\n",
              "성폭력      4509   4928   4424   4694   4663   4769   4911\n",
              "폭행      18103  15848  16147  16177  15898  17416  19926"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-aaade04b-a0ff-41ea-a35c-147c52f0b259\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>일</th>\n",
              "      <th>월</th>\n",
              "      <th>화</th>\n",
              "      <th>수</th>\n",
              "      <th>목</th>\n",
              "      <th>금</th>\n",
              "      <th>토</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>범죄별(1)</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>절도</th>\n",
              "      <td>21737</td>\n",
              "      <td>23478</td>\n",
              "      <td>23516</td>\n",
              "      <td>23794</td>\n",
              "      <td>24036</td>\n",
              "      <td>25275</td>\n",
              "      <td>24946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>살인</th>\n",
              "      <td>95</td>\n",
              "      <td>108</td>\n",
              "      <td>97</td>\n",
              "      <td>92</td>\n",
              "      <td>88</td>\n",
              "      <td>118</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>강도</th>\n",
              "      <td>89</td>\n",
              "      <td>54</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>69</td>\n",
              "      <td>88</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>성폭력</th>\n",
              "      <td>4509</td>\n",
              "      <td>4928</td>\n",
              "      <td>4424</td>\n",
              "      <td>4694</td>\n",
              "      <td>4663</td>\n",
              "      <td>4769</td>\n",
              "      <td>4911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>폭행</th>\n",
              "      <td>18103</td>\n",
              "      <td>15848</td>\n",
              "      <td>16147</td>\n",
              "      <td>16177</td>\n",
              "      <td>15898</td>\n",
              "      <td>17416</td>\n",
              "      <td>19926</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aaade04b-a0ff-41ea-a35c-147c52f0b259')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-98cc417b-5f72-4e60-8611-5e196f614e65\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98cc417b-5f72-4e60-8611-5e196f614e65')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-98cc417b-5f72-4e60-8611-5e196f614e65 button.colab-df-quickchart');\n",
              "  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-aaade04b-a0ff-41ea-a35c-147c52f0b259 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-aaade04b-a0ff-41ea-a35c-147c52f0b259');\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": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for _year in range(2014,2022):\n",
        "  df2 = pd.read_excel('/content/drive/MyDrive/산업인공지능 대회/data/'+str(_year)+'-요일별.xlsx', index_col = 0)\n",
        "  df2.columns = df2.loc['범죄별(1)'].values\n",
        "  df2 = df2.drop(['범죄별(1)'])\n",
        "  df2 = df2.drop(['합계'],axis=1)\n",
        "  if _year == 2014:\n",
        "    df2 = df2.drop(['미상'],axis=1)\n",
        "\n",
        "  _max = df2.loc[[crime[0]]].values.max()\n",
        "  _min = df2.loc[[crime[0]]].values.min()\n",
        "  my_value = df2.loc[[crime[0]]]\n",
        "  new_df2 = (my_value-_min)/(_max-_min)\n",
        "\n",
        "  for i in range(1,len(crime)):\n",
        "    _max = df2.loc[[crime[i]]].values.max()\n",
        "    _min = df2.loc[[crime[i]]].values.min()\n",
        "    my_value = df2.loc[[crime[i]]]\n",
        "    new_df2 = pd.concat([new_df2,(my_value-_min)/(_max-_min)])\n",
        "\n",
        "  new_df2 = new_df2.astype(float).round(2)\n",
        "  new_df2.to_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(_year)+'-요일별.csv',encoding='utf-8')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgHkcg8FN7Eo",
        "outputId": "675909bc-bb86-46bf-da7f-9951dbfe1e9f"
      },
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
            "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 지역별 데이터 병합"
      ],
      "metadata": {
        "id": "zpzJuTFhONdo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/data/'+year+'-지역별.csv',  encoding='utf-8')\n",
        "df3 = df3.drop(['범죄별(1)','범죄별(2)','2014'],axis=1)\n",
        "df3 = df3.drop([0,1,2]).set_index('범죄별(3)')"
      ],
      "metadata": {
        "id": "GG30NMCa4IDy"
      },
      "execution_count": 480,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = df3.replace('-', '0')"
      ],
      "metadata": {
        "id": "D_eIEObInpAv"
      },
      "execution_count": 481,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "c6ekonoAoZwy",
        "outputId": "27923996-8ee0-4a1b-8e0c-ef318af66233"
      },
      "execution_count": 482,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9  \\\n",
              "범죄별(3)                                                                     \n",
              "절도         59,447      2,258      2,565      1,603      1,755      3,169   \n",
              "살인            157          2          6          1          1          8   \n",
              "강도            345         11         14          7          5         16   \n",
              "성폭력         8,594        307        377        269        187        328   \n",
              "폭행         39,108      1,482      1,474      1,221      1,020      1,645   \n",
              "\n",
              "       Unnamed: 10 Unnamed: 11 Unnamed: 12 Unnamed: 13 Unnamed: 14  \\\n",
              "범죄별(3)                                                               \n",
              "절도           1,816       2,382       2,136       1,505       1,349   \n",
              "살인              12          11           8           6           6   \n",
              "강도              13          14          16          21          11   \n",
              "성폭력            210         205         220         220         208   \n",
              "폭행           1,503       1,613       1,399       1,509       1,027   \n",
              "\n",
              "       Unnamed: 15 Unnamed: 16 Unnamed: 17 Unnamed: 18 Unnamed: 19  \\\n",
              "범죄별(3)                                                               \n",
              "절도           2,404       2,354       1,871       2,787       2,058   \n",
              "살인               5           5           3           4           5   \n",
              "강도               8          24           4          22          12   \n",
              "성폭력            294         222         867         423         241   \n",
              "폭행           1,787       1,607       1,199       1,368       1,580   \n",
              "\n",
              "       Unnamed: 20 Unnamed: 21 Unnamed: 22 Unnamed: 23 Unnamed: 24  \\\n",
              "범죄별(3)                                                               \n",
              "절도           2,318       2,370       1,385       3,108       1,790   \n",
              "살인               8           4           8          13           2   \n",
              "강도               9          12           9          11          10   \n",
              "성폭력            308         426         250         352         437   \n",
              "폭행           1,755       1,715       1,227       2,016       1,134   \n",
              "\n",
              "       Unnamed: 25 Unnamed: 26 Unnamed: 27 Unnamed: 28 Unnamed: 29  \\\n",
              "범죄별(3)                                                               \n",
              "절도           3,192       2,766       3,886       4,190       2,430   \n",
              "살인               7           6          12           9           5   \n",
              "강도              25          15          36          10          10   \n",
              "성폭력            471         448         761         383         180   \n",
              "폭행           2,273       1,519       2,444       2,007       1,584   \n",
              "\n",
              "       Unnamed: 30 Unnamed: 31 Unnamed: 32 Unnamed: 33 Unnamed: 34  \\\n",
              "범죄별(3)                                                               \n",
              "절도          21,946         918         835         780         529   \n",
              "살인              71           4           5           4           3   \n",
              "강도             132           7           6           3           5   \n",
              "성폭력          2,348          91          61          71          65   \n",
              "폭행           8,577         363         250         367         294   \n",
              "\n",
              "       Unnamed: 35 Unnamed: 36 Unnamed: 37 Unnamed: 38 Unnamed: 39  \\\n",
              "범죄별(3)                                                               \n",
              "절도           3,337       1,647       1,728       1,259       2,288   \n",
              "살인               7           2           3           6           9   \n",
              "강도              21           7           6           6          18   \n",
              "성폭력            326         169         334         202         239   \n",
              "폭행           1,352         630         488         619         875   \n",
              "\n",
              "       Unnamed: 40 Unnamed: 41 Unnamed: 42 Unnamed: 43 Unnamed: 44  \\\n",
              "범죄별(3)                                                               \n",
              "절도           1,714       1,445         502       1,031       1,369   \n",
              "살인               6           4           1           2           1   \n",
              "강도              18           3           1           1           4   \n",
              "성폭력            176         144          28         143         117   \n",
              "폭행             778         558         119         558         508   \n",
              "\n",
              "       Unnamed: 45 Unnamed: 46 Unnamed: 47 Unnamed: 48 Unnamed: 49  \\\n",
              "범죄별(3)                                                               \n",
              "절도           1,968         596      14,627       1,835       2,380   \n",
              "살인              12           2          36           6           6   \n",
              "강도              20           6          68           7          21   \n",
              "성폭력            134          48       1,587         278         216   \n",
              "폭행             624         194       5,809         437         729   \n",
              "\n",
              "       Unnamed: 50 Unnamed: 51 Unnamed: 52 Unnamed: 53 Unnamed: 54  \\\n",
              "범죄별(3)                                                               \n",
              "절도           1,296       1,073       2,131       2,085       3,144   \n",
              "살인               7           0           4           5           6   \n",
              "강도              11           4           9           5           9   \n",
              "성폭력            162         121         248         188         312   \n",
              "폭행             882         520         735         684       1,506   \n",
              "\n",
              "       Unnamed: 55 Unnamed: 56 Unnamed: 57 Unnamed: 58 Unnamed: 59  \\\n",
              "범죄별(3)                                                               \n",
              "절도             683       9,751         506         252       1,291   \n",
              "살인               2          51           6           0           5   \n",
              "강도               2         112           7           2          16   \n",
              "성폭력             62       1,681          98          25         220   \n",
              "폭행             316       9,291         527         291       1,292   \n",
              "\n",
              "       Unnamed: 60 Unnamed: 61 Unnamed: 62 Unnamed: 63 Unnamed: 64  \\\n",
              "범죄별(3)                                                               \n",
              "절도           1,829         843       1,858       2,398         645   \n",
              "살인              11           5          16           4           3   \n",
              "강도              26           4          17          28          12   \n",
              "성폭력            297         204         244         335         226   \n",
              "폭행           1,638         783       1,780       1,859         930   \n",
              "\n",
              "       Unnamed: 65 Unnamed: 66 Unnamed: 67 Unnamed: 68 Unnamed: 69  \\\n",
              "범죄별(3)                                                               \n",
              "절도             104          25      10,368       1,254       2,623   \n",
              "살인               1           0          19           2           7   \n",
              "강도               0           0          53          15           9   \n",
              "성폭력             25           7       1,050         150         224   \n",
              "폭행             145          46       6,014         802       1,629   \n",
              "\n",
              "       Unnamed: 70 Unnamed: 71 Unnamed: 72 Unnamed: 73 Unnamed: 74  \\\n",
              "범죄별(3)                                                               \n",
              "절도           1,120       2,972       2,399      11,464       2,209   \n",
              "살인               1           6           3          30           2   \n",
              "강도               3          16          10          82          30   \n",
              "성폭력             91         315         270         805         170   \n",
              "폭행             612       1,704       1,267       2,710         796   \n",
              "\n",
              "       Unnamed: 75 Unnamed: 76 Unnamed: 77 Unnamed: 78 Unnamed: 79  \\\n",
              "범죄별(3)                                                               \n",
              "절도           2,248       3,458       2,152       1,397       5,782   \n",
              "살인              10           7           4           7          23   \n",
              "강도              11          21          10          10          32   \n",
              "성폭력            144         247         107         137         534   \n",
              "폭행             435         859         340         280       3,998   \n",
              "\n",
              "       Unnamed: 80 Unnamed: 81 Unnamed: 82 Unnamed: 83 Unnamed: 84  \\\n",
              "범죄별(3)                                                               \n",
              "절도           1,378         920       1,923         658         903   \n",
              "살인               5           2           8           3           5   \n",
              "강도               6           2          18           3           3   \n",
              "성폭력            109          77         196          68          84   \n",
              "폭행             824         485       1,566         352         771   \n",
              "\n",
              "       Unnamed: 85 Unnamed: 86 Unnamed: 87 Unnamed: 88 Unnamed: 89  \\\n",
              "범죄별(3)                                                               \n",
              "절도           5,686       6,785       4,558       3,799       2,180   \n",
              "살인              15          16          12           9           7   \n",
              "강도              27          32          28          30          12   \n",
              "성폭력            530         722         579         328         467   \n",
              "폭행           3,101       4,172       2,915       1,832       1,778   \n",
              "\n",
              "       Unnamed: 90 Unnamed: 91 Unnamed: 92 Unnamed: 93 Unnamed: 94  \\\n",
              "범죄별(3)                                                               \n",
              "절도           4,816       5,525       1,666       2,532       4,228   \n",
              "살인              16          21           3          11          12   \n",
              "강도              32          37          12           5          19   \n",
              "성폭력            372         354         163         175         384   \n",
              "폭행           2,285       2,175       1,241         938       2,586   \n",
              "\n",
              "       Unnamed: 95 Unnamed: 96 Unnamed: 97 Unnamed: 98 Unnamed: 99  \\\n",
              "범죄별(3)                                                               \n",
              "절도           2,266       3,690       4,983       1,639       2,602   \n",
              "살인              12          11          18           4           9   \n",
              "강도               7          17          25          12          13   \n",
              "성폭력            188         396         370         154         228   \n",
              "폭행             695       1,971       2,370         893       2,129   \n",
              "\n",
              "       Unnamed: 100 Unnamed: 101 Unnamed: 102 Unnamed: 103 Unnamed: 104  \\\n",
              "범죄별(3)                                                                    \n",
              "절도            1,751        1,311        2,310        4,416        1,162   \n",
              "살인                5            5            5           17            4   \n",
              "강도               13            8           20           33            9   \n",
              "성폭력             223          133          183          369          107   \n",
              "폭행              818          956          707        1,563          909   \n",
              "\n",
              "       Unnamed: 105 Unnamed: 106 Unnamed: 107 Unnamed: 108 Unnamed: 109  \\\n",
              "범죄별(3)                                                                    \n",
              "절도            1,113        1,793        2,826        1,241        3,785   \n",
              "살인                2            6            8            9            7   \n",
              "강도               14           12           22            5            7   \n",
              "성폭력             134          180          230          169          283   \n",
              "폭행              705        1,257        1,664          404        1,396   \n",
              "\n",
              "       Unnamed: 110 Unnamed: 111 Unnamed: 112 Unnamed: 113 Unnamed: 114  \\\n",
              "범죄별(3)                                                                    \n",
              "절도            1,346        1,287        1,785        1,072        1,094   \n",
              "살인                4            1            9            4            4   \n",
              "강도                5            5           15           11            4   \n",
              "성폭력             117          123          182          139           78   \n",
              "폭행              848          837        1,633          502          313   \n",
              "\n",
              "       Unnamed: 115 Unnamed: 116 Unnamed: 117 Unnamed: 118 Unnamed: 119  \\\n",
              "범죄별(3)                                                                    \n",
              "절도              749        1,408        1,431        1,313          572   \n",
              "살인                0            7            7            5            1   \n",
              "강도                0            4            8            5            3   \n",
              "성폭력              68          163          142          110           32   \n",
              "폭행              281          525          736          403          240   \n",
              "\n",
              "       Unnamed: 120 Unnamed: 121 Unnamed: 122 Unnamed: 123 Unnamed: 124  \\\n",
              "범죄별(3)                                                                    \n",
              "절도              492        2,916        1,875        1,273        1,274   \n",
              "살인                3            8           12           10            5   \n",
              "강도                4           17           12           10            9   \n",
              "성폭력              60          368          202          156          103   \n",
              "폭행              308        1,600        1,547        1,121          623   \n",
              "\n",
              "       Unnamed: 125 Unnamed: 126 Unnamed: 127 Unnamed: 128 Unnamed: 129  \\\n",
              "범죄별(3)                                                                    \n",
              "절도              734        1,024          805          590          695   \n",
              "살인                4            3            8            6            6   \n",
              "강도               13            7            5            6            5   \n",
              "성폭력              91           96          149           73           59   \n",
              "폭행              684          907          286          393           75   \n",
              "\n",
              "       Unnamed: 130 Unnamed: 131  \n",
              "범죄별(3)                            \n",
              "절도           17,742       13,259  \n",
              "살인               95          105  \n",
              "강도              110           85  \n",
              "성폭력           1,964        1,368  \n",
              "폭행           10,178        5,618  "
            ],
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              "    <tr>\n",
              "      <th>범죄별(3)</th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>절도</th>\n",
              "      <td>59,447</td>\n",
              "      <td>2,258</td>\n",
              "      <td>2,565</td>\n",
              "      <td>1,603</td>\n",
              "      <td>1,755</td>\n",
              "      <td>3,169</td>\n",
              "      <td>1,816</td>\n",
              "      <td>2,382</td>\n",
              "      <td>2,136</td>\n",
              "      <td>1,505</td>\n",
              "      <td>1,349</td>\n",
              "      <td>2,404</td>\n",
              "      <td>2,354</td>\n",
              "      <td>1,871</td>\n",
              "      <td>2,787</td>\n",
              "      <td>2,058</td>\n",
              "      <td>2,318</td>\n",
              "      <td>2,370</td>\n",
              "      <td>1,385</td>\n",
              "      <td>3,108</td>\n",
              "      <td>1,790</td>\n",
              "      <td>3,192</td>\n",
              "      <td>2,766</td>\n",
              "      <td>3,886</td>\n",
              "      <td>4,190</td>\n",
              "      <td>2,430</td>\n",
              "      <td>21,946</td>\n",
              "      <td>918</td>\n",
              "      <td>835</td>\n",
              "      <td>780</td>\n",
              "      <td>529</td>\n",
              "      <td>3,337</td>\n",
              "      <td>1,647</td>\n",
              "      <td>1,728</td>\n",
              "      <td>1,259</td>\n",
              "      <td>2,288</td>\n",
              "      <td>1,714</td>\n",
              "      <td>1,445</td>\n",
              "      <td>502</td>\n",
              "      <td>1,031</td>\n",
              "      <td>1,369</td>\n",
              "      <td>1,968</td>\n",
              "      <td>596</td>\n",
              "      <td>14,627</td>\n",
              "      <td>1,835</td>\n",
              "      <td>2,380</td>\n",
              "      <td>1,296</td>\n",
              "      <td>1,073</td>\n",
              "      <td>2,131</td>\n",
              "      <td>2,085</td>\n",
              "      <td>3,144</td>\n",
              "      <td>683</td>\n",
              "      <td>9,751</td>\n",
              "      <td>506</td>\n",
              "      <td>252</td>\n",
              "      <td>1,291</td>\n",
              "      <td>1,829</td>\n",
              "      <td>843</td>\n",
              "      <td>1,858</td>\n",
              "      <td>2,398</td>\n",
              "      <td>645</td>\n",
              "      <td>104</td>\n",
              "      <td>25</td>\n",
              "      <td>10,368</td>\n",
              "      <td>1,254</td>\n",
              "      <td>2,623</td>\n",
              "      <td>1,120</td>\n",
              "      <td>2,972</td>\n",
              "      <td>2,399</td>\n",
              "      <td>11,464</td>\n",
              "      <td>2,209</td>\n",
              "      <td>2,248</td>\n",
              "      <td>3,458</td>\n",
              "      <td>2,152</td>\n",
              "      <td>1,397</td>\n",
              "      <td>5,782</td>\n",
              "      <td>1,378</td>\n",
              "      <td>920</td>\n",
              "      <td>1,923</td>\n",
              "      <td>658</td>\n",
              "      <td>903</td>\n",
              "      <td>5,686</td>\n",
              "      <td>6,785</td>\n",
              "      <td>4,558</td>\n",
              "      <td>3,799</td>\n",
              "      <td>2,180</td>\n",
              "      <td>4,816</td>\n",
              "      <td>5,525</td>\n",
              "      <td>1,666</td>\n",
              "      <td>2,532</td>\n",
              "      <td>4,228</td>\n",
              "      <td>2,266</td>\n",
              "      <td>3,690</td>\n",
              "      <td>4,983</td>\n",
              "      <td>1,639</td>\n",
              "      <td>2,602</td>\n",
              "      <td>1,751</td>\n",
              "      <td>1,311</td>\n",
              "      <td>2,310</td>\n",
              "      <td>4,416</td>\n",
              "      <td>1,162</td>\n",
              "      <td>1,113</td>\n",
              "      <td>1,793</td>\n",
              "      <td>2,826</td>\n",
              "      <td>1,241</td>\n",
              "      <td>3,785</td>\n",
              "      <td>1,346</td>\n",
              "      <td>1,287</td>\n",
              "      <td>1,785</td>\n",
              "      <td>1,072</td>\n",
              "      <td>1,094</td>\n",
              "      <td>749</td>\n",
              "      <td>1,408</td>\n",
              "      <td>1,431</td>\n",
              "      <td>1,313</td>\n",
              "      <td>572</td>\n",
              "      <td>492</td>\n",
              "      <td>2,916</td>\n",
              "      <td>1,875</td>\n",
              "      <td>1,273</td>\n",
              "      <td>1,274</td>\n",
              "      <td>734</td>\n",
              "      <td>1,024</td>\n",
              "      <td>805</td>\n",
              "      <td>590</td>\n",
              "      <td>695</td>\n",
              "      <td>17,742</td>\n",
              "      <td>13,259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>살인</th>\n",
              "      <td>157</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>71</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>36</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>23</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>강도</th>\n",
              "      <td>345</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>36</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>132</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>6</td>\n",
              "      <td>68</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>112</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>82</td>\n",
              "      <td>30</td>\n",
              "      <td>11</td>\n",
              "      <td>21</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>32</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>28</td>\n",
              "      <td>30</td>\n",
              "      <td>12</td>\n",
              "      <td>32</td>\n",
              "      <td>37</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "      <td>17</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>33</td>\n",
              "      <td>9</td>\n",
              "      <td>14</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>성폭력</th>\n",
              "      <td>8,594</td>\n",
              "      <td>307</td>\n",
              "      <td>377</td>\n",
              "      <td>269</td>\n",
              "      <td>187</td>\n",
              "      <td>328</td>\n",
              "      <td>210</td>\n",
              "      <td>205</td>\n",
              "      <td>220</td>\n",
              "      <td>220</td>\n",
              "      <td>208</td>\n",
              "      <td>294</td>\n",
              "      <td>222</td>\n",
              "      <td>867</td>\n",
              "      <td>423</td>\n",
              "      <td>241</td>\n",
              "      <td>308</td>\n",
              "      <td>426</td>\n",
              "      <td>250</td>\n",
              "      <td>352</td>\n",
              "      <td>437</td>\n",
              "      <td>471</td>\n",
              "      <td>448</td>\n",
              "      <td>761</td>\n",
              "      <td>383</td>\n",
              "      <td>180</td>\n",
              "      <td>2,348</td>\n",
              "      <td>91</td>\n",
              "      <td>61</td>\n",
              "      <td>71</td>\n",
              "      <td>65</td>\n",
              "      <td>326</td>\n",
              "      <td>169</td>\n",
              "      <td>334</td>\n",
              "      <td>202</td>\n",
              "      <td>239</td>\n",
              "      <td>176</td>\n",
              "      <td>144</td>\n",
              "      <td>28</td>\n",
              "      <td>143</td>\n",
              "      <td>117</td>\n",
              "      <td>134</td>\n",
              "      <td>48</td>\n",
              "      <td>1,587</td>\n",
              "      <td>278</td>\n",
              "      <td>216</td>\n",
              "      <td>162</td>\n",
              "      <td>121</td>\n",
              "      <td>248</td>\n",
              "      <td>188</td>\n",
              "      <td>312</td>\n",
              "      <td>62</td>\n",
              "      <td>1,681</td>\n",
              "      <td>98</td>\n",
              "      <td>25</td>\n",
              "      <td>220</td>\n",
              "      <td>297</td>\n",
              "      <td>204</td>\n",
              "      <td>244</td>\n",
              "      <td>335</td>\n",
              "      <td>226</td>\n",
              "      <td>25</td>\n",
              "      <td>7</td>\n",
              "      <td>1,050</td>\n",
              "      <td>150</td>\n",
              "      <td>224</td>\n",
              "      <td>91</td>\n",
              "      <td>315</td>\n",
              "      <td>270</td>\n",
              "      <td>805</td>\n",
              "      <td>170</td>\n",
              "      <td>144</td>\n",
              "      <td>247</td>\n",
              "      <td>107</td>\n",
              "      <td>137</td>\n",
              "      <td>534</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>196</td>\n",
              "      <td>68</td>\n",
              "      <td>84</td>\n",
              "      <td>530</td>\n",
              "      <td>722</td>\n",
              "      <td>579</td>\n",
              "      <td>328</td>\n",
              "      <td>467</td>\n",
              "      <td>372</td>\n",
              "      <td>354</td>\n",
              "      <td>163</td>\n",
              "      <td>175</td>\n",
              "      <td>384</td>\n",
              "      <td>188</td>\n",
              "      <td>396</td>\n",
              "      <td>370</td>\n",
              "      <td>154</td>\n",
              "      <td>228</td>\n",
              "      <td>223</td>\n",
              "      <td>133</td>\n",
              "      <td>183</td>\n",
              "      <td>369</td>\n",
              "      <td>107</td>\n",
              "      <td>134</td>\n",
              "      <td>180</td>\n",
              "      <td>230</td>\n",
              "      <td>169</td>\n",
              "      <td>283</td>\n",
              "      <td>117</td>\n",
              "      <td>123</td>\n",
              "      <td>182</td>\n",
              "      <td>139</td>\n",
              "      <td>78</td>\n",
              "      <td>68</td>\n",
              "      <td>163</td>\n",
              "      <td>142</td>\n",
              "      <td>110</td>\n",
              "      <td>32</td>\n",
              "      <td>60</td>\n",
              "      <td>368</td>\n",
              "      <td>202</td>\n",
              "      <td>156</td>\n",
              "      <td>103</td>\n",
              "      <td>91</td>\n",
              "      <td>96</td>\n",
              "      <td>149</td>\n",
              "      <td>73</td>\n",
              "      <td>59</td>\n",
              "      <td>1,964</td>\n",
              "      <td>1,368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>폭행</th>\n",
              "      <td>39,108</td>\n",
              "      <td>1,482</td>\n",
              "      <td>1,474</td>\n",
              "      <td>1,221</td>\n",
              "      <td>1,020</td>\n",
              "      <td>1,645</td>\n",
              "      <td>1,503</td>\n",
              "      <td>1,613</td>\n",
              "      <td>1,399</td>\n",
              "      <td>1,509</td>\n",
              "      <td>1,027</td>\n",
              "      <td>1,787</td>\n",
              "      <td>1,607</td>\n",
              "      <td>1,199</td>\n",
              "      <td>1,368</td>\n",
              "      <td>1,580</td>\n",
              "      <td>1,755</td>\n",
              "      <td>1,715</td>\n",
              "      <td>1,227</td>\n",
              "      <td>2,016</td>\n",
              "      <td>1,134</td>\n",
              "      <td>2,273</td>\n",
              "      <td>1,519</td>\n",
              "      <td>2,444</td>\n",
              "      <td>2,007</td>\n",
              "      <td>1,584</td>\n",
              "      <td>8,577</td>\n",
              "      <td>363</td>\n",
              "      <td>250</td>\n",
              "      <td>367</td>\n",
              "      <td>294</td>\n",
              "      <td>1,352</td>\n",
              "      <td>630</td>\n",
              "      <td>488</td>\n",
              "      <td>619</td>\n",
              "      <td>875</td>\n",
              "      <td>778</td>\n",
              "      <td>558</td>\n",
              "      <td>119</td>\n",
              "      <td>558</td>\n",
              "      <td>508</td>\n",
              "      <td>624</td>\n",
              "      <td>194</td>\n",
              "      <td>5,809</td>\n",
              "      <td>437</td>\n",
              "      <td>729</td>\n",
              "      <td>882</td>\n",
              "      <td>520</td>\n",
              "      <td>735</td>\n",
              "      <td>684</td>\n",
              "      <td>1,506</td>\n",
              "      <td>316</td>\n",
              "      <td>9,291</td>\n",
              "      <td>527</td>\n",
              "      <td>291</td>\n",
              "      <td>1,292</td>\n",
              "      <td>1,638</td>\n",
              "      <td>783</td>\n",
              "      <td>1,780</td>\n",
              "      <td>1,859</td>\n",
              "      <td>930</td>\n",
              "      <td>145</td>\n",
              "      <td>46</td>\n",
              "      <td>6,014</td>\n",
              "      <td>802</td>\n",
              "      <td>1,629</td>\n",
              "      <td>612</td>\n",
              "      <td>1,704</td>\n",
              "      <td>1,267</td>\n",
              "      <td>2,710</td>\n",
              "      <td>796</td>\n",
              "      <td>435</td>\n",
              "      <td>859</td>\n",
              "      <td>340</td>\n",
              "      <td>280</td>\n",
              "      <td>3,998</td>\n",
              "      <td>824</td>\n",
              "      <td>485</td>\n",
              "      <td>1,566</td>\n",
              "      <td>352</td>\n",
              "      <td>771</td>\n",
              "      <td>3,101</td>\n",
              "      <td>4,172</td>\n",
              "      <td>2,915</td>\n",
              "      <td>1,832</td>\n",
              "      <td>1,778</td>\n",
              "      <td>2,285</td>\n",
              "      <td>2,175</td>\n",
              "      <td>1,241</td>\n",
              "      <td>938</td>\n",
              "      <td>2,586</td>\n",
              "      <td>695</td>\n",
              "      <td>1,971</td>\n",
              "      <td>2,370</td>\n",
              "      <td>893</td>\n",
              "      <td>2,129</td>\n",
              "      <td>818</td>\n",
              "      <td>956</td>\n",
              "      <td>707</td>\n",
              "      <td>1,563</td>\n",
              "      <td>909</td>\n",
              "      <td>705</td>\n",
              "      <td>1,257</td>\n",
              "      <td>1,664</td>\n",
              "      <td>404</td>\n",
              "      <td>1,396</td>\n",
              "      <td>848</td>\n",
              "      <td>837</td>\n",
              "      <td>1,633</td>\n",
              "      <td>502</td>\n",
              "      <td>313</td>\n",
              "      <td>281</td>\n",
              "      <td>525</td>\n",
              "      <td>736</td>\n",
              "      <td>403</td>\n",
              "      <td>240</td>\n",
              "      <td>308</td>\n",
              "      <td>1,600</td>\n",
              "      <td>1,547</td>\n",
              "      <td>1,121</td>\n",
              "      <td>623</td>\n",
              "      <td>684</td>\n",
              "      <td>907</td>\n",
              "      <td>286</td>\n",
              "      <td>393</td>\n",
              "      <td>75</td>\n",
              "      <td>10,178</td>\n",
              "      <td>5,618</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-35913204-56e7-474c-834f-4af2b9eaa1f2')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
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              "  .colab-df-quickchart {\n",
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              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-35913204-56e7-474c-834f-4af2b9eaa1f2 button.colab-df-quickchart');\n",
              "  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-cd113982-1240-4c6e-bc49-50ee696ada78 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-cd113982-1240-4c6e-bc49-50ee696ada78');\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": 482
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for p in df3.columns:\n",
        "    df3[p] = df3[p].str.replace(',','').astype('int64')"
      ],
      "metadata": {
        "id": "S5q8MQcvnCBa"
      },
      "execution_count": 483,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.columns = region[1:]"
      ],
      "metadata": {
        "id": "ciNqBas1o0QF"
      },
      "execution_count": 485,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "by4wZhd7o42r",
        "outputId": "9e6c00c9-fdd1-45ff-8b59-d37f17697f2c"
      },
      "execution_count": 486,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           서울    종로    중구    용산    성동    광진   동대문    중랑    성북    강북    도봉  \\\n",
              "범죄별(3)                                                                      \n",
              "절도      59447  2258  2565  1603  1755  3169  1816  2382  2136  1505  1349   \n",
              "살인        157     2     6     1     1     8    12    11     8     6     6   \n",
              "강도        345    11    14     7     5    16    13    14    16    21    11   \n",
              "성폭력      8594   307   377   269   187   328   210   205   220   220   208   \n",
              "폭행      39108  1482  1474  1221  1020  1645  1503  1613  1399  1509  1027   \n",
              "\n",
              "          노원    은평   서대문    마포    양천    강서    구로    금천   영등포    동작    관악  \\\n",
              "범죄별(3)                                                                     \n",
              "절도      2404  2354  1871  2787  2058  2318  2370  1385  3108  1790  3192   \n",
              "살인         5     5     3     4     5     8     4     8    13     2     7   \n",
              "강도         8    24     4    22    12     9    12     9    11    10    25   \n",
              "성폭력      294   222   867   423   241   308   426   250   352   437   471   \n",
              "폭행      1787  1607  1199  1368  1580  1755  1715  1227  2016  1134  2273   \n",
              "\n",
              "          서초    강남    송파    강동     부산   중구   서구   동구   영도   부산진    동래    남구  \\\n",
              "범죄별(3)                                                                        \n",
              "절도      2766  3886  4190  2430  21946  918  835  780  529  3337  1647  1728   \n",
              "살인         6    12     9     5     71    4    5    4    3     7     2     3   \n",
              "강도        15    36    10    10    132    7    6    3    5    21     7     6   \n",
              "성폭력      448   761   383   180   2348   91   61   71   65   326   169   334   \n",
              "폭행      1519  2444  2007  1584   8577  363  250  367  294  1352   630   488   \n",
              "\n",
              "          북구   해운대    사하    금정   강서    연제    수영    사상  기장군     대구    중구    동구  \\\n",
              "범죄별(3)                                                                          \n",
              "절도      1259  2288  1714  1445  502  1031  1369  1968  596  14627  1835  2380   \n",
              "살인         6     9     6     4    1     2     1    12    2     36     6     6   \n",
              "강도         6    18    18     3    1     1     4    20    6     68     7    21   \n",
              "성폭력      202   239   176   144   28   143   117   134   48   1587   278   216   \n",
              "폭행       619   875   778   558  119   558   508   624  194   5809   437   729   \n",
              "\n",
              "          서구    남구    북구    수성    달서  달성군    인천   중구   동구    서구   미추홀   연수  \\\n",
              "범죄별(3)                                                                       \n",
              "절도      1296  1073  2131  2085  3144  683  9751  506  252  1291  1829  843   \n",
              "살인         7     0     4     5     6    2    51    6    0     5    11    5   \n",
              "강도        11     4     9     5     9    2   112    7    2    16    26    4   \n",
              "성폭력      162   121   248   188   312   62  1681   98   25   220   297  204   \n",
              "폭행       882   520   735   684  1506  316  9291  527  291  1292  1638  783   \n",
              "\n",
              "          남동    부평   계양  강화군  옹진군     광주    동구    서구    남구    북구    광산     대전  \\\n",
              "범죄별(3)                                                                          \n",
              "절도      1858  2398  645  104   25  10368  1254  2623  1120  2972  2399  11464   \n",
              "살인        16     4    3    1    0     19     2     7     1     6     3     30   \n",
              "강도        17    28   12    0    0     53    15     9     3    16    10     82   \n",
              "성폭력      244   335  226   25    7   1050   150   224    91   315   270    805   \n",
              "폭행      1780  1859  930  145   46   6014   802  1629   612  1704  1267   2710   \n",
              "\n",
              "          중구    동구    서구    유성    대덕    울산    중구   동구    남구   북구  울주군    부천  \\\n",
              "범죄별(3)                                                                        \n",
              "절도      2209  2248  3458  2152  1397  5782  1378  920  1923  658  903  5686   \n",
              "살인         2    10     7     4     7    23     5    2     8    3    5    15   \n",
              "강도        30    11    21    10    10    32     6    2    18    3    3    27   \n",
              "성폭력      170   144   247   107   137   534   109   77   196   68   84   530   \n",
              "폭행       796   435   859   340   280  3998   824  485  1566  352  771  3101   \n",
              "\n",
              "          수원    성남    전주    안양    청주    창원    광명    포항    안산    진주    고양  \\\n",
              "범죄별(3)                                                                     \n",
              "절도      6785  4558  3799  2180  4816  5525  1666  2532  4228  2266  3690   \n",
              "살인        16    12     9     7    16    21     3    11    12    12    11   \n",
              "강도        32    28    30    12    32    37    12     5    19     7    17   \n",
              "성폭력      722   579   328   467   372   354   163   175   384   188   396   \n",
              "폭행      4172  2915  1832  1778  2285  2175  1241   938  2586   695  1971   \n",
              "\n",
              "          제주    목포   의정부    익산    군산    구미    천안    여수    춘천    원주    평택  \\\n",
              "범죄별(3)                                                                     \n",
              "절도      4983  1639  2602  1751  1311  2310  4416  1162  1113  1793  2826   \n",
              "살인        18     4     9     5     5     5    17     4     2     6     8   \n",
              "강도        25    12    13    13     8    20    33     9    14    12    22   \n",
              "성폭력      370   154   228   223   133   183   369   107   134   180   230   \n",
              "폭행      2370   893  2129   818   956   707  1563   909   705  1257  1664   \n",
              "\n",
              "          경주    김해    순천    군포   남양주    강릉    충주   안동    경산    아산    거제   김천  \\\n",
              "범죄별(3)                                                                         \n",
              "절도      1241  3785  1346  1287  1785  1072  1094  749  1408  1431  1313  572   \n",
              "살인         9     7     4     1     9     4     4    0     7     7     5    1   \n",
              "강도         5     7     5     5    15    11     4    0     4     8     5    3   \n",
              "성폭력      169   283   117   123   182   139    78   68   163   142   110   32   \n",
              "폭행       404  1396   848   837  1633   502   313  281   525   736   403  240   \n",
              "\n",
              "         정읍    용인    시흥    파주    양산   이천    구리   서산   제천   논산  기타 도시   도시이외  \n",
              "범죄별(3)                                                                       \n",
              "절도      492  2916  1875  1273  1274  734  1024  805  590  695  17742  13259  \n",
              "살인        3     8    12    10     5    4     3    8    6    6     95    105  \n",
              "강도        4    17    12    10     9   13     7    5    6    5    110     85  \n",
              "성폭력      60   368   202   156   103   91    96  149   73   59   1964   1368  \n",
              "폭행      308  1600  1547  1121   623  684   907  286  393   75  10178   5618  "
            ],
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              "  <div id=\"df-3c33f67f-7fd2-47a4-bea0-408100087329\">\n",
              "    <div class=\"colab-df-container\">\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>서울</th>\n",
              "      <th>종로</th>\n",
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              "      <th>성동</th>\n",
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              "      <th>강서</th>\n",
              "      <th>구로</th>\n",
              "      <th>금천</th>\n",
              "      <th>영등포</th>\n",
              "      <th>동작</th>\n",
              "      <th>관악</th>\n",
              "      <th>서초</th>\n",
              "      <th>강남</th>\n",
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              "      <th>부산</th>\n",
              "      <th>중구</th>\n",
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              "      <th>동구</th>\n",
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              "      <th>남구</th>\n",
              "      <th>북구</th>\n",
              "      <th>해운대</th>\n",
              "      <th>사하</th>\n",
              "      <th>금정</th>\n",
              "      <th>강서</th>\n",
              "      <th>연제</th>\n",
              "      <th>수영</th>\n",
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              "      <th>서구</th>\n",
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              "      <th>미추홀</th>\n",
              "      <th>연수</th>\n",
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              "      <th>옹진군</th>\n",
              "      <th>광주</th>\n",
              "      <th>동구</th>\n",
              "      <th>서구</th>\n",
              "      <th>남구</th>\n",
              "      <th>북구</th>\n",
              "      <th>광산</th>\n",
              "      <th>대전</th>\n",
              "      <th>중구</th>\n",
              "      <th>동구</th>\n",
              "      <th>서구</th>\n",
              "      <th>유성</th>\n",
              "      <th>대덕</th>\n",
              "      <th>울산</th>\n",
              "      <th>중구</th>\n",
              "      <th>동구</th>\n",
              "      <th>남구</th>\n",
              "      <th>북구</th>\n",
              "      <th>울주군</th>\n",
              "      <th>부천</th>\n",
              "      <th>수원</th>\n",
              "      <th>성남</th>\n",
              "      <th>전주</th>\n",
              "      <th>안양</th>\n",
              "      <th>청주</th>\n",
              "      <th>창원</th>\n",
              "      <th>광명</th>\n",
              "      <th>포항</th>\n",
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              "      <th>의정부</th>\n",
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              "      <th>군산</th>\n",
              "      <th>구미</th>\n",
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              "      <th>춘천</th>\n",
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              "      <th>평택</th>\n",
              "      <th>경주</th>\n",
              "      <th>김해</th>\n",
              "      <th>순천</th>\n",
              "      <th>군포</th>\n",
              "      <th>남양주</th>\n",
              "      <th>강릉</th>\n",
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              "      <th>제천</th>\n",
              "      <th>논산</th>\n",
              "      <th>기타 도시</th>\n",
              "      <th>도시이외</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>범죄별(3)</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>절도</th>\n",
              "      <td>59447</td>\n",
              "      <td>2258</td>\n",
              "      <td>2565</td>\n",
              "      <td>1603</td>\n",
              "      <td>1755</td>\n",
              "      <td>3169</td>\n",
              "      <td>1816</td>\n",
              "      <td>2382</td>\n",
              "      <td>2136</td>\n",
              "      <td>1505</td>\n",
              "      <td>1349</td>\n",
              "      <td>2404</td>\n",
              "      <td>2354</td>\n",
              "      <td>1871</td>\n",
              "      <td>2787</td>\n",
              "      <td>2058</td>\n",
              "      <td>2318</td>\n",
              "      <td>2370</td>\n",
              "      <td>1385</td>\n",
              "      <td>3108</td>\n",
              "      <td>1790</td>\n",
              "      <td>3192</td>\n",
              "      <td>2766</td>\n",
              "      <td>3886</td>\n",
              "      <td>4190</td>\n",
              "      <td>2430</td>\n",
              "      <td>21946</td>\n",
              "      <td>918</td>\n",
              "      <td>835</td>\n",
              "      <td>780</td>\n",
              "      <td>529</td>\n",
              "      <td>3337</td>\n",
              "      <td>1647</td>\n",
              "      <td>1728</td>\n",
              "      <td>1259</td>\n",
              "      <td>2288</td>\n",
              "      <td>1714</td>\n",
              "      <td>1445</td>\n",
              "      <td>502</td>\n",
              "      <td>1031</td>\n",
              "      <td>1369</td>\n",
              "      <td>1968</td>\n",
              "      <td>596</td>\n",
              "      <td>14627</td>\n",
              "      <td>1835</td>\n",
              "      <td>2380</td>\n",
              "      <td>1296</td>\n",
              "      <td>1073</td>\n",
              "      <td>2131</td>\n",
              "      <td>2085</td>\n",
              "      <td>3144</td>\n",
              "      <td>683</td>\n",
              "      <td>9751</td>\n",
              "      <td>506</td>\n",
              "      <td>252</td>\n",
              "      <td>1291</td>\n",
              "      <td>1829</td>\n",
              "      <td>843</td>\n",
              "      <td>1858</td>\n",
              "      <td>2398</td>\n",
              "      <td>645</td>\n",
              "      <td>104</td>\n",
              "      <td>25</td>\n",
              "      <td>10368</td>\n",
              "      <td>1254</td>\n",
              "      <td>2623</td>\n",
              "      <td>1120</td>\n",
              "      <td>2972</td>\n",
              "      <td>2399</td>\n",
              "      <td>11464</td>\n",
              "      <td>2209</td>\n",
              "      <td>2248</td>\n",
              "      <td>3458</td>\n",
              "      <td>2152</td>\n",
              "      <td>1397</td>\n",
              "      <td>5782</td>\n",
              "      <td>1378</td>\n",
              "      <td>920</td>\n",
              "      <td>1923</td>\n",
              "      <td>658</td>\n",
              "      <td>903</td>\n",
              "      <td>5686</td>\n",
              "      <td>6785</td>\n",
              "      <td>4558</td>\n",
              "      <td>3799</td>\n",
              "      <td>2180</td>\n",
              "      <td>4816</td>\n",
              "      <td>5525</td>\n",
              "      <td>1666</td>\n",
              "      <td>2532</td>\n",
              "      <td>4228</td>\n",
              "      <td>2266</td>\n",
              "      <td>3690</td>\n",
              "      <td>4983</td>\n",
              "      <td>1639</td>\n",
              "      <td>2602</td>\n",
              "      <td>1751</td>\n",
              "      <td>1311</td>\n",
              "      <td>2310</td>\n",
              "      <td>4416</td>\n",
              "      <td>1162</td>\n",
              "      <td>1113</td>\n",
              "      <td>1793</td>\n",
              "      <td>2826</td>\n",
              "      <td>1241</td>\n",
              "      <td>3785</td>\n",
              "      <td>1346</td>\n",
              "      <td>1287</td>\n",
              "      <td>1785</td>\n",
              "      <td>1072</td>\n",
              "      <td>1094</td>\n",
              "      <td>749</td>\n",
              "      <td>1408</td>\n",
              "      <td>1431</td>\n",
              "      <td>1313</td>\n",
              "      <td>572</td>\n",
              "      <td>492</td>\n",
              "      <td>2916</td>\n",
              "      <td>1875</td>\n",
              "      <td>1273</td>\n",
              "      <td>1274</td>\n",
              "      <td>734</td>\n",
              "      <td>1024</td>\n",
              "      <td>805</td>\n",
              "      <td>590</td>\n",
              "      <td>695</td>\n",
              "      <td>17742</td>\n",
              "      <td>13259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>살인</th>\n",
              "      <td>157</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>71</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>36</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>23</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>95</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>강도</th>\n",
              "      <td>345</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>22</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>36</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>132</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>6</td>\n",
              "      <td>68</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>112</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>82</td>\n",
              "      <td>30</td>\n",
              "      <td>11</td>\n",
              "      <td>21</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>32</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>28</td>\n",
              "      <td>30</td>\n",
              "      <td>12</td>\n",
              "      <td>32</td>\n",
              "      <td>37</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "      <td>17</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>33</td>\n",
              "      <td>9</td>\n",
              "      <td>14</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>110</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>성폭력</th>\n",
              "      <td>8594</td>\n",
              "      <td>307</td>\n",
              "      <td>377</td>\n",
              "      <td>269</td>\n",
              "      <td>187</td>\n",
              "      <td>328</td>\n",
              "      <td>210</td>\n",
              "      <td>205</td>\n",
              "      <td>220</td>\n",
              "      <td>220</td>\n",
              "      <td>208</td>\n",
              "      <td>294</td>\n",
              "      <td>222</td>\n",
              "      <td>867</td>\n",
              "      <td>423</td>\n",
              "      <td>241</td>\n",
              "      <td>308</td>\n",
              "      <td>426</td>\n",
              "      <td>250</td>\n",
              "      <td>352</td>\n",
              "      <td>437</td>\n",
              "      <td>471</td>\n",
              "      <td>448</td>\n",
              "      <td>761</td>\n",
              "      <td>383</td>\n",
              "      <td>180</td>\n",
              "      <td>2348</td>\n",
              "      <td>91</td>\n",
              "      <td>61</td>\n",
              "      <td>71</td>\n",
              "      <td>65</td>\n",
              "      <td>326</td>\n",
              "      <td>169</td>\n",
              "      <td>334</td>\n",
              "      <td>202</td>\n",
              "      <td>239</td>\n",
              "      <td>176</td>\n",
              "      <td>144</td>\n",
              "      <td>28</td>\n",
              "      <td>143</td>\n",
              "      <td>117</td>\n",
              "      <td>134</td>\n",
              "      <td>48</td>\n",
              "      <td>1587</td>\n",
              "      <td>278</td>\n",
              "      <td>216</td>\n",
              "      <td>162</td>\n",
              "      <td>121</td>\n",
              "      <td>248</td>\n",
              "      <td>188</td>\n",
              "      <td>312</td>\n",
              "      <td>62</td>\n",
              "      <td>1681</td>\n",
              "      <td>98</td>\n",
              "      <td>25</td>\n",
              "      <td>220</td>\n",
              "      <td>297</td>\n",
              "      <td>204</td>\n",
              "      <td>244</td>\n",
              "      <td>335</td>\n",
              "      <td>226</td>\n",
              "      <td>25</td>\n",
              "      <td>7</td>\n",
              "      <td>1050</td>\n",
              "      <td>150</td>\n",
              "      <td>224</td>\n",
              "      <td>91</td>\n",
              "      <td>315</td>\n",
              "      <td>270</td>\n",
              "      <td>805</td>\n",
              "      <td>170</td>\n",
              "      <td>144</td>\n",
              "      <td>247</td>\n",
              "      <td>107</td>\n",
              "      <td>137</td>\n",
              "      <td>534</td>\n",
              "      <td>109</td>\n",
              "      <td>77</td>\n",
              "      <td>196</td>\n",
              "      <td>68</td>\n",
              "      <td>84</td>\n",
              "      <td>530</td>\n",
              "      <td>722</td>\n",
              "      <td>579</td>\n",
              "      <td>328</td>\n",
              "      <td>467</td>\n",
              "      <td>372</td>\n",
              "      <td>354</td>\n",
              "      <td>163</td>\n",
              "      <td>175</td>\n",
              "      <td>384</td>\n",
              "      <td>188</td>\n",
              "      <td>396</td>\n",
              "      <td>370</td>\n",
              "      <td>154</td>\n",
              "      <td>228</td>\n",
              "      <td>223</td>\n",
              "      <td>133</td>\n",
              "      <td>183</td>\n",
              "      <td>369</td>\n",
              "      <td>107</td>\n",
              "      <td>134</td>\n",
              "      <td>180</td>\n",
              "      <td>230</td>\n",
              "      <td>169</td>\n",
              "      <td>283</td>\n",
              "      <td>117</td>\n",
              "      <td>123</td>\n",
              "      <td>182</td>\n",
              "      <td>139</td>\n",
              "      <td>78</td>\n",
              "      <td>68</td>\n",
              "      <td>163</td>\n",
              "      <td>142</td>\n",
              "      <td>110</td>\n",
              "      <td>32</td>\n",
              "      <td>60</td>\n",
              "      <td>368</td>\n",
              "      <td>202</td>\n",
              "      <td>156</td>\n",
              "      <td>103</td>\n",
              "      <td>91</td>\n",
              "      <td>96</td>\n",
              "      <td>149</td>\n",
              "      <td>73</td>\n",
              "      <td>59</td>\n",
              "      <td>1964</td>\n",
              "      <td>1368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>폭행</th>\n",
              "      <td>39108</td>\n",
              "      <td>1482</td>\n",
              "      <td>1474</td>\n",
              "      <td>1221</td>\n",
              "      <td>1020</td>\n",
              "      <td>1645</td>\n",
              "      <td>1503</td>\n",
              "      <td>1613</td>\n",
              "      <td>1399</td>\n",
              "      <td>1509</td>\n",
              "      <td>1027</td>\n",
              "      <td>1787</td>\n",
              "      <td>1607</td>\n",
              "      <td>1199</td>\n",
              "      <td>1368</td>\n",
              "      <td>1580</td>\n",
              "      <td>1755</td>\n",
              "      <td>1715</td>\n",
              "      <td>1227</td>\n",
              "      <td>2016</td>\n",
              "      <td>1134</td>\n",
              "      <td>2273</td>\n",
              "      <td>1519</td>\n",
              "      <td>2444</td>\n",
              "      <td>2007</td>\n",
              "      <td>1584</td>\n",
              "      <td>8577</td>\n",
              "      <td>363</td>\n",
              "      <td>250</td>\n",
              "      <td>367</td>\n",
              "      <td>294</td>\n",
              "      <td>1352</td>\n",
              "      <td>630</td>\n",
              "      <td>488</td>\n",
              "      <td>619</td>\n",
              "      <td>875</td>\n",
              "      <td>778</td>\n",
              "      <td>558</td>\n",
              "      <td>119</td>\n",
              "      <td>558</td>\n",
              "      <td>508</td>\n",
              "      <td>624</td>\n",
              "      <td>194</td>\n",
              "      <td>5809</td>\n",
              "      <td>437</td>\n",
              "      <td>729</td>\n",
              "      <td>882</td>\n",
              "      <td>520</td>\n",
              "      <td>735</td>\n",
              "      <td>684</td>\n",
              "      <td>1506</td>\n",
              "      <td>316</td>\n",
              "      <td>9291</td>\n",
              "      <td>527</td>\n",
              "      <td>291</td>\n",
              "      <td>1292</td>\n",
              "      <td>1638</td>\n",
              "      <td>783</td>\n",
              "      <td>1780</td>\n",
              "      <td>1859</td>\n",
              "      <td>930</td>\n",
              "      <td>145</td>\n",
              "      <td>46</td>\n",
              "      <td>6014</td>\n",
              "      <td>802</td>\n",
              "      <td>1629</td>\n",
              "      <td>612</td>\n",
              "      <td>1704</td>\n",
              "      <td>1267</td>\n",
              "      <td>2710</td>\n",
              "      <td>796</td>\n",
              "      <td>435</td>\n",
              "      <td>859</td>\n",
              "      <td>340</td>\n",
              "      <td>280</td>\n",
              "      <td>3998</td>\n",
              "      <td>824</td>\n",
              "      <td>485</td>\n",
              "      <td>1566</td>\n",
              "      <td>352</td>\n",
              "      <td>771</td>\n",
              "      <td>3101</td>\n",
              "      <td>4172</td>\n",
              "      <td>2915</td>\n",
              "      <td>1832</td>\n",
              "      <td>1778</td>\n",
              "      <td>2285</td>\n",
              "      <td>2175</td>\n",
              "      <td>1241</td>\n",
              "      <td>938</td>\n",
              "      <td>2586</td>\n",
              "      <td>695</td>\n",
              "      <td>1971</td>\n",
              "      <td>2370</td>\n",
              "      <td>893</td>\n",
              "      <td>2129</td>\n",
              "      <td>818</td>\n",
              "      <td>956</td>\n",
              "      <td>707</td>\n",
              "      <td>1563</td>\n",
              "      <td>909</td>\n",
              "      <td>705</td>\n",
              "      <td>1257</td>\n",
              "      <td>1664</td>\n",
              "      <td>404</td>\n",
              "      <td>1396</td>\n",
              "      <td>848</td>\n",
              "      <td>837</td>\n",
              "      <td>1633</td>\n",
              "      <td>502</td>\n",
              "      <td>313</td>\n",
              "      <td>281</td>\n",
              "      <td>525</td>\n",
              "      <td>736</td>\n",
              "      <td>403</td>\n",
              "      <td>240</td>\n",
              "      <td>308</td>\n",
              "      <td>1600</td>\n",
              "      <td>1547</td>\n",
              "      <td>1121</td>\n",
              "      <td>623</td>\n",
              "      <td>684</td>\n",
              "      <td>907</td>\n",
              "      <td>286</td>\n",
              "      <td>393</td>\n",
              "      <td>75</td>\n",
              "      <td>10178</td>\n",
              "      <td>5618</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c33f67f-7fd2-47a4-bea0-408100087329')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-31a770e0-671f-4a21-b726-6f8259a8ec87\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31a770e0-671f-4a21-b726-6f8259a8ec87')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-31a770e0-671f-4a21-b726-6f8259a8ec87 button.colab-df-quickchart');\n",
              "  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-3c33f67f-7fd2-47a4-bea0-408100087329 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-3c33f67f-7fd2-47a4-bea0-408100087329');\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": 486
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkqAgjd3iU8r",
        "outputId": "930f9573-a001-4ab6-a3eb-6ccdb1a375bb"
      },
      "execution_count": 430,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['범죄별(3)', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7',\n",
              "       'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12',\n",
              "       ...\n",
              "       'Unnamed: 122', 'Unnamed: 123', 'Unnamed: 124', 'Unnamed: 125',\n",
              "       'Unnamed: 126', 'Unnamed: 127', 'Unnamed: 128', 'Unnamed: 129',\n",
              "       'Unnamed: 130', 'Unnamed: 131'],\n",
              "      dtype='object', length=129)"
            ]
          },
          "metadata": {},
          "execution_count": 430
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.loc[0].values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfiahP89iniQ",
        "outputId": "86cbcc4c-ab20-4bf6-a185-fc148c0d4168"
      },
      "execution_count": 431,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, '서울', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, '부산', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, nan, nan, nan, nan, '대구', nan, nan, nan, nan, nan, nan, nan,\n",
              "       nan, '인천', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, '광주',\n",
              "       nan, nan, nan, nan, nan, '대전', nan, nan, nan, nan, nan, '울산', nan,\n",
              "       nan, nan, nan, nan, '부천', '수원', '성남', '전주', '안양', '청주', '창원', '광명',\n",
              "       '포항', '안산', '진주', '고양', '제주', '목포', '의정부', '익산', '군산', '구미', '천안',\n",
              "       '여수', '춘천', '원주', '평택', '경주', '김해', '순천', '군포', '남양주', '강릉', '충주',\n",
              "       '안동', '경산', '아산', '거제', '김천', '정읍', '용인', '시흥', '파주', '양산', '이천',\n",
              "       '구리', '서산', '제천', '논산', '기타 도시', '도시이외'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 431
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.loc[1].values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cthUyxVniiTX",
        "outputId": "c29afe7c-bf1b-44c2-9c90-cef7f65d61ea"
      },
      "execution_count": 432,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, '소계', '종로', '중구', '용산', '성동', '광진', '동대문', '중랑', '성북', '강북',\n",
              "       '도봉', '노원', '은평', '서대문', '마포', '양천', '강서', '구로', '금천', '영등포', '동작',\n",
              "       '관악', '서초', '강남', '송파', '강동', '소계', '중구', '서구', '동구', '영도', '부산진',\n",
              "       '동래', '남구', '북구', '해운대', '사하', '금정', '강서', '연제', '수영', '사상', '기장군',\n",
              "       '소계', '중구', '동구', '서구', '남구', '북구', '수성', '달서', '달성군', '소계', '중구',\n",
              "       '동구', '서구', '미추홀', '연수', '남동', '부평', '계양', '강화군', '옹진군', '소계',\n",
              "       '동구', '서구', '남구', '북구', '광산', '소계', '중구', '동구', '서구', '유성', '대덕',\n",
              "       '소계', '중구', '동구', '남구', '북구', '울주군', '소계', '소계', '소계', '소계', '소계',\n",
              "       '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계',\n",
              "       '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계',\n",
              "       '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계',\n",
              "       '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계', '소계'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 432
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "region=['범죄별', '서울', '종로', '중구', '용산', '성동', '광진', '동대문', '중랑', '성북', '강북',\n",
        "       '도봉', '노원', '은평', '서대문', '마포', '양천', '강서', '구로', '금천', '영등포', '동작',\n",
        "       '관악', '서초', '강남', '송파', '강동', '부산', '중구', '서구', '동구', '영도', '부산진',\n",
        "       '동래', '남구', '북구', '해운대', '사하', '금정', '강서', '연제', '수영', '사상', '기장군',\n",
        "       '대구', '중구', '동구', '서구', '남구', '북구', '수성', '달서', '달성군', '인천', '중구',\n",
        "       '동구', '서구', '미추홀', '연수', '남동', '부평', '계양', '강화군', '옹진군', '광주',\n",
        "       '동구', '서구', '남구', '북구', '광산', '대전', '중구', '동구', '서구', '유성', '대덕',\n",
        "       '울산', '중구', '동구', '남구', '북구', '울주군', '부천', '수원', '성남', '전주', '안양', '청주', '창원', '광명',\n",
        "       '포항', '안산', '진주', '고양', '제주', '목포', '의정부', '익산', '군산', '구미', '천안',\n",
        "       '여수', '춘천', '원주', '평택', '경주', '김해', '순천', '군포', '남양주', '강릉', '충주',\n",
        "       '안동', '경산', '아산', '거제', '김천', '정읍', '용인', '시흥', '파주', '양산', '이천',\n",
        "       '구리', '서산', '제천', '논산','기타 도시', '도시이외']"
      ],
      "metadata": {
        "id": "ddiujG18ip8s"
      },
      "execution_count": 433,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.columns = region"
      ],
      "metadata": {
        "id": "IDWUFl5QkLBu"
      },
      "execution_count": 434,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 지역별 데이터 병합(2014~2021)"
      ],
      "metadata": {
        "id": "RXt6Qio4lOZ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "region=['범죄별',\n",
        "\n",
        "       '서울', '종로', '중구', '용산', '성동', '광진', '동대문', '중랑', '성북', '강북',\n",
        "       '도봉', '노원', '은평', '서대문', '마포', '양천', '강서', '구로', '금천', '영등포', '동작',\n",
        "       '관악', '서초', '강남', '송파', '강동',\n",
        "\n",
        "       '부산', '중구', '서구', '동구', '영도', '부산진',\n",
        "       '동래', '남구', '북구', '해운대', '사하', '금정', '강서', '연제', '수영', '사상', '기장군',\n",
        "\n",
        "       '대구', '중구', '동구', '서구', '남구', '북구', '수성', '달서', '달성군',\n",
        "\n",
        "       '인천', '중구','동구', '서구', '미추홀', '연수', '남동', '부평', '계양', '강화군', '옹진군',\n",
        "\n",
        "       '광주','동구', '서구', '남구', '북구', '광산',\n",
        "\n",
        "       '대전', '중구', '동구', '서구', '유성', '대덕',\n",
        "\n",
        "       '울산', '중구', '동구', '남구', '북구', '울주군',\n",
        "\n",
        "       '부천', '수원', '성남', '전주', '안양', '청주', '창원', '광명',\n",
        "       '포항', '안산', '진주', '고양', '제주', '목포', '의정부', '익산', '군산', '구미', '천안',\n",
        "       '여수', '춘천', '원주', '평택', '경주', '김해', '순천', '군포', '남양주', '강릉', '충주',\n",
        "       '안동', '경산', '아산', '거제', '김천', '정읍', '용인', '시흥', '파주', '양산', '이천',\n",
        "       '구리', '서산', '제천', '논산','기타 도시', '도시이외']"
      ],
      "metadata": {
        "id": "KroyxxJalduq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for _year in range(2014,2022):\n",
        "  df3 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/data/'+year+'-지역별.csv',  encoding='utf-8')\n",
        "  df3 = df3.drop(['범죄별(1)','범죄별(2)','2014'],axis=1)\n",
        "  df3 = df3.drop([0,1,2]).set_index('범죄별(3)')\n",
        "  df3 = df3.replace('-', '0')\n",
        "  for p in df3.columns:\n",
        "    df3[p] = df3[p].str.replace(',','').astype('int64')\n",
        "  df3.columns = region[1:]\n",
        "\n",
        "  _max = df3.loc[[crime[0]]].values.max()\n",
        "  _min = df3.loc[[crime[0]]].values.min()\n",
        "  my_value = df3.loc[[crime[0]]]\n",
        "  new_df3 = (my_value-_min)/(_max-_min)\n",
        "\n",
        "  for i in range(1,len(crime)):\n",
        "    _max = df3.loc[[crime[i]]].values.max()\n",
        "    _min = df3.loc[[crime[i]]].values.min()\n",
        "    my_value = df3.loc[[crime[i]]]\n",
        "    new_df3 = pd.concat([new_df3,(my_value-_min)/(_max-_min)])\n",
        "\n",
        "  new_df3 = new_df3.astype(float).round(2)\n",
        "  new_df3.to_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(_year)+'-지역별.csv',encoding='utf-8')"
      ],
      "metadata": {
        "id": "t-FmH11OlRe0"
      },
      "execution_count": 487,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 장소별 데이터 병합"
      ],
      "metadata": {
        "id": "GwdRIEVWTjI3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/data/장소별.csv', encoding='utf-8',index_col=0)"
      ],
      "metadata": {
        "id": "Cwl6rYkmWZbh"
      },
      "execution_count": 389,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4['Unnamed: 49']['강도'] = '1'"
      ],
      "metadata": {
        "id": "n1w3kQ8TWsfm"
      },
      "execution_count": 390,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "oySgyhqQbg_A",
        "outputId": "c82b4e99-15d6-4a07-90f7-5bb99202bd29"
      },
      "execution_count": 391,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              2014 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6  \\\n",
              "범죄별(1)                                                                      \n",
              "NaN     아파트ㆍ연립ㆍ다세대       단독주택         노상  숙박업소ㆍ 목욕탕     유흥접객업소        사무실   \n",
              "절도          14,336     19,516     49,530      6,736     13,953      6,254   \n",
              "살인             186        197        185         37         37         22   \n",
              "강도             138        182        423         90        101         33   \n",
              "성폭력          2,584      2,629      4,919      2,604      2,115        655   \n",
              "폭행           7,915      6,869     58,095      1,052     12,158      2,888   \n",
              "\n",
              "       Unnamed: 7        2015 Unnamed: 9 Unnamed: 10 Unnamed: 11 Unnamed: 12  \\\n",
              "범죄별(1)                                                                         \n",
              "NaN            공장  아파트ㆍ연립ㆍ다세대       단독주택          노상   숙박업소ㆍ 목욕탕      유흥접객업소   \n",
              "절도          2,413      11,043     15,566      45,312       5,167      12,370   \n",
              "살인              8         176        190         177          24          42   \n",
              "강도              3          99        143         408         109          85   \n",
              "성폭력            60       2,440      2,683       4,748       2,611       2,097   \n",
              "폭행            426      11,179      9,317      59,353         942      10,930   \n",
              "\n",
              "       Unnamed: 13 Unnamed: 14        2016 Unnamed: 16 Unnamed: 17  \\\n",
              "범죄별(1)                                                               \n",
              "NaN            사무실          공장  아파트ㆍ연립ㆍ다세대        단독주택          노상   \n",
              "절도           5,473       1,717       8,778      13,228      39,363   \n",
              "살인              21           2         162         189         156   \n",
              "강도              32           5          74         114         339   \n",
              "성폭력            712          36       2,597       2,513       3,962   \n",
              "폭행           2,739         460      12,634       9,720      61,708   \n",
              "\n",
              "       Unnamed: 18 Unnamed: 19 Unnamed: 20 Unnamed: 21        2017  \\\n",
              "범죄별(1)                                                               \n",
              "NaN      숙박업소ㆍ 목욕탕      유흥접객업소         사무실          공장  아파트ㆍ연립ㆍ다세대   \n",
              "절도           4,409      11,739       3,954       1,075       7,038   \n",
              "살인              29          49          19           5         182   \n",
              "강도              68          75          31           3          83   \n",
              "성폭력          2,526       2,351         637          62       2,845   \n",
              "폭행             973      11,459       2,791         490      11,949   \n",
              "\n",
              "       Unnamed: 23 Unnamed: 24 Unnamed: 25 Unnamed: 26 Unnamed: 27  \\\n",
              "범죄별(1)                                                               \n",
              "NaN           단독주택          노상   숙박업소ㆍ 목욕탕      유흥접객업소         사무실   \n",
              "절도           9,045      36,808       3,559       9,985       3,096   \n",
              "살인             161         136          34          40          21   \n",
              "강도              87         261          71          62          25   \n",
              "성폭력          2,431       4,448       2,673       2,588         596   \n",
              "폭행           8,380      60,975       1,079      12,419       2,818   \n",
              "\n",
              "       Unnamed: 28        2018 Unnamed: 30 Unnamed: 31 Unnamed: 32  \\\n",
              "범죄별(1)                                                               \n",
              "NaN             공장  아파트ㆍ연립ㆍ다세대        단독주택          노상   숙박업소ㆍ 목욕탕   \n",
              "절도           1,022      11,519      11,544      39,959       6,254   \n",
              "살인               3         214         192         128          28   \n",
              "강도               3          78          85         252          80   \n",
              "성폭력             55       3,679       3,030       3,928       3,397   \n",
              "폭행             456      21,862      14,834      63,415       2,337   \n",
              "\n",
              "       Unnamed: 33 Unnamed: 34 Unnamed: 35        2019 Unnamed: 37  \\\n",
              "범죄별(1)                                                               \n",
              "NaN         유흥접객업소         사무실          공장  아파트ㆍ연립ㆍ다세대        단독주택   \n",
              "절도          15,445       3,576       1,179      14,345      11,450   \n",
              "살인              58          26           5         228         174   \n",
              "강도              58          23           3         106          89   \n",
              "성폭력          4,018         690          46       4,127       2,790   \n",
              "폭행          23,513       3,840         747      25,373      15,367   \n",
              "\n",
              "       Unnamed: 38 Unnamed: 39 Unnamed: 40 Unnamed: 41 Unnamed: 42  \\\n",
              "범죄별(1)                                                               \n",
              "NaN             노상   숙박업소ㆍ 목욕탕      유흥접객업소         사무실          공장   \n",
              "절도          45,543       6,142      15,700       3,241       1,066   \n",
              "살인             145          27          49          14           5   \n",
              "강도             221          79          53          21           1   \n",
              "성폭력          3,909       3,556       4,281         659          62   \n",
              "폭행          60,830       2,236      22,858       3,685         715   \n",
              "\n",
              "              2020 Unnamed: 44 Unnamed: 45 Unnamed: 46 Unnamed: 47  \\\n",
              "범죄별(1)                                                               \n",
              "NaN     아파트ㆍ연립ㆍ다세대        단독주택          노상   숙박업소ㆍ 목욕탕      유흥접객업소   \n",
              "절도          16,703      10,197      46,862       4,326      12,642   \n",
              "살인             211         172         138          17          36   \n",
              "강도              79          54         164          63          62   \n",
              "성폭력          4,391       2,886       3,533       3,414       3,874   \n",
              "폭행          23,483      13,491      53,395       1,840      19,382   \n",
              "\n",
              "       Unnamed: 48 Unnamed: 49        2021 Unnamed: 51 Unnamed: 52  \\\n",
              "범죄별(1)                                                               \n",
              "NaN            사무실          공장  아파트ㆍ연립ㆍ다세대        단독주택          노상   \n",
              "절도           3,330       1,261      16,485       8,535      46,374   \n",
              "살인              24           3         211         124         132   \n",
              "강도              19           1          78          47         114   \n",
              "성폭력            715          51       5,541       3,571       3,378   \n",
              "폭행           3,359         693      23,814      12,770      42,680   \n",
              "\n",
              "       Unnamed: 53 Unnamed: 54 Unnamed: 55 Unnamed: 56  \n",
              "범죄별(1)                                                  \n",
              "NaN      숙박업소ㆍ 목욕탕      유흥접객업소         사무실          공장  \n",
              "절도           2,666       7,116       2,288         970  \n",
              "살인              30          33          16           5  \n",
              "강도              47          27          15           2  \n",
              "성폭력          3,271       2,190         852          70  \n",
              "폭행           1,943       9,558       2,988         665  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-8ce779a5-12e5-4242-bce8-9e80c39e8ab4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2014</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "      <th>Unnamed: 5</th>\n",
              "      <th>Unnamed: 6</th>\n",
              "      <th>Unnamed: 7</th>\n",
              "      <th>2015</th>\n",
              "      <th>Unnamed: 9</th>\n",
              "      <th>Unnamed: 10</th>\n",
              "      <th>Unnamed: 11</th>\n",
              "      <th>Unnamed: 12</th>\n",
              "      <th>Unnamed: 13</th>\n",
              "      <th>Unnamed: 14</th>\n",
              "      <th>2016</th>\n",
              "      <th>Unnamed: 16</th>\n",
              "      <th>Unnamed: 17</th>\n",
              "      <th>Unnamed: 18</th>\n",
              "      <th>Unnamed: 19</th>\n",
              "      <th>Unnamed: 20</th>\n",
              "      <th>Unnamed: 21</th>\n",
              "      <th>2017</th>\n",
              "      <th>Unnamed: 23</th>\n",
              "      <th>Unnamed: 24</th>\n",
              "      <th>Unnamed: 25</th>\n",
              "      <th>Unnamed: 26</th>\n",
              "      <th>Unnamed: 27</th>\n",
              "      <th>Unnamed: 28</th>\n",
              "      <th>2018</th>\n",
              "      <th>Unnamed: 30</th>\n",
              "      <th>Unnamed: 31</th>\n",
              "      <th>Unnamed: 32</th>\n",
              "      <th>Unnamed: 33</th>\n",
              "      <th>Unnamed: 34</th>\n",
              "      <th>Unnamed: 35</th>\n",
              "      <th>2019</th>\n",
              "      <th>Unnamed: 37</th>\n",
              "      <th>Unnamed: 38</th>\n",
              "      <th>Unnamed: 39</th>\n",
              "      <th>Unnamed: 40</th>\n",
              "      <th>Unnamed: 41</th>\n",
              "      <th>Unnamed: 42</th>\n",
              "      <th>2020</th>\n",
              "      <th>Unnamed: 44</th>\n",
              "      <th>Unnamed: 45</th>\n",
              "      <th>Unnamed: 46</th>\n",
              "      <th>Unnamed: 47</th>\n",
              "      <th>Unnamed: 48</th>\n",
              "      <th>Unnamed: 49</th>\n",
              "      <th>2021</th>\n",
              "      <th>Unnamed: 51</th>\n",
              "      <th>Unnamed: 52</th>\n",
              "      <th>Unnamed: 53</th>\n",
              "      <th>Unnamed: 54</th>\n",
              "      <th>Unnamed: 55</th>\n",
              "      <th>Unnamed: 56</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>범죄별(1)</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>아파트ㆍ연립ㆍ다세대</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>노상</td>\n",
              "      <td>숙박업소ㆍ 목욕탕</td>\n",
              "      <td>유흥접객업소</td>\n",
              "      <td>사무실</td>\n",
              "      <td>공장</td>\n",
              "      <td>아파트ㆍ연립ㆍ다세대</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>노상</td>\n",
              "      <td>숙박업소ㆍ 목욕탕</td>\n",
              "      <td>유흥접객업소</td>\n",
              "      <td>사무실</td>\n",
              "      <td>공장</td>\n",
              "      <td>아파트ㆍ연립ㆍ다세대</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>노상</td>\n",
              "      <td>숙박업소ㆍ 목욕탕</td>\n",
              "      <td>유흥접객업소</td>\n",
              "      <td>사무실</td>\n",
              "      <td>공장</td>\n",
              "      <td>아파트ㆍ연립ㆍ다세대</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>노상</td>\n",
              "      <td>숙박업소ㆍ 목욕탕</td>\n",
              "      <td>유흥접객업소</td>\n",
              "      <td>사무실</td>\n",
              "      <td>공장</td>\n",
              "      <td>아파트ㆍ연립ㆍ다세대</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>노상</td>\n",
              "      <td>숙박업소ㆍ 목욕탕</td>\n",
              "      <td>유흥접객업소</td>\n",
              "      <td>사무실</td>\n",
              "      <td>공장</td>\n",
              "      <td>아파트ㆍ연립ㆍ다세대</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>노상</td>\n",
              "      <td>숙박업소ㆍ 목욕탕</td>\n",
              "      <td>유흥접객업소</td>\n",
              "      <td>사무실</td>\n",
              "      <td>공장</td>\n",
              "      <td>아파트ㆍ연립ㆍ다세대</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>노상</td>\n",
              "      <td>숙박업소ㆍ 목욕탕</td>\n",
              "      <td>유흥접객업소</td>\n",
              "      <td>사무실</td>\n",
              "      <td>공장</td>\n",
              "      <td>아파트ㆍ연립ㆍ다세대</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>노상</td>\n",
              "      <td>숙박업소ㆍ 목욕탕</td>\n",
              "      <td>유흥접객업소</td>\n",
              "      <td>사무실</td>\n",
              "      <td>공장</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>절도</th>\n",
              "      <td>14,336</td>\n",
              "      <td>19,516</td>\n",
              "      <td>49,530</td>\n",
              "      <td>6,736</td>\n",
              "      <td>13,953</td>\n",
              "      <td>6,254</td>\n",
              "      <td>2,413</td>\n",
              "      <td>11,043</td>\n",
              "      <td>15,566</td>\n",
              "      <td>45,312</td>\n",
              "      <td>5,167</td>\n",
              "      <td>12,370</td>\n",
              "      <td>5,473</td>\n",
              "      <td>1,717</td>\n",
              "      <td>8,778</td>\n",
              "      <td>13,228</td>\n",
              "      <td>39,363</td>\n",
              "      <td>4,409</td>\n",
              "      <td>11,739</td>\n",
              "      <td>3,954</td>\n",
              "      <td>1,075</td>\n",
              "      <td>7,038</td>\n",
              "      <td>9,045</td>\n",
              "      <td>36,808</td>\n",
              "      <td>3,559</td>\n",
              "      <td>9,985</td>\n",
              "      <td>3,096</td>\n",
              "      <td>1,022</td>\n",
              "      <td>11,519</td>\n",
              "      <td>11,544</td>\n",
              "      <td>39,959</td>\n",
              "      <td>6,254</td>\n",
              "      <td>15,445</td>\n",
              "      <td>3,576</td>\n",
              "      <td>1,179</td>\n",
              "      <td>14,345</td>\n",
              "      <td>11,450</td>\n",
              "      <td>45,543</td>\n",
              "      <td>6,142</td>\n",
              "      <td>15,700</td>\n",
              "      <td>3,241</td>\n",
              "      <td>1,066</td>\n",
              "      <td>16,703</td>\n",
              "      <td>10,197</td>\n",
              "      <td>46,862</td>\n",
              "      <td>4,326</td>\n",
              "      <td>12,642</td>\n",
              "      <td>3,330</td>\n",
              "      <td>1,261</td>\n",
              "      <td>16,485</td>\n",
              "      <td>8,535</td>\n",
              "      <td>46,374</td>\n",
              "      <td>2,666</td>\n",
              "      <td>7,116</td>\n",
              "      <td>2,288</td>\n",
              "      <td>970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>살인</th>\n",
              "      <td>186</td>\n",
              "      <td>197</td>\n",
              "      <td>185</td>\n",
              "      <td>37</td>\n",
              "      <td>37</td>\n",
              "      <td>22</td>\n",
              "      <td>8</td>\n",
              "      <td>176</td>\n",
              "      <td>190</td>\n",
              "      <td>177</td>\n",
              "      <td>24</td>\n",
              "      <td>42</td>\n",
              "      <td>21</td>\n",
              "      <td>2</td>\n",
              "      <td>162</td>\n",
              "      <td>189</td>\n",
              "      <td>156</td>\n",
              "      <td>29</td>\n",
              "      <td>49</td>\n",
              "      <td>19</td>\n",
              "      <td>5</td>\n",
              "      <td>182</td>\n",
              "      <td>161</td>\n",
              "      <td>136</td>\n",
              "      <td>34</td>\n",
              "      <td>40</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>214</td>\n",
              "      <td>192</td>\n",
              "      <td>128</td>\n",
              "      <td>28</td>\n",
              "      <td>58</td>\n",
              "      <td>26</td>\n",
              "      <td>5</td>\n",
              "      <td>228</td>\n",
              "      <td>174</td>\n",
              "      <td>145</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>211</td>\n",
              "      <td>172</td>\n",
              "      <td>138</td>\n",
              "      <td>17</td>\n",
              "      <td>36</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>211</td>\n",
              "      <td>124</td>\n",
              "      <td>132</td>\n",
              "      <td>30</td>\n",
              "      <td>33</td>\n",
              "      <td>16</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>강도</th>\n",
              "      <td>138</td>\n",
              "      <td>182</td>\n",
              "      <td>423</td>\n",
              "      <td>90</td>\n",
              "      <td>101</td>\n",
              "      <td>33</td>\n",
              "      <td>3</td>\n",
              "      <td>99</td>\n",
              "      <td>143</td>\n",
              "      <td>408</td>\n",
              "      <td>109</td>\n",
              "      <td>85</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>74</td>\n",
              "      <td>114</td>\n",
              "      <td>339</td>\n",
              "      <td>68</td>\n",
              "      <td>75</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>83</td>\n",
              "      <td>87</td>\n",
              "      <td>261</td>\n",
              "      <td>71</td>\n",
              "      <td>62</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>78</td>\n",
              "      <td>85</td>\n",
              "      <td>252</td>\n",
              "      <td>80</td>\n",
              "      <td>58</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "      <td>106</td>\n",
              "      <td>89</td>\n",
              "      <td>221</td>\n",
              "      <td>79</td>\n",
              "      <td>53</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>79</td>\n",
              "      <td>54</td>\n",
              "      <td>164</td>\n",
              "      <td>63</td>\n",
              "      <td>62</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>78</td>\n",
              "      <td>47</td>\n",
              "      <td>114</td>\n",
              "      <td>47</td>\n",
              "      <td>27</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>성폭력</th>\n",
              "      <td>2,584</td>\n",
              "      <td>2,629</td>\n",
              "      <td>4,919</td>\n",
              "      <td>2,604</td>\n",
              "      <td>2,115</td>\n",
              "      <td>655</td>\n",
              "      <td>60</td>\n",
              "      <td>2,440</td>\n",
              "      <td>2,683</td>\n",
              "      <td>4,748</td>\n",
              "      <td>2,611</td>\n",
              "      <td>2,097</td>\n",
              "      <td>712</td>\n",
              "      <td>36</td>\n",
              "      <td>2,597</td>\n",
              "      <td>2,513</td>\n",
              "      <td>3,962</td>\n",
              "      <td>2,526</td>\n",
              "      <td>2,351</td>\n",
              "      <td>637</td>\n",
              "      <td>62</td>\n",
              "      <td>2,845</td>\n",
              "      <td>2,431</td>\n",
              "      <td>4,448</td>\n",
              "      <td>2,673</td>\n",
              "      <td>2,588</td>\n",
              "      <td>596</td>\n",
              "      <td>55</td>\n",
              "      <td>3,679</td>\n",
              "      <td>3,030</td>\n",
              "      <td>3,928</td>\n",
              "      <td>3,397</td>\n",
              "      <td>4,018</td>\n",
              "      <td>690</td>\n",
              "      <td>46</td>\n",
              "      <td>4,127</td>\n",
              "      <td>2,790</td>\n",
              "      <td>3,909</td>\n",
              "      <td>3,556</td>\n",
              "      <td>4,281</td>\n",
              "      <td>659</td>\n",
              "      <td>62</td>\n",
              "      <td>4,391</td>\n",
              "      <td>2,886</td>\n",
              "      <td>3,533</td>\n",
              "      <td>3,414</td>\n",
              "      <td>3,874</td>\n",
              "      <td>715</td>\n",
              "      <td>51</td>\n",
              "      <td>5,541</td>\n",
              "      <td>3,571</td>\n",
              "      <td>3,378</td>\n",
              "      <td>3,271</td>\n",
              "      <td>2,190</td>\n",
              "      <td>852</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>폭행</th>\n",
              "      <td>7,915</td>\n",
              "      <td>6,869</td>\n",
              "      <td>58,095</td>\n",
              "      <td>1,052</td>\n",
              "      <td>12,158</td>\n",
              "      <td>2,888</td>\n",
              "      <td>426</td>\n",
              "      <td>11,179</td>\n",
              "      <td>9,317</td>\n",
              "      <td>59,353</td>\n",
              "      <td>942</td>\n",
              "      <td>10,930</td>\n",
              "      <td>2,739</td>\n",
              "      <td>460</td>\n",
              "      <td>12,634</td>\n",
              "      <td>9,720</td>\n",
              "      <td>61,708</td>\n",
              "      <td>973</td>\n",
              "      <td>11,459</td>\n",
              "      <td>2,791</td>\n",
              "      <td>490</td>\n",
              "      <td>11,949</td>\n",
              "      <td>8,380</td>\n",
              "      <td>60,975</td>\n",
              "      <td>1,079</td>\n",
              "      <td>12,419</td>\n",
              "      <td>2,818</td>\n",
              "      <td>456</td>\n",
              "      <td>21,862</td>\n",
              "      <td>14,834</td>\n",
              "      <td>63,415</td>\n",
              "      <td>2,337</td>\n",
              "      <td>23,513</td>\n",
              "      <td>3,840</td>\n",
              "      <td>747</td>\n",
              "      <td>25,373</td>\n",
              "      <td>15,367</td>\n",
              "      <td>60,830</td>\n",
              "      <td>2,236</td>\n",
              "      <td>22,858</td>\n",
              "      <td>3,685</td>\n",
              "      <td>715</td>\n",
              "      <td>23,483</td>\n",
              "      <td>13,491</td>\n",
              "      <td>53,395</td>\n",
              "      <td>1,840</td>\n",
              "      <td>19,382</td>\n",
              "      <td>3,359</td>\n",
              "      <td>693</td>\n",
              "      <td>23,814</td>\n",
              "      <td>12,770</td>\n",
              "      <td>42,680</td>\n",
              "      <td>1,943</td>\n",
              "      <td>9,558</td>\n",
              "      <td>2,988</td>\n",
              "      <td>665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ce779a5-12e5-4242-bce8-9e80c39e8ab4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-9f37f2e9-6122-41c3-8b17-d5b87df3576a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f37f2e9-6122-41c3-8b17-d5b87df3576a')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-9f37f2e9-6122-41c3-8b17-d5b87df3576a button.colab-df-quickchart');\n",
              "  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",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8ce779a5-12e5-4242-bce8-9e80c39e8ab4 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-8ce779a5-12e5-4242-bce8-9e80c39e8ab4');\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": 391
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "place = ['아파트ㆍ연립ㆍ다세대',\t'단독주택',\t'노상',\t'숙박업소ㆍ 목욕탕',\t'유흥접객업소',\t'사무실',\t'공장']"
      ],
      "metadata": {
        "id": "raE1_Fh-dJsu"
      },
      "execution_count": 392,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = 2\n",
        "year = 2014"
      ],
      "metadata": {
        "id": "h-ToR3bjWvjb"
      },
      "execution_count": 393,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4 = df4[[str(year),'Unnamed: '+str(index),'Unnamed: '+str(index+1),'Unnamed: '+str(index+2),'Unnamed: '+str(index+3),'Unnamed: '+str(index+4),'Unnamed: '+str(index+5)]]\n",
        "df4.columns = df4.iloc[0].values\n",
        "df4 = df4.drop(df4.index[0],axis=0)"
      ],
      "metadata": {
        "id": "9KvKqgN8WZ0h"
      },
      "execution_count": 394,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for p in range(len(place)):\n",
        "  df4[place[p]] = df4[place[p]].str.replace(',','').astype('int64')"
      ],
      "metadata": {
        "id": "Cftn7ghfdpxj"
      },
      "execution_count": 395,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "V_JcjvhiePFl",
        "outputId": "cca4953c-21b0-4b01-a166-b78d2d389499"
      },
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        아파트ㆍ연립ㆍ다세대   단독주택     노상  숙박업소ㆍ 목욕탕  유흥접객업소   사무실    공장\n",
              "범죄별(1)                                                         \n",
              "절도           14336  19516  49530       6736   13953  6254  2413\n",
              "살인             186    197    185         37      37    22     8\n",
              "강도             138    182    423         90     101    33     3\n",
              "성폭력           2584   2629   4919       2604    2115   655    60\n",
              "폭행            7915   6869  58095       1052   12158  2888   426"
            ],
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              "      <th>아파트ㆍ연립ㆍ다세대</th>\n",
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              "      <td>90</td>\n",
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              "      <td>3</td>\n",
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              "      <td>2584</td>\n",
              "      <td>2629</td>\n",
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              "      <td>2604</td>\n",
              "      <td>2115</td>\n",
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              "      <td>60</td>\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-667ac250-bcfa-49ea-9146-6a6b0f9abd54 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-667ac250-bcfa-49ea-9146-6a6b0f9abd54');\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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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 396
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_max = df4.loc[[crime[0]]].values.max()\n",
        "_min = df4.loc[[crime[0]]].values.min()\n",
        "my_value = df4.loc[[crime[0]]]\n",
        "new_df4 = (my_value-_min)/(_max-_min)\n",
        "\n",
        "for i in range(1,len(crime)):\n",
        "  _max = df4.loc[[crime[i]]].values.max()\n",
        "  _min = df4.loc[[crime[i]]].values.min()\n",
        "  my_value = df4.loc[[crime[i]]]\n",
        "  new_df4 = pd.concat([new_df4,(my_value-_min)/(_max-_min)])\n",
        "\n",
        "new_df4 = new_df4.astype(float).round(2)"
      ],
      "metadata": {
        "id": "Q3KEb7wXXXRX"
      },
      "execution_count": 397,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "UCw5G8lJXNkS",
        "outputId": "1442ddb9-acbb-4ec5-dc65-11f274067ff0"
      },
      "execution_count": 398,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        아파트ㆍ연립ㆍ다세대  단독주택   노상  숙박업소ㆍ 목욕탕  유흥접객업소  사무실   공장\n",
              "범죄별(1)                                                    \n",
              "절도            0.25  0.36 1.00       0.09    0.24 0.08 0.00\n",
              "살인            0.94  1.00 0.94       0.15    0.15 0.07 0.00\n",
              "강도            0.32  0.43 1.00       0.21    0.23 0.07 0.00\n",
              "성폭력           0.52  0.53 1.00       0.52    0.42 0.12 0.00\n",
              "폭행            0.13  0.11 1.00       0.01    0.20 0.04 0.00"
            ],
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              "      <th>아파트ㆍ연립ㆍ다세대</th>\n",
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              "      <th>절도</th>\n",
              "      <td>0.25</td>\n",
              "      <td>0.36</td>\n",
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              "      <td>0.24</td>\n",
              "      <td>0.08</td>\n",
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              "    <tr>\n",
              "      <th>살인</th>\n",
              "      <td>0.94</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>강도</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.21</td>\n",
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              "      <td>0.42</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ff5e8bc-67ab-4db7-850d-f4f0c08190c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-5c11b054-8401-481a-b9d5-6853e0a68f83\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5c11b054-8401-481a-b9d5-6853e0a68f83')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5c11b054-8401-481a-b9d5-6853e0a68f83 button.colab-df-quickchart');\n",
              "  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-3ff5e8bc-67ab-4db7-850d-f4f0c08190c2 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-3ff5e8bc-67ab-4db7-850d-f4f0c08190c2');\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": 398
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 장소별 데이터 병합 (2014~2021)"
      ],
      "metadata": {
        "id": "5Wyglesneuim"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "place = ['아파트ㆍ연립ㆍ다세대',\t'단독주택',\t'노상',\t'숙박업소ㆍ 목욕탕',\t'유흥접객업소',\t'사무실',\t'공장']\n",
        "index = 2\n",
        "year = 2014\n",
        "\n",
        "for _year in range(2014,2022):\n",
        "  df4 = pd.read_csv('/content/drive/MyDrive/산업인공지능 대회/data/장소별.csv', encoding='utf-8',index_col=0)\n",
        "  df4['Unnamed: 49']['강도'] = '1'\n",
        "  df4 = df4[[str(year),'Unnamed: '+str(index),'Unnamed: '+str(index+1),'Unnamed: '+str(index+2),'Unnamed: '+str(index+3),'Unnamed: '+str(index+4),'Unnamed: '+str(index+5)]]\n",
        "  df4.columns = df4.iloc[0].values\n",
        "  df4 = df4.drop(df4.index[0],axis=0)\n",
        "\n",
        "  for p in range(len(place)):\n",
        "    df4[place[p]] = df4[place[p]].str.replace(',','').astype('int64')\n",
        "\n",
        "  _max = df4.loc[[crime[0]]].values.max()\n",
        "  _min = df4.loc[[crime[0]]].values.min()\n",
        "  my_value = df4.loc[[crime[0]]]\n",
        "  new_df4 = (my_value-_min)/(_max-_min)\n",
        "\n",
        "  for i in range(1,len(crime)):\n",
        "    _max = df4.loc[[crime[i]]].values.max()\n",
        "    _min = df4.loc[[crime[i]]].values.min()\n",
        "    my_value = df4.loc[[crime[i]]]\n",
        "    new_df4 = pd.concat([new_df4,(my_value-_min)/(_max-_min)])\n",
        "\n",
        "  new_df4 = new_df4.astype(float).round(2)\n",
        "  new_df4.to_csv('/content/drive/MyDrive/산업인공지능 대회/preprocessed_data/'+str(_year)+'-장소별.csv',encoding='utf-8')\n",
        "  year+=1\n",
        "  index+=7"
      ],
      "metadata": {
        "id": "g18JyV6Jewy4"
      },
      "execution_count": 400,
      "outputs": []
    }
  ]
}