연관분석.ipynb

{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mw7FD05botAQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "import csv\n",
        "import codecs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Qhz5qHUbjZo"
      },
      "outputs": [],
      "source": [
        "filename = \"/Users/user/Desktop/outlier/Gwangjingu_parking_total.csv\"\n",
        "\n",
        "with codecs.open(filename, 'r', 'euc_kr', errors='ignore') as f:\n",
        "    reader = csv.reader(f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXdglFRLXbkB"
      },
      "source": [
        "Apriori 알고리즘 -> 실패 ^^(원인 : 지표로서 사용하기 좋지 않음)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "A6zYbbUspJmv",
        "outputId": "50ae68fe-9135-4cff-9efd-6c683d9958f5"
      },
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>단속일시</th>\n",
              "      <th>단속동</th>\n",
              "      <th>단속장소</th>\n",
              "      <th>위반내용</th>\n",
              "      <th>요일</th>\n",
              "      <th>시간대</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-01 06:44</td>\n",
              "      <td>화양동</td>\n",
              "      <td>119-4</td>\n",
              "      <td>주정차금지</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-01 07:01</td>\n",
              "      <td>화양동</td>\n",
              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-01 07:24</td>\n",
              "      <td>구의동</td>\n",
              "      <td>동서울터미널 주차장앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>2015-01-01 08:00</td>\n",
              "      <td>화양동</td>\n",
              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
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              "      <th>4</th>\n",
              "      <td>2015-01-01 08:15</td>\n",
              "      <td>화양동</td>\n",
              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지 34조위반</td>\n",
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              "    <tr>\n",
              "      <th>490926</th>\n",
              "      <td>2023-02-28 20:43:00</td>\n",
              "      <td>군자동</td>\n",
              "      <td>군자빌딩</td>\n",
              "      <td>주정차금지(황색실선)구역</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490927</th>\n",
              "      <td>2023-02-28 21:23:00</td>\n",
              "      <td>자양동</td>\n",
              "      <td>서울 광진구 자양동 659-32</td>\n",
              "      <td>보도</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490928</th>\n",
              "      <td>2023-02-28 21:38:00</td>\n",
              "      <td>화양동</td>\n",
              "      <td>서울 광진구 능동로 154</td>\n",
              "      <td>보도</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490929</th>\n",
              "      <td>2023-02-28 22:36:00</td>\n",
              "      <td>자양동</td>\n",
              "      <td>서울 광진구 자양동 12-10</td>\n",
              "      <td>교차로</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
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              "    <tr>\n",
              "      <th>490930</th>\n",
              "      <td>2023-02-28 23:19:00</td>\n",
              "      <td>자양동</td>\n",
              "      <td>02-02</td>\n",
              "      <td>횡단보도</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
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              "<p>490931 rows × 6 columns</p>\n",
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              "                       단속일시  단속동               단속장소           위반내용  요일 시간대\n",
              "0          2015-01-01 06:44  화양동              119-4          주정차금지   3   3\n",
              "1          2015-01-01 07:01  화양동          건대입구1번출구앞    32조내지 34조위반   3   3\n",
              "2          2015-01-01 07:24  구의동        동서울터미널 주차장앞    32조내지 34조위반   3   3\n",
              "3          2015-01-01 08:00  화양동          건대입구1번출구앞    32조내지 34조위반   3   4\n",
              "4          2015-01-01 08:15  화양동          건대입구1번출구앞    32조내지 34조위반   3   4\n",
              "...                     ...  ...                ...            ...  ..  ..\n",
              "490926  2023-02-28 20:43:00  군자동               군자빌딩  주정차금지(황색실선)구역   1  10\n",
              "490927  2023-02-28 21:23:00  자양동  서울 광진구 자양동 659-32             보도   1  10\n",
              "490928  2023-02-28 21:38:00  화양동     서울 광진구 능동로 154             보도   1  10\n",
              "490929  2023-02-28 22:36:00  자양동   서울 광진구 자양동 12-10            교차로   1  11\n",
              "490930  2023-02-28 23:19:00  자양동              02-02           횡단보도   1  11\n",
              "\n",
              "[490931 rows x 6 columns]"
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      "source": [
        "# 데이터 불러오기\n",
        "df = pd.read_csv(filename, encoding='cp949', usecols=['단속일시', '단속동', '단속장소', '위반내용'])\n",
        "\n",
        "# 단속일시를 이용하여 요일 구하기\n",
        "df['요일'] = pd.to_datetime(df['단속일시']).dt.dayofweek\n",
        "# 시간대 구하기\n",
        "df['시간대'] = pd.cut(pd.to_datetime(df['단속일시']).dt.hour, bins=12, labels=range(12))\n",
        "\n",
        "df"
      ]
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(453963, 6)\n"
          ]
        },
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              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지34조위반</td>\n",
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              "      <td>2015-01-01 07:24</td>\n",
              "      <td>구의동</td>\n",
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              "      <td>32조내지34조위반</td>\n",
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              "      <td>2015-01-01 08:00</td>\n",
              "      <td>화양동</td>\n",
              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지34조위반</td>\n",
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              "      <td>화양동</td>\n",
              "      <td>건대입구1번출구앞</td>\n",
              "      <td>32조내지34조위반</td>\n",
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              "      <th>490926</th>\n",
              "      <td>2023-02-28 20:43:00</td>\n",
              "      <td>군자동</td>\n",
              "      <td>군자빌딩</td>\n",
              "      <td>주정차금지(황색실선)구역</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490927</th>\n",
              "      <td>2023-02-28 21:23:00</td>\n",
              "      <td>자양동</td>\n",
              "      <td>서울광진구자양동659-32</td>\n",
              "      <td>보도</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490928</th>\n",
              "      <td>2023-02-28 21:38:00</td>\n",
              "      <td>화양동</td>\n",
              "      <td>서울광진구능동로154</td>\n",
              "      <td>보도</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>490929</th>\n",
              "      <td>2023-02-28 22:36:00</td>\n",
              "      <td>자양동</td>\n",
              "      <td>서울광진구자양동12-10</td>\n",
              "      <td>교차로</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
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              "      <th>490930</th>\n",
              "      <td>2023-02-28 23:19:00</td>\n",
              "      <td>자양동</td>\n",
              "      <td>02-02</td>\n",
              "      <td>횡단보도</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
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              "                       단속일시  단속동            단속장소           위반내용  요일 시간대\n",
              "0          2015-01-01 06:44  화양동           119-4          주정차금지   3   3\n",
              "1          2015-01-01 07:01  화양동       건대입구1번출구앞     32조내지34조위반   3   3\n",
              "2          2015-01-01 07:24  구의동      동서울터미널주차장앞     32조내지34조위반   3   3\n",
              "3          2015-01-01 08:00  화양동       건대입구1번출구앞     32조내지34조위반   3   4\n",
              "4          2015-01-01 08:15  화양동       건대입구1번출구앞     32조내지34조위반   3   4\n",
              "...                     ...  ...             ...            ...  ..  ..\n",
              "490926  2023-02-28 20:43:00  군자동            군자빌딩  주정차금지(황색실선)구역   1  10\n",
              "490927  2023-02-28 21:23:00  자양동  서울광진구자양동659-32             보도   1  10\n",
              "490928  2023-02-28 21:38:00  화양동     서울광진구능동로154             보도   1  10\n",
              "490929  2023-02-28 22:36:00  자양동   서울광진구자양동12-10            교차로   1  11\n",
              "490930  2023-02-28 23:19:00  자양동           02-02           횡단보도   1  11\n",
              "\n",
              "[453963 rows x 6 columns]"
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          },
          "execution_count": 37,
          "metadata": {},
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        }
      ],
      "source": [
        "##### 데이터 전처리 #####\n",
        "# 중복 데이터 제거\n",
        "df.drop_duplicates(inplace=True)\n",
        "print(df.shape)\n",
        "\n",
        "# 단속장소 열을 문자열로 변환\n",
        "df['단속장소'] = df['단속장소'].astype(str)\n",
        "\n",
        "# 단속장소와 위반내용 컬럼에서 공백 제거\n",
        "df['단속장소'] = df['단속장소'].str.replace(' ', '')\n",
        "df['위반내용'] = df['위반내용'].str.replace(' ', '')\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RRd4dEXoXbkE"
      },
      "outputs": [],
      "source": [
        "# 요일과 시간대를 카테고리화\n",
        "df['요일'] = df['요일'].astype('category')\n",
        "df['시간대'] = df['시간대'].astype('category')"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "T_4qG_4fXbkE",
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              "      <td>0.000826</td>\n",
              "      <td>0.001038</td>\n",
              "      <td>0.001115</td>\n",
              "      <td>0.001185</td>\n",
              "      <td>0.002000</td>\n",
              "      <td>0.002518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000359</td>\n",
              "      <td>0.000289</td>\n",
              "      <td>0.000408</td>\n",
              "      <td>0.000315</td>\n",
              "      <td>0.000482</td>\n",
              "      <td>0.000485</td>\n",
              "      <td>0.000498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.008838</td>\n",
              "      <td>0.008289</td>\n",
              "      <td>0.008265</td>\n",
              "      <td>0.008598</td>\n",
              "      <td>0.008474</td>\n",
              "      <td>0.005395</td>\n",
              "      <td>0.005113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.017378</td>\n",
              "      <td>0.016369</td>\n",
              "      <td>0.016197</td>\n",
              "      <td>0.016794</td>\n",
              "      <td>0.017634</td>\n",
              "      <td>0.004408</td>\n",
              "      <td>0.004791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.016849</td>\n",
              "      <td>0.016056</td>\n",
              "      <td>0.015770</td>\n",
              "      <td>0.016673</td>\n",
              "      <td>0.016385</td>\n",
              "      <td>0.013768</td>\n",
              "      <td>0.013717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.014466</td>\n",
              "      <td>0.015404</td>\n",
              "      <td>0.015387</td>\n",
              "      <td>0.015144</td>\n",
              "      <td>0.015682</td>\n",
              "      <td>0.009177</td>\n",
              "      <td>0.010377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.024782</td>\n",
              "      <td>0.023819</td>\n",
              "      <td>0.021568</td>\n",
              "      <td>0.023991</td>\n",
              "      <td>0.022251</td>\n",
              "      <td>0.017211</td>\n",
              "      <td>0.022167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.020872</td>\n",
              "      <td>0.020021</td>\n",
              "      <td>0.019977</td>\n",
              "      <td>0.020383</td>\n",
              "      <td>0.020955</td>\n",
              "      <td>0.013928</td>\n",
              "      <td>0.015911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.020927</td>\n",
              "      <td>0.021418</td>\n",
              "      <td>0.021209</td>\n",
              "      <td>0.021841</td>\n",
              "      <td>0.022793</td>\n",
              "      <td>0.010415</td>\n",
              "      <td>0.010305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.015230</td>\n",
              "      <td>0.016012</td>\n",
              "      <td>0.016169</td>\n",
              "      <td>0.016431</td>\n",
              "      <td>0.018750</td>\n",
              "      <td>0.017294</td>\n",
              "      <td>0.015182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.008157</td>\n",
              "      <td>0.009485</td>\n",
              "      <td>0.009362</td>\n",
              "      <td>0.009186</td>\n",
              "      <td>0.012140</td>\n",
              "      <td>0.012959</td>\n",
              "      <td>0.009040</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "요일          0         1         2         3         4         5         6\n",
              "시간대                                                                      \n",
              "0    0.004060  0.003110  0.004047  0.004119  0.003978  0.007309  0.007549\n",
              "1    0.000771  0.000826  0.001038  0.001115  0.001185  0.002000  0.002518\n",
              "2    0.000359  0.000289  0.000408  0.000315  0.000482  0.000485  0.000498\n",
              "3    0.008838  0.008289  0.008265  0.008598  0.008474  0.005395  0.005113\n",
              "4    0.017378  0.016369  0.016197  0.016794  0.017634  0.004408  0.004791\n",
              "5    0.016849  0.016056  0.015770  0.016673  0.016385  0.013768  0.013717\n",
              "6    0.014466  0.015404  0.015387  0.015144  0.015682  0.009177  0.010377\n",
              "7    0.024782  0.023819  0.021568  0.023991  0.022251  0.017211  0.022167\n",
              "8    0.020872  0.020021  0.019977  0.020383  0.020955  0.013928  0.015911\n",
              "9    0.020927  0.021418  0.021209  0.021841  0.022793  0.010415  0.010305\n",
              "10   0.015230  0.016012  0.016169  0.016431  0.018750  0.017294  0.015182\n",
              "11   0.008157  0.009485  0.009362  0.009186  0.012140  0.012959  0.009040"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 시간대별 요일별 빈도수 계산\n",
        "df['시간대'] = df['시간대'].astype(int)\n",
        "df['요일'] = df['요일'].astype(int)\n",
        "freq_table = pd.crosstab(df['시간대'], df['요일'], normalize = True)\n",
        "freq_table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZHOpI_jtXbkF",
        "outputId": "eade5cad-3c30-4828-8a4e-3e88ec75765b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mlxtend\\frequent_patterns\\fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "ename": "ValueError",
          "evalue": "The allowed values for a DataFrame are True, False, 0, 1. Found value 0.004059802230578263",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[1;32mc:\\Users\\user\\Desktop\\outlier\\[CSV]광진구 주정차 단속 현황 - 복사본.ipynb Cell 8\u001b[0m in \u001b[0;36m3\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/outlier/%5BCSV%5D%EA%B4%91%EC%A7%84%EA%B5%AC%20%EC%A3%BC%EC%A0%95%EC%B0%A8%20%EB%8B%A8%EC%86%8D%20%ED%98%84%ED%99%A9%20-%20%EB%B3%B5%EC%82%AC%EB%B3%B8.ipynb#X31sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Apriori 분석 수행\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/outlier/%5BCSV%5D%EA%B4%91%EC%A7%84%EA%B5%AC%20%EC%A3%BC%EC%A0%95%EC%B0%A8%20%EB%8B%A8%EC%86%8D%20%ED%98%84%ED%99%A9%20-%20%EB%B3%B5%EC%82%AC%EB%B3%B8.ipynb#X31sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m freq_table\u001b[39m.\u001b[39mcolumns \u001b[39m=\u001b[39m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(freq_table\u001b[39m.\u001b[39mcolumns))\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/outlier/%5BCSV%5D%EA%B4%91%EC%A7%84%EA%B5%AC%20%EC%A3%BC%EC%A0%95%EC%B0%A8%20%EB%8B%A8%EC%86%8D%20%ED%98%84%ED%99%A9%20-%20%EB%B3%B5%EC%82%AC%EB%B3%B8.ipynb#X31sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m frequent_itemsets \u001b[39m=\u001b[39m apriori(freq_table, min_support\u001b[39m=\u001b[39;49m\u001b[39m0.1\u001b[39;49m, use_colnames\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/outlier/%5BCSV%5D%EA%B4%91%EC%A7%84%EA%B5%AC%20%EC%A3%BC%EC%A0%95%EC%B0%A8%20%EB%8B%A8%EC%86%8D%20%ED%98%84%ED%99%A9%20-%20%EB%B3%B5%EC%82%AC%EB%B3%B8.ipynb#X31sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m rules \u001b[39m=\u001b[39m association_rules(frequent_itemsets, metric\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mconfidence\u001b[39m\u001b[39m'\u001b[39m, min_threshold\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m)\n",
            "File \u001b[1;32mc:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mlxtend\\frequent_patterns\\apriori.py:241\u001b[0m, in \u001b[0;36mapriori\u001b[1;34m(df, min_support, use_colnames, max_len, verbose, low_memory)\u001b[0m\n\u001b[0;32m    234\u001b[0m \u001b[39mif\u001b[39;00m min_support \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m \u001b[39m0.0\u001b[39m:\n\u001b[0;32m    235\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    236\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m`min_support` must be a positive \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    237\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mnumber within the interval `(0, 1]`. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    238\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mGot \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m min_support\n\u001b[0;32m    239\u001b[0m     )\n\u001b[1;32m--> 241\u001b[0m fpc\u001b[39m.\u001b[39;49mvalid_input_check(df)\n\u001b[0;32m    243\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(df, \u001b[39m\"\u001b[39m\u001b[39msparse\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m    244\u001b[0m     \u001b[39m# DataFrame with SparseArray (pandas >= 0.24)\u001b[39;00m\n\u001b[0;32m    245\u001b[0m     \u001b[39mif\u001b[39;00m df\u001b[39m.\u001b[39msize \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n",
            "File \u001b[1;32mc:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mlxtend\\frequent_patterns\\fpcommon.py:132\u001b[0m, in \u001b[0;36mvalid_input_check\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m    127\u001b[0m val \u001b[39m=\u001b[39m values[\u001b[39mtuple\u001b[39m(loc[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m loc \u001b[39min\u001b[39;00m idxs)]\n\u001b[0;32m    128\u001b[0m s \u001b[39m=\u001b[39m (\n\u001b[0;32m    129\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mThe allowed values for a DataFrame\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    130\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m are True, False, 0, 1. Found value \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m (val)\n\u001b[0;32m    131\u001b[0m )\n\u001b[1;32m--> 132\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(s)\n",
            "\u001b[1;31mValueError\u001b[0m: The allowed values for a DataFrame are True, False, 0, 1. Found value 0.004059802230578263"
          ]
        }
      ],
      "source": [
        "# Apriori 분석 수행\n",
        "freq_table.columns = range(len(freq_table.columns))\n",
        "frequent_itemsets = apriori(freq_table, min_support=0.1, use_colnames=True)\n",
        "rules = association_rules(frequent_itemsets, metric='confidence', min_threshold=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4a7NqEwgXbkF"
      },
      "outputs": [],
      "source": [
        "print(freq_table)\n",
        "print(frequent_itemsets)\n",
        "print(rules)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_YTDpJ8oXbkG"
      },
      "outputs": [],
      "source": [
        "# Apriori 알고리즘 적용 :  위반내용\n",
        "def apriori_generator(df):\n",
        "    for time_name, time_data in df.groupby('시간대'):\n",
        "        for day_name, day_data in time_data.groupby('요일'):\n",
        "            transactions = day_data['위반내용'].apply(lambda x: x.split(',')).tolist()\n",
        "            te = TransactionEncoder()\n",
        "            te_ary = te.fit_transform(transactions)\n",
        "            yield apriori(pd.DataFrame(te_ary, columns=te.columns_), min_support=0.001, use_colnames=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w93lxbFEXbkG",
        "outputId": "3de8ac61-6082-44c7-ecf7-5275ebd791ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     support         itemsets\n",
            "3   0.255562     (도로교통법 제32조)\n",
            "19  0.158980   (주차금지(황색점선)구역)\n",
            "15  0.141074          (주정차금지)\n",
            "20  0.129137         (주차방법위반)\n",
            "1   0.071622         (교통소통장애)\n",
            "7   0.037439             (보도)\n",
            "8   0.036354           (보도주차)\n",
            "2   0.033641         (도로 모퉁이)\n",
            "16  0.033641  (주정차금지(황색실선)구역)\n",
            "21  0.023874           (횡단보도)\n",
            "     support        itemsets\n",
            "17  0.184136  (주차금지(황색점선)구역)\n",
            "3   0.175637    (도로교통법 제32조)\n",
            "13  0.136686         (주정차금지)\n",
            "18  0.129603        (주차방법위반)\n",
            "1   0.073654        (교통소통장애)\n",
            "7   0.072238            (보도)\n",
            "19  0.065864          (횡단보도)\n",
            "2   0.043201        (도로 모퉁이)\n",
            "8   0.034703          (보도주차)\n",
            "5   0.019830         (버스정류소)\n",
            "     support         itemsets\n",
            "3   0.182907     (도로교통법 제32조)\n",
            "14  0.178008          (주정차금지)\n",
            "18  0.146434   (주차금지(황색점선)구역)\n",
            "19  0.132281         (주차방법위반)\n",
            "1   0.071312         (교통소통장애)\n",
            "7   0.064780             (보도)\n",
            "22  0.046271           (횡단보도)\n",
            "2   0.039194         (도로 모퉁이)\n",
            "8   0.038106           (보도주차)\n",
            "15  0.023952  (주정차금지(황색실선)구역)\n",
            "     support         itemsets\n",
            "3   0.214973     (도로교통법 제32조)\n",
            "17  0.187166          (주정차금지)\n",
            "21  0.172193   (주차금지(황색점선)구역)\n",
            "22  0.109626         (주차방법위반)\n",
            "1   0.060428         (교통소통장애)\n",
            "7   0.054545             (보도)\n",
            "23  0.049733           (횡단보도)\n",
            "8   0.041176           (보도주차)\n",
            "2   0.028877         (도로 모퉁이)\n",
            "18  0.019786  (주정차금지(황색실선)구역)\n",
            "     support         itemsets\n",
            "4   0.207641     (도로교통법 제32조)\n",
            "18  0.161130   (주차금지(황색점선)구역)\n",
            "14  0.153378          (주정차금지)\n",
            "19  0.133998         (주차방법위반)\n",
            "2   0.075305         (교통소통장애)\n",
            "8   0.054817             (보도)\n",
            "9   0.045404           (보도주차)\n",
            "15  0.043189  (주정차금지(황색실선)구역)\n",
            "20  0.035991           (횡단보도)\n",
            "3   0.030454         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "18  0.212176   (주차금지(황색점선)구역)\n",
            "15  0.179024          (주정차금지)\n",
            "3   0.156118     (도로교통법 제32조)\n",
            "19  0.127486         (주차방법위반)\n",
            "1   0.084690         (교통소통장애)\n",
            "7   0.053345             (보도)\n",
            "20  0.041591           (횡단보도)\n",
            "16  0.036468  (주정차금지(황색실선)구역)\n",
            "2   0.026221         (도로 모퉁이)\n",
            "8   0.025316           (보도주차)\n",
            "     support         itemsets\n",
            "19  0.219434   (주차금지(황색점선)구역)\n",
            "15  0.159323          (주정차금지)\n",
            "20  0.152028         (주차방법위반)\n",
            "3   0.144149     (도로교통법 제32조)\n",
            "1   0.080829         (교통소통장애)\n",
            "7   0.050481             (보도)\n",
            "21  0.037642           (횡단보도)\n",
            "16  0.023928  (주정차금지(황색실선)구역)\n",
            "5   0.023636          (버스정류소)\n",
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            "7   0.019076          (버스정류소)\n",
            "     support         itemsets\n",
            "21  0.284340   (주차금지(황색점선)구역)\n",
            "17  0.109694  (주정차금지(황색실선)구역)\n",
            "19  0.108830          (주정차위반)\n",
            "3   0.095645         (교통소통장애)\n",
            "9   0.091106             (보도)\n",
            "0   0.090349    (32조내지 34조위반)\n",
            "24  0.043986           (횡단보도)\n",
            "16  0.037177          (주정차금지)\n",
            "7   0.025829          (버스정류소)\n",
            "10  0.021507           (보도주차)\n",
            "     support         itemsets\n",
            "22  0.288027   (주차금지(황색점선)구역)\n",
            "18  0.117523  (주정차금지(황색실선)구역)\n",
            "20  0.114370          (주정차위반)\n",
            "3   0.098181         (교통소통장애)\n",
            "9   0.097971             (보도)\n",
            "0   0.079996    (32조내지 34조위반)\n",
            "25  0.040786           (횡단보도)\n",
            "17  0.027646          (주정차금지)\n",
            "7   0.023442          (버스정류소)\n",
            "10  0.022390           (보도주차)\n",
            "     support         itemsets\n",
            "3   0.194844         (교통소통장애)\n",
            "21  0.152301   (주차금지(황색점선)구역)\n",
            "18  0.110074  (주정차금지(황색실선)구역)\n",
            "17  0.095682          (주정차금지)\n",
            "9   0.087617             (보도)\n",
            "0   0.061996    (32조내지 34조위반)\n",
            "23  0.058517           (횡단보도)\n",
            "7   0.040962          (버스정류소)\n",
            "14  0.038115           (안전지대)\n",
            "4   0.034794         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "20  0.166274   (주차금지(황색점선)구역)\n",
            "2   0.166136         (교통소통장애)\n",
            "17  0.105219  (주정차금지(황색실선)구역)\n",
            "8   0.094836             (보도)\n",
            "16  0.085006          (주정차금지)\n",
            "0   0.075177    (32조내지 34조위반)\n",
            "23  0.066870           (횡단보도)\n",
            "13  0.043888           (안전지대)\n",
            "6   0.041949          (버스정류소)\n",
            "3   0.026305         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "17  0.303895   (주차금지(황색점선)구역)\n",
            "15  0.156211          (주정차위반)\n",
            "0   0.096421    (32조내지 34조위반)\n",
            "13  0.093368  (주정차금지(황색실선)구역)\n",
            "8   0.086947             (보도)\n",
            "3   0.076842         (교통소통장애)\n",
            "20  0.038211           (횡단보도)\n",
            "12  0.030737          (주정차금지)\n",
            "6   0.028316          (버스정류소)\n",
            "18  0.020842         (주차방법위반)\n",
            "     support         itemsets\n",
            "19  0.296616   (주차금지(황색점선)구역)\n",
            "17  0.159519          (주정차위반)\n",
            "0   0.092975    (32조내지 34조위반)\n",
            "3   0.089376         (교통소통장애)\n",
            "15  0.087422  (주정차금지(황색실선)구역)\n",
            "9   0.082793             (보도)\n",
            "22  0.038671           (횡단보도)\n",
            "7   0.034249          (버스정류소)\n",
            "14  0.030958          (주정차금지)\n",
            "20  0.023347         (주차방법위반)\n",
            "     support         itemsets\n",
            "17  0.310345   (주차금지(황색점선)구역)\n",
            "15  0.160262          (주정차위반)\n",
            "0   0.091504    (32조내지 34조위반)\n",
            "13  0.091192  (주정차금지(황색실선)구역)\n",
            "7   0.083403             (보도)\n",
            "3   0.074782         (교통소통장애)\n",
            "20  0.037391           (횡단보도)\n",
            "5   0.036041          (버스정류소)\n",
            "12  0.031886          (주정차금지)\n",
            "18  0.018072         (주차방법위반)\n",
            "     support         itemsets\n",
            "18  0.311145   (주차금지(황색점선)구역)\n",
            "16  0.146848          (주정차위반)\n",
            "0   0.096319    (32조내지 34조위반)\n",
            "14  0.089864  (주정차금지(황색실선)구역)\n",
            "3   0.086132         (교통소통장애)\n",
            "7   0.082602             (보도)\n",
            "5   0.038124          (버스정류소)\n",
            "13  0.035603          (주정차금지)\n",
            "21  0.034594           (횡단보도)\n",
            "19  0.018356         (주차방법위반)\n",
            "     support         itemsets\n",
            "17  0.329081   (주차금지(황색점선)구역)\n",
            "16  0.127283          (주정차위반)\n",
            "14  0.100609  (주정차금지(황색실선)구역)\n",
            "0   0.084759    (32조내지 34조위반)\n",
            "7   0.084179             (보도)\n",
            "3   0.080410         (교통소통장애)\n",
            "5   0.037885          (버스정류소)\n",
            "20  0.037789           (횡단보도)\n",
            "13  0.030057          (주정차금지)\n",
            "18  0.022615         (주차방법위반)\n",
            "     support         itemsets\n",
            "15  0.199027   (주차금지(황색점선)구역)\n",
            "6   0.122039          (버스정류소)\n",
            "14  0.120135  (주정차금지(황색실선)구역)\n",
            "0   0.095601    (32조내지 34조위반)\n",
            "8   0.091582             (보도)\n",
            "16  0.084179         (주차방법위반)\n",
            "13  0.076142          (주정차금지)\n",
            "18  0.072547           (횡단보도)\n",
            "3   0.056261         (교통소통장애)\n",
            "11  0.023054           (안전지대)\n",
            "     support         itemsets\n",
            "17  0.220821   (주차금지(황색점선)구역)\n",
            "15  0.144292  (주정차금지(황색실선)구역)\n",
            "0   0.132108    (32조내지 34조위반)\n",
            "7   0.102180             (보도)\n",
            "14  0.072039          (주정차금지)\n",
            "18  0.070543         (주차방법위반)\n",
            "20  0.070115           (횡단보도)\n",
            "2   0.055793         (교통소통장애)\n",
            "5   0.042967          (버스정류소)\n",
            "11  0.029286           (안전지대)\n",
            "     support         itemsets\n",
            "20  0.206393   (주차금지(황색점선)구역)\n",
            "0   0.123518    (32조내지 34조위반)\n",
            "3   0.122650         (교통소통장애)\n",
            "18  0.110934          (주정차위반)\n",
            "16  0.105728  (주정차금지(황색실선)구역)\n",
            "9   0.084466             (보도)\n",
            "23  0.054527           (횡단보도)\n",
            "15  0.044981          (주정차금지)\n",
            "21  0.027047         (주차방법위반)\n",
            "7   0.025022          (버스정류소)\n",
            "     support         itemsets\n",
            "21  0.208144   (주차금지(황색점선)구역)\n",
            "3   0.129041         (교통소통장애)\n",
            "0   0.115559    (32조내지 34조위반)\n",
            "19  0.111157          (주정차위반)\n",
            "17  0.107030  (주정차금지(황색실선)구역)\n",
            "9   0.087495             (보도)\n",
            "23  0.052965           (횡단보도)\n",
            "16  0.031916          (주정차금지)\n",
            "22  0.027514         (주차방법위반)\n",
            "7   0.026138          (버스정류소)\n",
            "     support         itemsets\n",
            "21  0.211717   (주차금지(황색점선)구역)\n",
            "3   0.126567         (교통소통장애)\n",
            "0   0.114578    (32조내지 34조위반)\n",
            "19  0.102044          (주정차위반)\n",
            "17  0.101907  (주정차금지(황색실선)구역)\n",
            "9   0.091553             (보도)\n",
            "23  0.046049           (횡단보도)\n",
            "16  0.036512          (주정차금지)\n",
            "7   0.028202          (버스정류소)\n",
            "5   0.024114     (도로교통법 제32조)\n",
            "     support         itemsets\n",
            "22  0.205121   (주차금지(황색점선)구역)\n",
            "3   0.129910         (교통소통장애)\n",
            "0   0.127229    (32조내지 34조위반)\n",
            "18  0.108728  (주정차금지(황색실선)구역)\n",
            "20  0.095053          (주정차위반)\n",
            "9   0.085400             (보도)\n",
            "24  0.048934           (횡단보도)\n",
            "17  0.036064          (주정차금지)\n",
            "7   0.027484          (버스정류소)\n",
            "4   0.023596         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "20  0.214168   (주차금지(황색점선)구역)\n",
            "16  0.126410  (주정차금지(황색실선)구역)\n",
            "3   0.124530         (교통소통장애)\n",
            "0   0.112077    (32조내지 34조위반)\n",
            "18  0.090695          (주정차위반)\n",
            "9   0.080827             (보도)\n",
            "23  0.047697           (횡단보도)\n",
            "15  0.043820          (주정차금지)\n",
            "21  0.027491         (주차방법위반)\n",
            "7   0.025258          (버스정류소)\n",
            "     support         itemsets\n",
            "17  0.193224   (주차금지(황색점선)구역)\n",
            "2   0.119603         (교통소통장애)\n",
            "13  0.097949          (주정차금지)\n",
            "14  0.093746  (주정차금지(황색실선)구역)\n",
            "0   0.086741    (32조내지 34조위반)\n",
            "18  0.085085         (주차방법위반)\n",
            "8   0.075532             (보도)\n",
            "20  0.060247           (횡단보도)\n",
            "6   0.054388          (버스정류소)\n",
            "3   0.035792         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "19  0.204150   (주차금지(황색점선)구역)\n",
            "16  0.118979  (주정차금지(황색실선)구역)\n",
            "0   0.115786    (32조내지 34조위반)\n",
            "3   0.092571         (교통소통장애)\n",
            "20  0.085316         (주차방법위반)\n",
            "15  0.080528          (주정차금지)\n",
            "8   0.070807             (보도)\n",
            "21  0.056587           (횡단보도)\n",
            "5   0.042078     (도로교통법 제32조)\n",
            "6   0.032356          (버스정류소)\n",
            "     support         itemsets\n",
            "20  0.179044   (주차금지(황색점선)구역)\n",
            "0   0.139617    (32조내지 34조위반)\n",
            "16  0.130165          (주정차금지)\n",
            "17  0.113692  (주정차금지(황색실선)구역)\n",
            "21  0.090467         (주차방법위반)\n",
            "22  0.071294           (횡단보도)\n",
            "9   0.067513             (보도)\n",
            "2   0.066703         (교통소통장애)\n",
            "4   0.035377     (도로교통법 제32조)\n",
            "3   0.028895         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "20  0.204830   (주차금지(황색점선)구역)\n",
            "16  0.130980          (주정차금지)\n",
            "0   0.122620    (32조내지 34조위반)\n",
            "17  0.111705  (주정차금지(황색실선)구역)\n",
            "21  0.082443         (주차방법위반)\n",
            "22  0.066419           (횡단보도)\n",
            "2   0.058291         (교통소통장애)\n",
            "9   0.057362             (보도)\n",
            "4   0.047144     (도로교통법 제32조)\n",
            "3   0.025546         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "15  0.203765   (주차금지(황색점선)구역)\n",
            "11  0.137647          (주정차금지)\n",
            "0   0.114353    (32조내지 34조위반)\n",
            "12  0.100471  (주정차금지(황색실선)구역)\n",
            "16  0.091059         (주차방법위반)\n",
            "7   0.072235             (보도)\n",
            "2   0.059765         (교통소통장애)\n",
            "17  0.059294           (횡단보도)\n",
            "4   0.034118     (도로교통법 제32조)\n",
            "3   0.032000         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "17  0.196403   (주차금지(황색점선)구역)\n",
            "0   0.132374    (32조내지 34조위반)\n",
            "14  0.119664          (주정차금지)\n",
            "15  0.107194  (주정차금지(황색실선)구역)\n",
            "18  0.099281         (주차방법위반)\n",
            "8   0.070264             (보도)\n",
            "2   0.058034         (교통소통장애)\n",
            "19  0.057314           (횡단보도)\n",
            "4   0.037410     (도로교통법 제32조)\n",
            "3   0.030456         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "15  0.231537   (주차금지(황색점선)구역)\n",
            "13  0.133733          (주정차금지)\n",
            "14  0.122664  (주정차금지(황색실선)구역)\n",
            "0   0.119760    (32조내지 34조위반)\n",
            "16  0.086191         (주차방법위반)\n",
            "7   0.069860             (보도)\n",
            "2   0.055888         (교통소통장애)\n",
            "17  0.049174           (횡단보도)\n",
            "4   0.025222     (도로교통법 제32조)\n",
            "3   0.024678         (도로 모퉁이)\n",
            "     support         itemsets\n",
            "19  0.201598   (주차금지(황색점선)구역)\n",
            "16  0.123406  (주정차금지(황색실선)구역)\n",
            "15  0.119667          (주정차금지)\n",
            "0   0.116947    (32조내지 34조위반)\n",
            "20  0.101989         (주차방법위반)\n",
            "2   0.062043         (교통소통장애)\n",
            "8   0.058134             (보도)\n",
            "22  0.053884           (횡단보도)\n",
            "4   0.042325     (도로교통법 제32조)\n",
            "6   0.024817          (버스정류소)\n",
            "     support         itemsets\n",
            "18  0.215156   (주차금지(황색점선)구역)\n",
            "0   0.159844    (32조내지 34조위반)\n",
            "16  0.110136  (주정차금지(황색실선)구역)\n",
            "15  0.099415          (주정차금지)\n",
            "19  0.084308         (주차방법위반)\n",
            "4   0.064815     (도로교통법 제32조)\n",
            "20  0.055799           (횡단보도)\n",
            "2   0.054581         (교통소통장애)\n",
            "9   0.051170             (보도)\n",
            "3   0.026803         (도로 모퉁이)\n"
          ]
        }
      ],
      "source": [
        "# 연관 규칙 탐색\n",
        "for frequent_itemsets in apriori_generator(df):\n",
        "    rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
        "    # rules를 활용한 분석 결과를 저장하거나 출력하는 등의 작업을 수행합니다.\n",
        "    top_items = frequent_itemsets.nlargest(10, 'support')\n",
        "    print(top_items)\n",
        "\n",
        "# inf : 전제조건이 성립할 때 결과조건이 성립하지 않을 확률의 증가비율이 무한대라는 것 -> 다른 지표 사용"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xbBibomWXbkG",
        "outputId": "962e083e-9293-4d46-c283-998c3c754cca"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.215156</td>\n",
              "      <td>(주차금지(황색점선)구역)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.159844</td>\n",
              "      <td>(32조내지 34조위반)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.110136</td>\n",
              "      <td>(주정차금지(황색실선)구역)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.099415</td>\n",
              "      <td>(주정차금지)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.084308</td>\n",
              "      <td>(주차방법위반)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.064815</td>\n",
              "      <td>(도로교통법 제32조)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.055799</td>\n",
              "      <td>(횡단보도)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.054581</td>\n",
              "      <td>(교통소통장애)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.051170</td>\n",
              "      <td>(보도)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.026803</td>\n",
              "      <td>(도로 모퉁이)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     support         itemsets\n",
              "18  0.215156   (주차금지(황색점선)구역)\n",
              "0   0.159844    (32조내지 34조위반)\n",
              "16  0.110136  (주정차금지(황색실선)구역)\n",
              "15  0.099415          (주정차금지)\n",
              "19  0.084308         (주차방법위반)\n",
              "4   0.064815     (도로교통법 제32조)\n",
              "20  0.055799           (횡단보도)\n",
              "2   0.054581         (교통소통장애)\n",
              "9   0.051170             (보도)\n",
              "3   0.026803         (도로 모퉁이)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.DataFrame(top_items)\n",
        "data\n",
        "\n",
        "# 지지도가 높은 상위 10개의 데이터 출력 시 상(32조 내지 34조 : 보행자,차량 우선순위/제한속도/신호준수)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5pD7c4y4XbkH",
        "outputId": "ce3df9da-687d-44ab-fe4a-c6373b48677c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "      <th>zhangs_metric</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
              "Index: []"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "association_rules(data, metric = 'confidence', min_threshold=0.001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wUSPkuRYXbkH",
        "outputId": "f2907d7b-a47e-4520-87b1-5e7b2571f344"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0      (황색점선)      (황색실선)            0.001089            0.001089  0.001089   \n",
            "1      (황색실선)      (황색점선)            0.001089            0.001089  0.001089   \n",
            "\n",
            "   confidence   lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  918.5  0.001088         inf            1.0  \n",
            "1         1.0  918.5  0.001088         inf            1.0  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (건널목)      (횡단보도)            0.001107            0.035991  0.001107   \n",
            "1      (횡단보도)       (건널목)            0.035991            0.001107  0.001107   \n",
            "2     (소방용기기)       (소화전)            0.001107            0.011628  0.001107   \n",
            "3       (소화전)     (소방용기기)            0.011628            0.001107  0.001107   \n",
            "\n",
            "   confidence       lift  leverage  conviction  zhangs_metric  \n",
            "0    1.000000  27.784615  0.001068         inf       0.965078  \n",
            "1    0.030769  27.784615  0.001068    1.030603       1.000000  \n",
            "2    1.000000  86.000000  0.001095         inf       0.989468  \n",
            "3    0.095238  86.000000  0.001095    1.104039       1.000000  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0     (소방용기기)       (소화전)            0.001859            0.009294  0.001859   \n",
            "1       (소화전)     (소방용기기)            0.009294            0.001859  0.001859   \n",
            "\n",
            "   confidence   lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  107.6  0.001841         inf       0.992551  \n",
            "1         0.2  107.6  0.001841    1.247677       1.000000  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0      (황색점선)      (황색실선)            0.001994            0.001994  0.001994   \n",
            "1      (황색실선)      (황색점선)            0.001994            0.001994  0.001994   \n",
            "\n",
            "   confidence   lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  501.5   0.00199         inf            1.0  \n",
            "1         1.0  501.5   0.00199         inf            1.0  \n",
            "  antecedents consequents  antecedent support  consequent support  support  \\\n",
            "0      (황색점선)      (황색실선)             0.00186             0.00186  0.00186   \n",
            "1      (황색실선)      (황색점선)             0.00186             0.00186  0.00186   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  537.571429  0.001857         inf            1.0  \n",
            "1         1.0  537.571429  0.001857         inf            1.0  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (건널목)      (횡단보도)            0.002154            0.126239  0.001293   \n",
            "1      (횡단보도)       (건널목)            0.126239            0.002154  0.001293   \n",
            "\n",
            "   confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0    0.600000  4.752901  0.001021    2.184403       0.791307  \n",
            "1    0.010239  4.752901  0.001021    1.008168       0.903682  \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0      (황색점선)      (황색실선)            0.001141            0.001141  0.001141   \n",
            "1      (황색실선)      (황색점선)            0.001141            0.001141  0.001141   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  876.555556   0.00114         inf            1.0  \n",
            "1         1.0  876.555556   0.00114         inf            1.0  \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0      (황색점선)      (황색실선)            0.001884            0.001884  0.001884   \n",
            "1      (황색실선)      (황색점선)            0.001884            0.001884  0.001884   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  530.785714   0.00188         inf            1.0  \n",
            "1         1.0  530.785714   0.00188         inf            1.0  \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0      (황색점선)      (황색실선)            0.002992            0.002992  0.002992   \n",
            "1      (황색실선)      (황색점선)            0.002992            0.002992  0.002992   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  334.227273  0.002983         inf            1.0  \n",
            "1         1.0  334.227273  0.002983         inf            1.0  \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0      (황색점선)      (황색실선)            0.001836            0.001836  0.001836   \n",
            "1      (황색실선)      (황색점선)            0.001836            0.001836  0.001836   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  544.571429  0.001833         inf            1.0  \n",
            "1         1.0  544.571429  0.001833         inf            1.0  \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0      (황색점선)      (황색실선)            0.001249            0.001249  0.001249   \n",
            "1      (황색실선)      (황색점선)            0.001249            0.001249  0.001249   \n",
            "\n",
            "   confidence   lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  800.5  0.001248         inf            1.0  \n",
            "1         1.0  800.5  0.001248         inf            1.0  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (모퉁이)       (교차로)            0.002299            0.011954  0.001839   \n",
            "1       (교차로)       (모퉁이)            0.011954            0.002299  0.001839   \n",
            "\n",
            "   confidence       lift  leverage  conviction  zhangs_metric  \n",
            "0    0.800000  66.923077  0.001812    4.940230       0.987327  \n",
            "1    0.153846  66.923077  0.001812    1.179101       0.996975  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0      (황색점선)      (황색실선)            0.001235            0.001235  0.001235   \n",
            "1      (황색실선)      (황색점선)            0.001235            0.001235  0.001235   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0         1.0  809.888889  0.001233         inf            1.0  \n",
            "1         1.0  809.888889  0.001233         inf            1.0  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support  support  \\\n",
            "0       (건널목)      (횡단보도)             0.00192             0.06592  0.00144   \n",
            "1      (횡단보도)       (건널목)             0.06592             0.00192  0.00144   \n",
            "2      (황색점선)      (황색실선)             0.00256             0.00256  0.00256   \n",
            "3      (황색실선)      (황색점선)             0.00256             0.00256  0.00256   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0    0.750000   11.377427  0.001313     3.73632       0.913861  \n",
            "1    0.021845   11.377427  0.001313     1.02037       0.976476  \n",
            "2    1.000000  390.625000  0.002553         inf       1.000000  \n",
            "3    1.000000  390.625000  0.002553         inf       1.000000  \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (건널목)      (횡단보도)            0.002730            0.074835  0.001606   \n",
            "1      (횡단보도)       (건널목)            0.074835            0.002730  0.001606   \n",
            "\n",
            "   confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0    0.588235  7.860389  0.001402    2.246828       0.875169  \n",
            "1    0.021459  7.860389  0.001402    1.019140       0.943378  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (건널목)      (횡단보도)            0.004321            0.078253  0.004321   \n",
            "1      (횡단보도)       (건널목)            0.078253            0.004321  0.004321   \n",
            "\n",
            "   confidence       lift  leverage  conviction  zhangs_metric  \n",
            "0    1.000000  12.779141  0.003983         inf       0.925747  \n",
            "1    0.055215  12.779141  0.003983    1.053868       1.000000  \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0       (건널목)      (횡단보도)            0.002123            0.082573  0.002123   \n",
            "1      (횡단보도)       (건널목)            0.082573            0.002123  0.002123   \n",
            "2       (모퉁이)       (교차로)            0.003821            0.009552  0.001061   \n",
            "3       (교차로)       (모퉁이)            0.009552            0.003821  0.001061   \n",
            "\n",
            "   confidence       lift  leverage  conviction  zhangs_metric  \n",
            "0    1.000000  12.110540  0.001947         inf       0.919379  \n",
            "1    0.025707  12.110540  0.001947    1.024207       1.000000  \n",
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            "Empty DataFrame\n",
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            "\n",
            "   confidence       lift  leverage  conviction  zhangs_metric  \n",
            "0    0.888889  17.807407  0.000966    8.550749       0.944932  \n",
            "1    0.020513  17.807407  0.000966    1.019766       0.993433  \n",
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            "Index: []\n",
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            "0     (소방용기기)       (소화전)            0.001213            0.008490  0.001213   \n",
            "1       (소화전)     (소방용기기)            0.008490            0.001213  0.001213   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0    1.000000  117.779221  0.001203         inf       0.992714  \n",
            "1    0.142857  117.779221  0.001203    1.165252       1.000000  \n",
            "  antecedents consequents  antecedent support  consequent support   support  \\\n",
            "0     (소방용기기)       (소화전)            0.001081            0.009835  0.001081   \n",
            "1       (소화전)     (소방용기기)            0.009835            0.001081  0.001081   \n",
            "\n",
            "   confidence        lift  leverage  conviction  zhangs_metric  \n",
            "0     1.00000  101.681319   0.00107         inf       0.991237  \n",
            "1     0.10989  101.681319   0.00107    1.122243       1.000000  \n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "# 연관 규칙 탐색\n",
        "for frequent_itemsets in apriori_generator(df):\n",
        "    rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
        "    print(rules)\n",
        "# convince -> inf : 전제조건이 성립할 때 결과조건이 성립하지 않을 확률의 증가비율이 무한대 -> 지표로 사용하기 적절하지 x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BZgzmou9XbkH",
        "outputId": "219becf2-98ba-4ebd-a724-93b09f3a3780"
      },
      "outputs": [
        {
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              "           0      1      2      3      4      5      6      7      8      9   \\\n",
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              "\n",
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            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "te = TransactionEncoder()\n",
        "te_result = te.fit(df).transform(df)\n",
        "df = pd.DataFrame(te_result)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MIuCSl_9XbkH",
        "outputId": "615acfa4-9c12-454e-9fec-c4eb929dcdd3"
      },
      "outputs": [
        {
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      "source": [
        "itemset = apriori(df, use_colnames = True)\n",
        "itemset"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CDyzkm4XbkH"
      },
      "source": [
        "단속동으로 GROUPBY"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "dUv8SVbpupeR",
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      "outputs": [
        {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>단속일시</th>\n",
              "      <th>단속원금</th>\n",
              "      <th>단속장소</th>\n",
              "      <th>위반내용</th>\n",
              "      <th>견인지시</th>\n",
              "      <th>데이터기준일자</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>단속동</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>강변북로</th>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>강변역로</th>\n",
              "      <td>67</td>\n",
              "      <td>67</td>\n",
              "      <td>67</td>\n",
              "      <td>67</td>\n",
              "      <td>67</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>강변역로2길</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>강변역로4길</th>\n",
              "      <td>230</td>\n",
              "      <td>230</td>\n",
              "      <td>230</td>\n",
              "      <td>230</td>\n",
              "      <td>230</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>광나루로</th>\n",
              "      <td>2040</td>\n",
              "      <td>2040</td>\n",
              "      <td>2040</td>\n",
              "      <td>2040</td>\n",
              "      <td>2040</td>\n",
              "      <td>2040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>천호대로138길</th>\n",
              "      <td>209</td>\n",
              "      <td>209</td>\n",
              "      <td>209</td>\n",
              "      <td>209</td>\n",
              "      <td>209</td>\n",
              "      <td>209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>천호대로140길</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>천호대로141길</th>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>천호대로143길</th>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>화양동</th>\n",
              "      <td>50312</td>\n",
              "      <td>50312</td>\n",
              "      <td>50312</td>\n",
              "      <td>50312</td>\n",
              "      <td>50312</td>\n",
              "      <td>49480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>491 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2483f581-aaa5-456d-9caa-194c86812cbf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "          document.querySelector('#df-2483f581-aaa5-456d-9caa-194c86812cbf 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-2483f581-aaa5-456d-9caa-194c86812cbf');\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",
              "  "
            ],
            "text/plain": [
              "           단속일시   단속원금   단속장소   위반내용   견인지시  데이터기준일자\n",
              "단속동                                                 \n",
              "강변북로         29     29     29     29     29       28\n",
              "강변역로         67     67     67     67     67       67\n",
              "강변역로2길        4      4      4      4      4        4\n",
              "강변역로4길      230    230    230    230    230      230\n",
              "광나루로       2040   2040   2040   2040   2040     2040\n",
              "...         ...    ...    ...    ...    ...      ...\n",
              "천호대로138길    209    209    209    209    209      209\n",
              "천호대로140길      5      5      5      5      5        5\n",
              "천호대로141길     32     32     32     32     32       32\n",
              "천호대로143길     11     11     11     11     11       11\n",
              "화양동       50312  50312  50312  50312  50312    49480\n",
              "\n",
              "[491 rows x 6 columns]"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.DataFrame(df)\n",
        "data.groupby('단속동').count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubVN5KBbqTD0",
        "outputId": "b00e4943-76fd-426a-d00d-b59edd6a0def"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------------------------------------------------------\n",
            "                      단속일시   단속원금       단속동      단속장소    위반내용 견인지시     데이터기준일자\n",
            "331897 2020-01-01 00:05:00  40000       군자로       152  주차방법위반  미견인  2022-06-10\n",
            "331898 2020-01-01 00:18:00  40000      아차산로       334      보도  미견인  2022-06-10\n",
            "331899 2020-01-01 00:23:00  40000   뚝섬로58가길  20 주차장막음  주차방법위반  미견인  2022-06-10\n",
            "331900 2020-01-01 00:33:00  40000  자양번영로3나길   35 주차장앞  주차방법위반  미견인  2022-06-10\n",
            "331901 2020-01-01 00:45:00  40000    동일로12길        28  주차방법위반  미견인  2022-06-10\n",
            "(60545, 7)\n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "                      단속일시   단속원금  단속동                    단속장소    위반내용 견인지시  \\\n",
            "392442 2021-01-01 01:28:00  40000  중곡동  서울 광진구 중곡동 296-29 주차장앞  교통소통장애  미견인   \n",
            "392443 2021-01-01 02:21:00  40000  구의동         서울 광진구 구의로 22-1      보도  미견인   \n",
            "392444 2021-01-01 02:26:00  40000  중곡동        서울 광진구 중곡동 438-3  교통소통장애  미견인   \n",
            "392445 2021-01-01 03:01:00  50000  자양동        서울 광진구 중곡동 438-3      보도  미견인   \n",
            "392446 2021-01-01 03:05:00  50000  자양동          서울 광진구 뚝섬로 492     소화전  미견인   \n",
            "\n",
            "           데이터기준일자  \n",
            "392442  2022-06-10  \n",
            "392443  2022-06-10  \n",
            "392444  2022-06-10  \n",
            "392445  2022-06-10  \n",
            "392446  2022-06-10  \n",
            "(58757, 7)\n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "                      단속일시   단속원금  단속동                   단속장소    위반내용  견인지시  \\\n",
            "451199 2022-01-01 02:29:00  40000  중곡동    서울특별시 광진구 영화사로5길 19  교통소통장애   미견인   \n",
            "451200 2022-01-01 06:41:00  80000  중곡동                150-196     소화전   미견인   \n",
            "451201 2022-01-01 07:02:00  40000  구의동  서울 광진구 구의동 660-1 주차장앞  교통소통장애   미견인   \n",
            "451202 2022-01-01 07:29:00  40000  구의동         서울 광진구 구의동 6-4    횡단보도  견인처리   \n",
            "451203 2022-01-01 10:55:00  40000  구의동             리젠트오피스텔건너편    안전지대   미견인   \n",
            "\n",
            "           데이터기준일자  \n",
            "451199  2022-06-10  \n",
            "451200  2022-06-10  \n",
            "451201  2022-06-10  \n",
            "451202  2022-06-10  \n",
            "451203  2022-06-10  \n",
            "(35701, 7)\n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "                      단속일시   단속원금    단속동              단속장소           위반내용  \\\n",
            "486900 2023-01-01 00:25:00  32000  자양강변길               740           횡단보도   \n",
            "486901 2023-01-01 03:36:00  32000    자양동  서울특별시 광진구 자양로 61             보도   \n",
            "486902 2023-01-01 03:38:00  40000    자양동  서울특별시 광진구 자양로 61             보도   \n",
            "486903 2023-01-01 07:10:00  40000    구의동         강변역 4번 출구   주차금지(황색점선)구역   \n",
            "486904 2023-01-01 07:12:00  40000    구의동       지너스타워 주차장 앞  주정차금지(황색실선)구역   \n",
            "\n",
            "       견인지시 데이터기준일자  \n",
            "486900  미견인     NaN  \n",
            "486901  미견인     NaN  \n",
            "486902  미견인     NaN  \n",
            "486903  미견인     NaN  \n",
            "486904  미견인     NaN  \n",
            "(4031, 7)\n"
          ]
        }
      ],
      "source": [
        "# 단속일시 년도별로 추출\n",
        "df['단속일시'] = pd.to_datetime(df['단속일시'], format='%Y-%m-%d %H:%M:%S')\n",
        "\n",
        "# '단속일시' 열에서 '2020'년도 데이터만 추출\n",
        "df_2020 = df[df['단속일시'].dt.year == 2020]\n",
        "\n",
        "print(df_2020.head())\n",
        "print(df_2020.shape)\n",
        "\n",
        "print('----------------------------------------------------------------------------------------------------------------')\n",
        "\n",
        "# '단속일시' 열에서 '2021'년도 데이터만 추출\n",
        "df_2021 = df[df['단속일시'].dt.year == 2021]\n",
        "\n",
        "print(df_2021.head())\n",
        "print(df_2021.shape)\n",
        "\n",
        "print('----------------------------------------------------------------------------------------------------------------')\n",
        "\n",
        "# '단속일시' 열에서 '2022'년도 데이터만 추출\n",
        "df_2022 = df[df['단속일시'].dt.year == 2022]\n",
        "\n",
        "print(df_2022.head())\n",
        "print(df_2022.shape)\n",
        "\n",
        "print('----------------------------------------------------------------------------------------------------------------')\n",
        "\n",
        "# '단속일시' 열에서 '2023'년도 데이터만 추출\n",
        "df_2023 = df[df['단속일시'].dt.year == 2023]\n",
        "\n",
        "print(df_2023.head())\n",
        "print(df_2023.shape)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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