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Model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m         \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_kwargs_and_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhandle_train\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"fit_transform\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/yellowbrick/cluster/silhouette.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_clusters_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'OPTICS' object has no attribute 'n_clusters'","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    255\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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estimator, plot, scale, save, fold, fit_kwargs, plot_kwargs, groups, feature_name, label, use_train_data, verbose, system, display, display_format)\u001b[0m\n\u001b[1;32m   1942\u001b[0m                 \u001b[0;31m# execute the plot method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1943\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mredirect_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1944\u001b[0;31m                     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1945\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1946\u001b[0m                     \u001b[0mplot_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pycaret/internal/pycaret_experiment/tabular_experiment.py\u001b[0m in \u001b[0;36msilhouette\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1099\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Silhouette plot failed. 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Exception:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1101\u001b[0;31m                         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Plot Type not supported for this model.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1103\u001b[0m                 \u001b[0;32mdef\u001b[0m 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\n"},"metadata":{}}]}}}}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"njmyNKcCBXxr","executionInfo":{"status":"ok","timestamp":1682428445456,"user_tz":-540,"elapsed":34619,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"fbdb84e1-3bde-4df1-b2da-24c49211b0a2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking 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/root/.cache/pip/wheels/9e/36/06/4c11e300918011376af149098621ec7ebe06d8256566d43d51\n","  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pyod: filename=pyod-1.0.9-py3-none-any.whl size=184112 sha256=0f2c8cc7c555f26ad184c7cd2c2175feb11f952d88440d6393e5b05973808c2b\n","  Stored in directory: /root/.cache/pip/wheels/1b/9c/b8/9759d7cc64a1e01bb9872ade80cb7db445ccf506e083325106\n","Successfully built plotly-resampler pyod\n","Installing collected packages: trace-updater, kaleido, dash-table, dash-html-components, dash-core-components, xxhash, wurlitzer, schemdraw, retrying, orjson, jedi, deprecation, deprecated, ansi2html, sktime, scikit-plot, pyod, dash, pmdarima, jupyter-dash, category-encoders, tbats, plotly-resampler, pycaret\n","Successfully installed ansi2html-1.8.0 category-encoders-2.6.0 dash-2.9.3 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 deprecated-1.2.13 deprecation-2.1.0 jedi-0.18.2 jupyter-dash-0.4.2 kaleido-0.2.1 orjson-3.8.10 plotly-resampler-0.8.3.2 pmdarima-2.0.3 pycaret-3.0.0 pyod-1.0.9 retrying-1.3.4 schemdraw-0.16 scikit-plot-0.3.7 sktime-0.17.2 tbats-1.1.3 trace-updater-0.0.9.1 wurlitzer-3.0.3 xxhash-3.2.0\n"]}],"source":["# 필요 라이브러리 설치\n","!pip install pycaret"]},{"cell_type":"code","source":["# 드라이브 마운트\n","from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_A1PqdRrT2-K","executionInfo":{"status":"ok","timestamp":1682686805276,"user_tz":-540,"elapsed":19370,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"dc83d63a-5655-41ce-ad18-417ac5a627b7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KbTjTb03Ts_m"},"outputs":[],"source":["# 필요 라이브러리 불러오기\n","import os\n","\n","import numpy as np\n","import pandas as pd\n","\n","import matplotlib.pyplot as plt\n","\n","# #pycaret\n","# import ipywidgets as widgets\n","# from pycaret.clustering import *"]},{"cell_type":"code","source":["ROOT = '/content/drive/MyDrive/contest/gwangjin/'"],"metadata":{"id":"uGUna6naUINO"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":384},"id":"8G-CkxNaTs_n","executionInfo":{"status":"ok","timestamp":1682686806448,"user_tz":-540,"elapsed":734,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"84539d46-56e4-43d5-ca9b-b7f379efc251"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   표준버스정류장ID  버스정류장ARS번호      낮시간대총승객수     밤시간대총승객수        일일총승객수  대규모점포  \\\n","0  101000252        5118  14247.166667  1726.166667  30186.000000    0.0   \n","1  104000001        5001  15681.333333  3756.500000  39580.666667    0.0   \n","\n","     사업체수    종사자수     낮시간대 생활인구     밤시간대 생활인구  환승가능노선수  starbucks_distance  \\\n","0  2350.0  7380.0  9.197688e+07  1.254655e+08      3.0          740.041174   \n","1  2152.0  6363.0  8.353262e+07  9.590508e+07      7.0          350.338493   \n","\n","   market_distance  count        총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율   행정동  \n","0        93.098861    1.0  3.238924e+08        0.146777      0.232630  중곡2동  \n","1       723.257352    3.0  2.708308e+08        0.145448      0.223181  중곡1동  "],"text/html":["\n","  <div id=\"df-27134c9e-3ecb-4297-9138-ffcee99142e9\">\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>표준버스정류장ID</th>\n","      <th>버스정류장ARS번호</th>\n","      <th>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>대규모점포</th>\n","      <th>사업체수</th>\n","      <th>종사자수</th>\n","      <th>낮시간대 생활인구</th>\n","      <th>밤시간대 생활인구</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>총생활인구수</th>\n","      <th>65세 이상 생활인구 비율</th>\n","      <th>1020 생활인구 비율</th>\n","      <th>행정동</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>101000252</td>\n","      <td>5118</td>\n","      <td>14247.166667</td>\n","      <td>1726.166667</td>\n","      <td>30186.000000</td>\n","      <td>0.0</td>\n","      <td>2350.0</td>\n","      <td>7380.0</td>\n","      <td>9.197688e+07</td>\n","      <td>1.254655e+08</td>\n","      <td>3.0</td>\n","      <td>740.041174</td>\n","      <td>93.098861</td>\n","      <td>1.0</td>\n","      <td>3.238924e+08</td>\n","      <td>0.146777</td>\n","      <td>0.232630</td>\n","      <td>중곡2동</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>104000001</td>\n","      <td>5001</td>\n","      <td>15681.333333</td>\n","      <td>3756.500000</td>\n","      <td>39580.666667</td>\n","      <td>0.0</td>\n","      <td>2152.0</td>\n","      <td>6363.0</td>\n","      <td>8.353262e+07</td>\n","      <td>9.590508e+07</td>\n","      <td>7.0</td>\n","      <td>350.338493</td>\n","      <td>723.257352</td>\n","      <td>3.0</td>\n","      <td>2.708308e+08</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>중곡1동</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27134c9e-3ecb-4297-9138-ffcee99142e9')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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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":4}],"source":["data = pd.read_csv(ROOT+'data/merged_df_ver6.csv', encoding='utf-8')\n","data.head(2)"]},{"cell_type":"code","source":["data_drop = data.drop(['표준버스정류장ID', '버스정류장ARS번호', '대규모점포', '사업체수', '종사자수', '낮시간대 생활인구', '밤시간대 생활인구', '총생활인구수', '65세 이상 생활인구 비율', '1020 생활인구 비율', '행정동'],\n","                      axis=1)\n","# 정수형 변환\n","data_drop = data_drop.astype('int32')\n","data_drop.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R8l7EtTdBgYv","executionInfo":{"status":"ok","timestamp":1682428468649,"user_tz":-540,"elapsed":8,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"9ed0f6f2-38a5-4ccc-a3d5-82cd2677f63c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 269 entries, 0 to 268\n","Data columns (total 7 columns):\n"," #   Column              Non-Null Count  Dtype\n","---  ------              --------------  -----\n"," 0   낮시간대총승객수            269 non-null    int32\n"," 1   밤시간대총승객수            269 non-null    int32\n"," 2   일일총승객수              269 non-null    int32\n"," 3   환승가능노선수             269 non-null    int32\n"," 4   starbucks_distance  269 non-null    int32\n"," 5   market_distance     269 non-null    int32\n"," 6   count               269 non-null    int32\n","dtypes: int32(7)\n","memory usage: 7.5 KB\n"]}]},{"cell_type":"code","source":["# 필요 라이브러리 불러오기\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.cluster import DBSCAN"],"metadata":{"id":"C21fbaL9BlZE"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BrassNwZTs_s"},"outputs":[],"source":["# 정규화\n","\n","scaler = MinMaxScaler()\n","\n","scaler.fit(data_drop)\n","data_sc = scaler.transform(data_drop)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2dXTMW41Ts_s","colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1682428469161,"user_tz":-540,"elapsed":6,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"8468f210-74d6-4648-eef0-3836c8b61213"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수    밤시간대총승객수      일일총승객수     환승가능노선수  starbucks_distance  \\\n","count  269.000000  269.000000  269.000000  269.000000          269.000000   \n","mean     0.110083    0.069763    0.103974    0.175651            0.361630   \n","std      0.140668    0.116543    0.137310    0.221176            0.222905   \n","min      0.000000    0.000000    0.000000    0.000000            0.000000   \n","25%      0.022022    0.003620    0.018202    0.000000            0.188751   \n","50%      0.058783    0.025617    0.054480    0.083333            0.325071   \n","75%      0.143459    0.091888    0.137104    0.250000            0.515729   \n","max      1.000000    1.000000    1.000000    1.000000            1.000000   \n","\n","       market_distance       count  \n","count       269.000000  269.000000  \n","mean          0.329405    0.137546  \n","std           0.256354    0.207964  \n","min           0.000000    0.000000  \n","25%           0.132275    0.000000  \n","50%           0.240019    0.000000  \n","75%           0.478595    0.200000  \n","max           1.000000    1.000000  "],"text/html":["\n","  <div id=\"df-07d40bc7-cfb5-45d2-8d25-a89a9dfd1764\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>269.000000</td>\n","      <td>269.000000</td>\n","      <td>269.000000</td>\n","      <td>269.000000</td>\n","      <td>269.000000</td>\n","      <td>269.000000</td>\n","      <td>269.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.110083</td>\n","      <td>0.069763</td>\n","      <td>0.103974</td>\n","      <td>0.175651</td>\n","      <td>0.361630</td>\n","      <td>0.329405</td>\n","      <td>0.137546</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.140668</td>\n","      <td>0.116543</td>\n","      <td>0.137310</td>\n","      <td>0.221176</td>\n","      <td>0.222905</td>\n","      <td>0.256354</td>\n","      <td>0.207964</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.022022</td>\n","      <td>0.003620</td>\n","      <td>0.018202</td>\n","      <td>0.000000</td>\n","      <td>0.188751</td>\n","      <td>0.132275</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.058783</td>\n","      <td>0.025617</td>\n","      <td>0.054480</td>\n","      <td>0.083333</td>\n","      <td>0.325071</td>\n","      <td>0.240019</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.143459</td>\n","      <td>0.091888</td>\n","      <td>0.137104</td>\n","      <td>0.250000</td>\n","      <td>0.515729</td>\n","      <td>0.478595</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07d40bc7-cfb5-45d2-8d25-a89a9dfd1764')\"\n","              title=\"Convert this dataframe to an interactive 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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":9}],"source":["# 정규화된 결과를 다시 데이터 프레임으로 받기\n","df_sc = pd.DataFrame(data_sc, columns=data_drop.columns)\n","\n","df_sc.describe()"]},{"cell_type":"code","source":["## DBSCAN\n","\n","# 하이퍼파라미터 설정\n","eps = 0.22\n","min_samples = 2\n","\n","# 클러스터 선언\n","cluster = DBSCAN(eps=eps, min_samples=min_samples)"],"metadata":{"id":"__uYgv4DDuan"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1681298965811,"user_tz":-540,"elapsed":533,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"8fb98db2-e0d1-4407-db91-5ceae0a7faa2","id":"fRPqVh0AOg1q"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수    밤시간대총승객수      일일총승객수     환승가능노선수  starbucks_distance  \\\n","count  269.000000  269.000000  269.000000  269.000000          269.000000   \n","mean     0.110083    0.069763    0.103974    0.175651            0.361630   \n","std      0.140668    0.116543    0.137310    0.221176            0.222905   \n","min      0.000000    0.000000    0.000000    0.000000            0.000000   \n","25%      0.022022    0.003620    0.018202    0.000000            0.188751   \n","50%      0.058783    0.025617    0.054480    0.083333            0.325071   \n","75%      0.143459    0.091888    0.137104    0.250000            0.515729   \n","max      1.000000    1.000000    1.000000    1.000000            1.000000   \n","\n","       market_distance       count  \n","count       269.000000  269.000000  \n","mean          0.329405    0.137546  \n","std           0.256354    0.207964  \n","min           0.000000    0.000000  \n","25%           0.132275    0.000000  \n","50%           0.240019    0.000000  \n","75%           0.478595    0.200000  \n","max           1.000000    1.000000  "],"text/html":["\n","  <div id=\"df-7707cc40-0ec1-48bf-894e-f079a2a0a90f\">\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: 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<td>0.116543</td>\n","      <td>0.137310</td>\n","      <td>0.221176</td>\n","      <td>0.222905</td>\n","      <td>0.256354</td>\n","      <td>0.207964</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.022022</td>\n","      <td>0.003620</td>\n","      <td>0.018202</td>\n","      <td>0.000000</td>\n","      <td>0.188751</td>\n","      <td>0.132275</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.058783</td>\n","      <td>0.025617</td>\n","      <td>0.054480</td>\n","      <td>0.083333</td>\n","      <td>0.325071</td>\n","      <td>0.240019</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.143459</td>\n","      <td>0.091888</td>\n","      <td>0.137104</td>\n","      <td>0.250000</td>\n","      <td>0.515729</td>\n","      <td>0.478595</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7707cc40-0ec1-48bf-894e-f079a2a0a90f')\"\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 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axis=1)"],"metadata":{"id":"GrB_gkQHDP1G"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 클러스터 별 행정동 개수 파악 (잘됐는지)\n","df_cluster.groupby(by='cluster', axis=0, as_index=False)['행정동'].nunique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"1dP1WgZWEf0o","executionInfo":{"status":"ok","timestamp":1681298966371,"user_tz":-540,"elapsed":8,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"67421a07-6743-4fb5-f317-0755cd99239b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   cluster  행정동\n","0        0   11\n","1        1    4\n","2        2   10\n","3        3    8\n","4        4   13\n","5        5    7\n","6        6    5\n","7        7    9"],"text/html":["\n","  <div id=\"df-29b3e2ac-e68b-41b7-9e6f-34357befc473\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<td>6</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>9</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29b3e2ac-e68b-41b7-9e6f-34357befc473')\"\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 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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":397}]},{"cell_type":"code","source":["# 건대입구 사거리는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"Dva21r0JDUz9","executionInfo":{"status":"ok","timestamp":1681298970206,"user_tz":-540,"elapsed":7,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"b8014706-1f37-42f9-eb2b-d4c84e71bdd7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     낮시간대총승객수  밤시간대총승객수  일일총승객수  환승가능노선수  starbucks_distance  market_distance  \\\n","114       1.0       1.0     1.0      1.0            0.032412          0.15392   \n","\n","     count  cluster  행정동  버스정류장ARS번호  \n","114    1.0        0  화양동        5232  "],"text/html":["\n","  <div id=\"df-032e3ec0-90ed-491c-af16-cb7de5bc5aa5\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>114</th>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.032412</td>\n","      <td>0.15392</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-032e3ec0-90ed-491c-af16-cb7de5bc5aa5')\"\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","  <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-032e3ec0-90ed-491c-af16-cb7de5bc5aa5 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"6ZoMXI05D0G4","executionInfo":{"status":"ok","timestamp":1681298972443,"user_tz":-540,"elapsed":7,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"38db44bb-39bd-45ab-e516-5115eaa6413f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수    일일총승객수   환승가능노선수  starbucks_distance  \\\n","89  0.596284  0.461667  0.619866  0.333333            0.068637   \n","\n","    market_distance  count  cluster   행정동  버스정류장ARS번호  \n","89         0.112073    0.2        0  자양3동        5204  "],"text/html":["\n","  <div id=\"df-82ae2ecb-5892-4466-b317-8f27265b496b\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>89</th>\n","      <td>0.596284</td>\n","      <td>0.461667</td>\n","      <td>0.619866</td>\n","      <td>0.333333</td>\n","      <td>0.068637</td>\n","      <td>0.112073</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82ae2ecb-5892-4466-b317-8f27265b496b')\"\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","  <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-82ae2ecb-5892-4466-b317-8f27265b496b button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":399}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5169]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"HYjEtq1aFCwu","executionInfo":{"status":"ok","timestamp":1681298194341,"user_tz":-540,"elapsed":12,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"cde11055-7cf6-4bc7-e592-f01b5c3a77ed"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수    일일총승객수   환승가능노선수  starbucks_distance  \\\n","68  0.098162  0.025617  0.091449  0.083333            0.300286   \n","\n","    market_distance  count  cluster  행정동  버스정류장ARS번호  \n","68         0.942761    0.2        7  광장동        5169  "],"text/html":["\n","  <div id=\"df-99db15cf-8634-4010-b17d-4fd3f0404e06\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>68</th>\n","      <td>0.098162</td>\n","      <td>0.025617</td>\n","      <td>0.091449</td>\n","      <td>0.083333</td>\n","      <td>0.300286</td>\n","      <td>0.942761</td>\n","      <td>0.2</td>\n","      <td>7</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99db15cf-8634-4010-b17d-4fd3f0404e06')\"\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","  <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-99db15cf-8634-4010-b17d-4fd3f0404e06 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":363}]},{"cell_type":"code","source":["# 같은 클러스터 확인\n","df_cluster.loc[df_cluster['cluster'] == 7]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"CzR7_yFnEAdh","executionInfo":{"status":"ok","timestamp":1681298205208,"user_tz":-540,"elapsed":591,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"2d2649f2-cf38-4f77-a45c-b4cb08993334"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     낮시간대총승객수  밤시간대총승객수    일일총승객수   환승가능노선수  starbucks_distance  \\\n","67   0.013231  0.002320  0.010601  0.083333            0.280267   \n","68   0.098162  0.025617  0.091449  0.083333            0.300286   \n","155  0.171485  0.142565  0.173624  0.166667            0.304099   \n","\n","     market_distance  count  cluster  행정동  버스정류장ARS번호  \n","67          0.960558    0.2        7  광장동        5168  \n","68          0.942761    0.2        7  광장동        5169  \n","155         0.897547    0.2        7  광장동        5012  "],"text/html":["\n","  <div id=\"df-c8fac1dd-ee26-408d-8444-a473af1df7bd\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>67</th>\n","      <td>0.013231</td>\n","      <td>0.002320</td>\n","      <td>0.010601</td>\n","      <td>0.083333</td>\n","      <td>0.280267</td>\n","      <td>0.960558</td>\n","      <td>0.2</td>\n","      <td>7</td>\n","      <td>광장동</td>\n","      <td>5168</td>\n","    </tr>\n","    <tr>\n","      <th>68</th>\n","      <td>0.098162</td>\n","      <td>0.025617</td>\n","      <td>0.091449</td>\n","      <td>0.083333</td>\n","      <td>0.300286</td>\n","      <td>0.942761</td>\n","      <td>0.2</td>\n","      <td>7</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","    <tr>\n","      <th>155</th>\n","      <td>0.171485</td>\n","      <td>0.142565</td>\n","      <td>0.173624</td>\n","      <td>0.166667</td>\n","      <td>0.304099</td>\n","      <td>0.897547</td>\n","      <td>0.2</td>\n","      <td>7</td>\n","      <td>광장동</td>\n","      <td>5012</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8fac1dd-ee26-408d-8444-a473af1df7bd')\"\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","  <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: 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'starbucks_distance','market_distance', 'count', 'cluster', '행정동'], axis=1)"],"metadata":{"id":"piXB9wWBJ6dH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 행정동 데이터 넣어서 재클러스터링\n","\n","# 하이퍼파라미터 설정\n","eps = 0.8\n","min_samples = 3\n","\n","# 클러스터 선언\n","cluster = DBSCAN(eps=eps, min_samples=min_samples)"],"metadata":{"id":"hs39PTzQHQO_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# DBSCAN 진행 및 결과 저장\n","df_hgd['cluster'] = cluster.fit_predict(df_hgd)\n","\n","# 탐색을 위해 클러스터링 결과와 데이터 합병\n","df_cluster = pd.merge(df_hgd, data[['행정동', '버스정류장ARS번호']])"],"metadata":{"id":"MVr0nYyQHixj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 클러스터 별 행정동 개수 파악 (잘됐는지)\n","df_cluster.groupby(by='cluster', axis=0, as_index=False)['행정동'].nunique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"a6EY5AAcHk0w","executionInfo":{"status":"ok","timestamp":1681297676714,"user_tz":-540,"elapsed":5,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"a78365e7-d472-4c69-e759-7ea20f3adfe4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   cluster  행정동\n","0       -1   11"],"text/html":["\n","  <div id=\"df-8855accf-2868-4734-bee8-eda3e6a0c535\">\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>cluster</th>\n","      <th>행정동</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-1</td>\n","      <td>11</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8855accf-2868-4734-bee8-eda3e6a0c535')\"\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 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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":203}]},{"cell_type":"markdown","source":["# K-medoids"],"metadata":{"id":"oTdU7nUUH7Ih"}},{"cell_type":"code","source":["!pip install https://github.com/scikit-learn-contrib/scikit-learn-extra/archive/master.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QuYXSjGZMASq","executionInfo":{"status":"ok","timestamp":1682428530192,"user_tz":-540,"elapsed":61037,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"26eb0b74-142f-4b88-c94e-30d1a0d7f7aa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting https://github.com/scikit-learn-contrib/scikit-learn-extra/archive/master.zip\n","  Downloading https://github.com/scikit-learn-contrib/scikit-learn-extra/archive/master.zip\n","\u001b[2K     \u001b[32m\\\u001b[0m \u001b[32m740.1 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m \u001b[33m0:00:00\u001b[0m\n","\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.9/dist-packages (from scikit-learn-extra==0.3.0.dev0) (1.22.4)\n","Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn-extra==0.3.0.dev0) (1.10.1)\n","Requirement already satisfied: scikit-learn>=0.23.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn-extra==0.3.0.dev0) (1.2.2)\n","Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.23.0->scikit-learn-extra==0.3.0.dev0) (1.2.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.23.0->scikit-learn-extra==0.3.0.dev0) (3.1.0)\n","Building wheels for collected packages: scikit-learn-extra\n","  Building wheel for scikit-learn-extra (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for scikit-learn-extra: filename=scikit_learn_extra-0.3.0.dev0-cp39-cp39-linux_x86_64.whl size=1950460 sha256=3d2082c0316875d02a1d30deec462dc31452af9b13e731579d686144678ddd9f\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-1mt4jkbl/wheels/2a/50/ef/8f550e66b73775b0d138e1bc5ae39027b81675bfe6a0061099\n","Successfully built scikit-learn-extra\n","Installing collected packages: scikit-learn-extra\n","Successfully installed scikit-learn-extra-0.3.0.dev0\n"]}]},{"cell_type":"code","source":["from sklearn_extra.cluster import KMedoids"],"metadata":{"id":"Mp5wJ5M-MBJY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 정규화된 결과를 다시 데이터 프레임으로 받기\n","df_sc = pd.DataFrame(data_sc, columns=data_drop.columns)\n","\n","# 클러스터링\n","cluster = KMedoids(random_state=22, n_clusters=9).fit(df_sc)\n","\n","# 결과 저장\n","labels = cluster.labels_"],"metadata":{"id":"bj2Hd9JNMU9h"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 탐색을 위해 클러스터링 결과와 데이터 합병\n","df_sc['cluster'] = labels\n","\n","# 탐색을 위해 클러스터링 결과와 데이터 합병\n","df_cluster = pd.concat([df_sc, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"id":"5BfF_dIWNG16"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 클러스터 별 행정동 개수 파악 (잘됐는지)\n","df_cluster.groupby(by='cluster', axis=0, as_index=False)['행정동'].nunique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":332},"id":"7t1G1ns-NJwL","executionInfo":{"status":"ok","timestamp":1681300451015,"user_tz":-540,"elapsed":511,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"3d314bb4-0ed4-43ce-f5c0-88fd51f2bbc3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   cluster  행정동\n","0        0   11\n","1        1    4\n","2        2    9\n","3        3    9\n","4        4   10\n","5        5    9\n","6        6    8\n","7        7    8\n","8        8    8"],"text/html":["\n","  <div id=\"df-0d6d0b7c-9211-4187-807b-a97ce50e7052\">\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>cluster</th>\n","      <th>행정동</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>7</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>8</td>\n","      <td>8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d6d0b7c-9211-4187-807b-a97ce50e7052')\"\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 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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":540}]},{"cell_type":"code","source":["# 건대입구 사거리는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"AzcXSnFANXpB","executionInfo":{"status":"ok","timestamp":1681299014641,"user_tz":-540,"elapsed":498,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"b09f627e-c717-411e-f082-e6837737d91a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     낮시간대총승객수  밤시간대총승객수  일일총승객수  환승가능노선수  starbucks_distance  market_distance  \\\n","114       1.0       1.0     1.0      1.0            0.032412          0.15392   \n","\n","     count  cluster  행정동  버스정류장ARS번호  \n","114    1.0        0  화양동        5232  "],"text/html":["\n","  <div id=\"df-ce80cc0f-eb73-4db8-adb6-d86ffdb9a1b8\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>114</th>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.032412</td>\n","      <td>0.15392</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce80cc0f-eb73-4db8-adb6-d86ffdb9a1b8')\"\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","  <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-ce80cc0f-eb73-4db8-adb6-d86ffdb9a1b8 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":407}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"dZF3RnFgOEUI","executionInfo":{"status":"ok","timestamp":1681299015396,"user_tz":-540,"elapsed":7,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"a956f5f2-1ed9-4a8c-ab02-aaa0a6e15404"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수    일일총승객수   환승가능노선수  starbucks_distance  \\\n","89  0.596284  0.461667  0.619866  0.333333            0.068637   \n","\n","    market_distance  count  cluster   행정동  버스정류장ARS번호  \n","89         0.112073    0.2        0  자양3동        5204  "],"text/html":["\n","  <div id=\"df-cfb4ee82-fe52-4d56-b259-2ad4ee848b62\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>89</th>\n","      <td>0.596284</td>\n","      <td>0.461667</td>\n","      <td>0.619866</td>\n","      <td>0.333333</td>\n","      <td>0.068637</td>\n","      <td>0.112073</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfb4ee82-fe52-4d56-b259-2ad4ee848b62')\"\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","  <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-cfb4ee82-fe52-4d56-b259-2ad4ee848b62 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":408}]},{"cell_type":"code","source":["# 같은 클러스터 확인\n","df_cluster.loc[df_cluster['cluster'] == 0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"5sl-ab6EOGUL","executionInfo":{"status":"ok","timestamp":1681299018963,"user_tz":-540,"elapsed":551,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"1731f691-b6c1-45fe-b069-47a264b603bb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     낮시간대총승객수  밤시간대총승객수    일일총승객수   환승가능노선수  starbucks_distance  \\\n","1    0.446167  0.348617  0.456484  0.583333            0.309819   \n","2    0.320975  0.271394  0.318688  0.583333            0.369876   \n","3    0.434786  0.395953  0.426240  0.500000            0.279314   \n","4    0.532607  0.637461  0.562398  0.500000            0.203051   \n","6    0.565356  0.451364  0.517181  0.416667            0.050524   \n","7    0.767029  0.598385  0.754963  0.583333            0.182078   \n","45   0.146930  0.064043  0.121532  0.666667            0.029552   \n","46   0.197348  0.179321  0.186901  0.666667            0.167779   \n","50   0.435725  0.150176  0.382453  0.666667            0.452812   \n","53   0.690491  0.458233  0.631158  0.666667            0.188751   \n","69   0.422495  0.210507  0.370803  0.666667            0.128694   \n","78   0.343994  0.193707  0.358564  0.666667            0.407054   \n","83   0.233853  0.164841  0.226397  0.583333            0.513823   \n","89   0.596284  0.461667  0.619866  0.333333            0.068637   \n","90   0.352160  0.169018  0.298559  0.416667            0.132507   \n","99   0.166989  0.194821  0.166542  0.750000            0.265968   \n","100  0.572640  0.465565  0.601952  0.333333            0.098189   \n","102  0.270728  0.255987  0.273759  0.833333            0.656816   \n","108  0.286064  0.129293  0.250505  0.333333            0.279314   \n","109  0.196352  0.133469  0.175931  0.916667            0.046711   \n","110  0.095203  0.054761  0.087631  0.916667            0.226883   \n","111  0.145934  0.161036  0.136297  0.750000            0.012393   \n","112  0.295112  0.305365  0.305365  0.666667            0.191611   \n","114  1.000000  1.000000  1.000000  1.000000            0.032412   \n","115  0.201474  0.198069  0.192807  1.000000            0.012393   \n","130  0.197263  0.164099  0.224978  0.416667            0.282173   \n","131  0.066124  0.023482  0.045367  0.750000            0.222116   \n","132  0.167729  0.059217  0.137104  0.666667            0.085796   \n","133  0.336141  0.381938  0.331076  0.750000            0.045758   \n","150  0.157287  0.154817  0.152527  0.666667            0.158246   \n","160  0.022079  0.014665  0.022401  0.916667            0.030505   \n","\n","     market_distance  count  cluster   행정동  버스정류장ARS번호  \n","1           0.343915    0.6        0  중곡1동        5001  \n","2           0.363636    0.6        0   군자동        5002  \n","3           0.266474    0.2        0  중곡2동        5003  \n","4           0.302549    0.2        0    능동        5004  \n","6           0.333814    0.2        0  구의2동        5006  \n","7           0.264069    0.2        0    능동        5007  \n","45          0.527177    0.6        0    능동        5140  \n","46          0.436748    0.6        0    능동        5141  \n","50          0.418951    0.2        0  구의1동        5145  \n","53          0.285233    0.2        0  구의1동        5148  \n","69          0.255411    0.2        0  자양1동        5175  \n","78          0.139490    0.4        0  자양1동        5188  \n","83          0.197691    0.4        0  자양2동        5194  \n","89          0.112073    0.2        0  자양3동        5204  \n","90          0.310726    0.4        0  자양3동        5205  \n","99          0.094276    0.8        0  자양4동        5217  \n","100         0.099567    0.2        0  자양4동        5218  \n","102         0.134680    0.4        0  자양4동        5220  \n","108         0.208754    0.6        0   화양동        5226  \n","109         0.213083    1.0        0   화양동        5227  \n","110         0.101010    1.0        0   화양동        5228  \n","111         0.147186    0.8        0   화양동        5229  \n","112         0.101491    0.8        0   화양동        5230  \n","114         0.153920    1.0        0   화양동        5232  \n","115         0.189033    1.0        0   화양동        5234  \n","130         0.223184    0.6        0   군자동        5250  \n","131         0.388648    0.6        0   군자동        5251  \n","132         0.546417    0.6        0   군자동        5252  \n","133         0.396344    0.6        0   군자동        5253  \n","150         0.139490    0.4        0  자양2동        5192  \n","160         0.157768    1.0        0   화양동        5499  "],"text/html":["\n","  <div id=\"df-ce70db5e-feb2-4d41-88f6-59472d3db1ab\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>0.446167</td>\n","      <td>0.348617</td>\n","      <td>0.456484</td>\n","      <td>0.583333</td>\n","      <td>0.309819</td>\n","      <td>0.343915</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>중곡1동</td>\n","      <td>5001</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.320975</td>\n","      <td>0.271394</td>\n","      <td>0.318688</td>\n","      <td>0.583333</td>\n","      <td>0.369876</td>\n","      <td>0.363636</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>군자동</td>\n","      <td>5002</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.434786</td>\n","      <td>0.395953</td>\n","      <td>0.426240</td>\n","      <td>0.500000</td>\n","      <td>0.279314</td>\n","      <td>0.266474</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>중곡2동</td>\n","      <td>5003</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.532607</td>\n","      <td>0.637461</td>\n","      <td>0.562398</td>\n","      <td>0.500000</td>\n","      <td>0.203051</td>\n","      <td>0.302549</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>능동</td>\n","      <td>5004</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.565356</td>\n","      <td>0.451364</td>\n","      <td>0.517181</td>\n","      <td>0.416667</td>\n","      <td>0.050524</td>\n","      <td>0.333814</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>구의2동</td>\n","      <td>5006</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.767029</td>\n","      <td>0.598385</td>\n","      <td>0.754963</td>\n","      <td>0.583333</td>\n","      <td>0.182078</td>\n","      <td>0.264069</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>능동</td>\n","      <td>5007</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>0.146930</td>\n","      <td>0.064043</td>\n","      <td>0.121532</td>\n","      <td>0.666667</td>\n","      <td>0.029552</td>\n","      <td>0.527177</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>능동</td>\n","      <td>5140</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>0.197348</td>\n","      <td>0.179321</td>\n","      <td>0.186901</td>\n","      <td>0.666667</td>\n","      <td>0.167779</td>\n","      <td>0.436748</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>능동</td>\n","      <td>5141</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>0.435725</td>\n","      <td>0.150176</td>\n","      <td>0.382453</td>\n","      <td>0.666667</td>\n","      <td>0.452812</td>\n","      <td>0.418951</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>구의1동</td>\n","      <td>5145</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>0.690491</td>\n","      <td>0.458233</td>\n","      <td>0.631158</td>\n","      <td>0.666667</td>\n","      <td>0.188751</td>\n","      <td>0.285233</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>구의1동</td>\n","      <td>5148</td>\n","    </tr>\n","    <tr>\n","      <th>69</th>\n","      <td>0.422495</td>\n","      <td>0.210507</td>\n","      <td>0.370803</td>\n","      <td>0.666667</td>\n","      <td>0.128694</td>\n","      <td>0.255411</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>자양1동</td>\n","      <td>5175</td>\n","    </tr>\n","    <tr>\n","      <th>78</th>\n","      <td>0.343994</td>\n","      <td>0.193707</td>\n","      <td>0.358564</td>\n","      <td>0.666667</td>\n","      <td>0.407054</td>\n","      <td>0.139490</td>\n","      <td>0.4</td>\n","      <td>0</td>\n","      <td>자양1동</td>\n","      <td>5188</td>\n","    </tr>\n","    <tr>\n","      <th>83</th>\n","      <td>0.233853</td>\n","      <td>0.164841</td>\n","      <td>0.226397</td>\n","      <td>0.583333</td>\n","      <td>0.513823</td>\n","      <td>0.197691</td>\n","      <td>0.4</td>\n","      <td>0</td>\n","      <td>자양2동</td>\n","      <td>5194</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>0.596284</td>\n","      <td>0.461667</td>\n","      <td>0.619866</td>\n","      <td>0.333333</td>\n","      <td>0.068637</td>\n","      <td>0.112073</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","    <tr>\n","      <th>90</th>\n","      <td>0.352160</td>\n","      <td>0.169018</td>\n","      <td>0.298559</td>\n","      <td>0.416667</td>\n","      <td>0.132507</td>\n","      <td>0.310726</td>\n","      <td>0.4</td>\n","      <td>0</td>\n","      <td>자양3동</td>\n","      <td>5205</td>\n","    </tr>\n","    <tr>\n","      <th>99</th>\n","      <td>0.166989</td>\n","      <td>0.194821</td>\n","      <td>0.166542</td>\n","      <td>0.750000</td>\n","      <td>0.265968</td>\n","      <td>0.094276</td>\n","      <td>0.8</td>\n","      <td>0</td>\n","      <td>자양4동</td>\n","      <td>5217</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>0.572640</td>\n","      <td>0.465565</td>\n","      <td>0.601952</td>\n","      <td>0.333333</td>\n","      <td>0.098189</td>\n","      <td>0.099567</td>\n","      <td>0.2</td>\n","      <td>0</td>\n","      <td>자양4동</td>\n","      <td>5218</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>0.270728</td>\n","      <td>0.255987</td>\n","      <td>0.273759</td>\n","      <td>0.833333</td>\n","      <td>0.656816</td>\n","      <td>0.134680</td>\n","      <td>0.4</td>\n","      <td>0</td>\n","      <td>자양4동</td>\n","      <td>5220</td>\n","    </tr>\n","    <tr>\n","      <th>108</th>\n","      <td>0.286064</td>\n","      <td>0.129293</td>\n","      <td>0.250505</td>\n","      <td>0.333333</td>\n","      <td>0.279314</td>\n","      <td>0.208754</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5226</td>\n","    </tr>\n","    <tr>\n","      <th>109</th>\n","      <td>0.196352</td>\n","      <td>0.133469</td>\n","      <td>0.175931</td>\n","      <td>0.916667</td>\n","      <td>0.046711</td>\n","      <td>0.213083</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5227</td>\n","    </tr>\n","    <tr>\n","      <th>110</th>\n","      <td>0.095203</td>\n","      <td>0.054761</td>\n","      <td>0.087631</td>\n","      <td>0.916667</td>\n","      <td>0.226883</td>\n","      <td>0.101010</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5228</td>\n","    </tr>\n","    <tr>\n","      <th>111</th>\n","      <td>0.145934</td>\n","      <td>0.161036</td>\n","      <td>0.136297</td>\n","      <td>0.750000</td>\n","      <td>0.012393</td>\n","      <td>0.147186</td>\n","      <td>0.8</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5229</td>\n","    </tr>\n","    <tr>\n","      <th>112</th>\n","      <td>0.295112</td>\n","      <td>0.305365</td>\n","      <td>0.305365</td>\n","      <td>0.666667</td>\n","      <td>0.191611</td>\n","      <td>0.101491</td>\n","      <td>0.8</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5230</td>\n","    </tr>\n","    <tr>\n","      <th>114</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.032412</td>\n","      <td>0.153920</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","    <tr>\n","      <th>115</th>\n","      <td>0.201474</td>\n","      <td>0.198069</td>\n","      <td>0.192807</td>\n","      <td>1.000000</td>\n","      <td>0.012393</td>\n","      <td>0.189033</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5234</td>\n","    </tr>\n","    <tr>\n","      <th>130</th>\n","      <td>0.197263</td>\n","      <td>0.164099</td>\n","      <td>0.224978</td>\n","      <td>0.416667</td>\n","      <td>0.282173</td>\n","      <td>0.223184</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>군자동</td>\n","      <td>5250</td>\n","    </tr>\n","    <tr>\n","      <th>131</th>\n","      <td>0.066124</td>\n","      <td>0.023482</td>\n","      <td>0.045367</td>\n","      <td>0.750000</td>\n","      <td>0.222116</td>\n","      <td>0.388648</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>군자동</td>\n","      <td>5251</td>\n","    </tr>\n","    <tr>\n","      <th>132</th>\n","      <td>0.167729</td>\n","      <td>0.059217</td>\n","      <td>0.137104</td>\n","      <td>0.666667</td>\n","      <td>0.085796</td>\n","      <td>0.546417</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>군자동</td>\n","      <td>5252</td>\n","    </tr>\n","    <tr>\n","      <th>133</th>\n","      <td>0.336141</td>\n","      <td>0.381938</td>\n","      <td>0.331076</td>\n","      <td>0.750000</td>\n","      <td>0.045758</td>\n","      <td>0.396344</td>\n","      <td>0.6</td>\n","      <td>0</td>\n","      <td>군자동</td>\n","      <td>5253</td>\n","    </tr>\n","    <tr>\n","      <th>150</th>\n","      <td>0.157287</td>\n","      <td>0.154817</td>\n","      <td>0.152527</td>\n","      <td>0.666667</td>\n","      <td>0.158246</td>\n","      <td>0.139490</td>\n","      <td>0.4</td>\n","      <td>0</td>\n","      <td>자양2동</td>\n","      <td>5192</td>\n","    </tr>\n","    <tr>\n","      <th>160</th>\n","      <td>0.022079</td>\n","      <td>0.014665</td>\n","      <td>0.022401</td>\n","      <td>0.916667</td>\n","      <td>0.030505</td>\n","      <td>0.157768</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>화양동</td>\n","      <td>5499</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce70db5e-feb2-4d41-88f6-59472d3db1ab')\"\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 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'버스정류장ARS번호'].values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6xixQNaEOK7i","executionInfo":{"status":"ok","timestamp":1681300565440,"user_tz":-540,"elapsed":883,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"e9f82235-b383-4415-a241-60a2b0e2bd96"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5001, 5002, 5003, 5004, 5006, 5007, 5140, 5141, 5145, 5148, 5175,\n","       5188, 5194, 5204, 5205, 5217, 5218, 5220, 5226, 5227, 5228, 5229,\n","       5230, 5232, 5234, 5250, 5251, 5252, 5253, 5192, 5499])"]},"metadata":{},"execution_count":551}]},{"cell_type":"code","source":["# 클러스터링 결과 추출\n","df_re = df_cluster.loc[df_cluster['cluster'] == 0].reset_index(drop=True)\n","df_re = pd.concat([df_re, data[['대규모점포', '사업체수', '종사자수', '낮시간대 생활인구', '밤시간대 생활인구', '총생활인구수', '65세 이상 생활인구 비율', '1020 생활인구 비율']]], axis=1, join='inner')\n","\n","# 정규화\n","\n","scaler = MinMaxScaler()\n","\n","scaler.fit(df_re.drop(['행정동', '버스정류장ARS번호'], axis=1))\n","data_re = scaler.transform(df_re.drop(['행정동', '버스정류장ARS번호'], axis=1))\n","\n","# 정규화된 결과를 다시 데이터 프레임으로 받기\n","df_re_sc = pd.DataFrame(data_re, columns=df_re.drop(['행정동', '버스정류장ARS번호'], axis=1).columns)"],"metadata":{"id":"WLTS0LSAO5Ev"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 행정동 데이터 넣어서 재클러스터링\n","df_hgd = df_re_sc.drop(['대규모점포', '사업체수', '65세 이상 생활인구 비율', '밤시간대 생활인구', '낮시간대 생활인구'], axis=1)\n","# df_hgd = df_re_sc.copy()\n","\n","# 클러스터링\n","cluster = KMedoids(random_state=22, n_clusters=5).fit(df_hgd)\n","\n","# 결과 저장\n","labels = cluster.labels_\n","\n","# 탐색을 위해 클러스터링 결과와 데이터 합병\n","df_hgd['cluster'] = labels\n","\n","# 탐색을 위해 클러스터링 결과와 데이터 합병\n","df_re_cluster = pd.concat([df_hgd, df_re[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"id":"hTwzqMiWPNLR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 클러스터 별 행정동 개수 파악 (잘됐는지)\n","df_re_cluster.groupby(by='cluster', axis=0, as_index=False)['행정동'].nunique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"85QRFwQGPiiT","executionInfo":{"status":"ok","timestamp":1681302749245,"user_tz":-540,"elapsed":9,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"b69d2c3a-e453-4d5c-9ad2-254367b975de"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   cluster  행정동\n","0        0    4\n","1        1    3\n","2        2    3\n","3        3    7\n","4        4    2"],"text/html":["\n","  <div id=\"df-39ee6a7d-d964-4326-8916-dfff793fdd89\">\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","    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5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"kAzZ6JIkPjI7","executionInfo":{"status":"ok","timestamp":1681302752113,"user_tz":-540,"elapsed":8,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"faeaf40c-115b-4236-f10b-d63eca58f7d6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수  일일총승객수  환승가능노선수  starbucks_distance  market_distance  \\\n","23       1.0       1.0     1.0      1.0            0.031065         0.131915   \n","\n","    count  cluster      종사자수    총생활인구수  1020 생활인구 비율  행정동  버스정류장ARS번호  \n","23    1.0        4  0.490343  0.504929      0.193334  화양동        5232  "],"text/html":["\n","  <div id=\"df-ccd8d1b8-cf42-4cc1-872b-e4cff696db37\">\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","      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<td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccd8d1b8-cf42-4cc1-872b-e4cff696db37')\"\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","     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5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1681302753744,"user_tz":-540,"elapsed":512,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"d6428b9e-d514-46a7-ffe1-c842b449d746","id":"Jl6GsA7EQIFd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수    일일총승객수  환승가능노선수  starbucks_distance  \\\n","13  0.587169  0.453655  0.611155      0.0            0.087278   \n","\n","    market_distance  count  cluster      종사자수    총생활인구수  1020 생활인구 비율   행정동  \\\n","13         0.039362    0.0        3  0.279128  0.000482      0.128775  자양3동   \n","\n","    버스정류장ARS번호  \n","13        5204  "],"text/html":["\n","  <div id=\"df-ce6ee0b6-8cc4-4724-b5e9-cdeabb155517\">\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","    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<td>0.128775</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce6ee0b6-8cc4-4724-b5e9-cdeabb155517')\"\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 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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":606}]},{"cell_type":"code","source":["# 같은 클러스터 확인\n","df_re_cluster.loc[df_re_cluster['cluster'] == 3]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"ef3Pd68kQIp5","executionInfo":{"status":"ok","timestamp":1681302807642,"user_tz":-540,"elapsed":646,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"9870ae74-9770-47f2-c6ea-fcc899a8212f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수    일일총승객수  환승가능노선수  starbucks_distance  \\\n","2   0.422025  0.386963  0.413092    0.250            0.414201   \n","3   0.522054  0.632065  0.552371    0.250            0.295858   \n","4   0.555543  0.443199  0.506118    0.125            0.059172   \n","5   0.761769  0.592408  0.749348    0.375            0.263314   \n","9   0.683503  0.450170  0.622707    0.500            0.273669   \n","10  0.409456  0.198757  0.356385    0.500            0.180473   \n","13  0.587169  0.453655  0.611155    0.000            0.087278   \n","16  0.562991  0.457611  0.592831    0.000            0.133136   \n","\n","    market_distance  count  cluster      종사자수    총생활인구수  1020 생활인구 비율   행정동  \\\n","2          0.380851    0.0        3  1.000000  0.693046      1.000000  중곡2동   \n","3          0.460638    0.0        3  0.490343  0.504929      0.193334    능동   \n","4          0.529787    0.0        3  0.251298  0.027756      0.439859  구의2동   \n","5          0.375532    0.0        3  0.490343  0.504929      0.193334    능동   \n","9          0.422340    0.0        3  0.279128  0.000482      0.128775  구의1동   \n","10         0.356383    0.0        3  0.279128  0.000482      0.128775  자양1동   \n","13         0.039362    0.0        3  0.279128  0.000482      0.128775  자양3동   \n","16         0.011702    0.0        3  0.000000  0.000000      0.000000  자양4동   \n","\n","    버스정류장ARS번호  \n","2         5003  \n","3         5004  \n","4         5006  \n","5         5007  \n","9         5148  \n","10        5175  \n","13        5204  \n","16        5218  "],"text/html":["\n","  <div 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<td>0.386963</td>\n","      <td>0.413092</td>\n","      <td>0.250</td>\n","      <td>0.414201</td>\n","      <td>0.380851</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>1.000000</td>\n","      <td>0.693046</td>\n","      <td>1.000000</td>\n","      <td>중곡2동</td>\n","      <td>5003</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.522054</td>\n","      <td>0.632065</td>\n","      <td>0.552371</td>\n","      <td>0.250</td>\n","      <td>0.295858</td>\n","      <td>0.460638</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>0.490343</td>\n","      <td>0.504929</td>\n","      <td>0.193334</td>\n","      <td>능동</td>\n","      <td>5004</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.555543</td>\n","      <td>0.443199</td>\n","      <td>0.506118</td>\n","      <td>0.125</td>\n","      <td>0.059172</td>\n","      <td>0.529787</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>0.251298</td>\n","      <td>0.027756</td>\n","      <td>0.439859</td>\n","      <td>구의2동</td>\n","      <td>5006</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.761769</td>\n","      <td>0.592408</td>\n","      <td>0.749348</td>\n","      <td>0.375</td>\n","      <td>0.263314</td>\n","      <td>0.375532</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>0.490343</td>\n","      <td>0.504929</td>\n","      <td>0.193334</td>\n","      <td>능동</td>\n","      <td>5007</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.683503</td>\n","      <td>0.450170</td>\n","      <td>0.622707</td>\n","      <td>0.500</td>\n","      <td>0.273669</td>\n","      <td>0.422340</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>0.279128</td>\n","      <td>0.000482</td>\n","      <td>0.128775</td>\n","      <td>구의1동</td>\n","      <td>5148</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>0.409456</td>\n","      <td>0.198757</td>\n","      <td>0.356385</td>\n","      <td>0.500</td>\n","      <td>0.180473</td>\n","      <td>0.356383</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>0.279128</td>\n","      <td>0.000482</td>\n","      <td>0.128775</td>\n","      <td>자양1동</td>\n","      <td>5175</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0.587169</td>\n","      <td>0.453655</td>\n","      <td>0.611155</td>\n","      <td>0.000</td>\n","      <td>0.087278</td>\n","      <td>0.039362</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>0.279128</td>\n","      <td>0.000482</td>\n","      <td>0.128775</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0.562991</td>\n","      <td>0.457611</td>\n","      <td>0.592831</td>\n","      <td>0.000</td>\n","      <td>0.133136</td>\n","      <td>0.011702</td>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>자양4동</td>\n","      <td>5218</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17a799d0-9828-468c-8db1-ba223ec3d716')\"\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 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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":609}]},{"cell_type":"code","source":["df_re_cluster.loc[df_cluster['cluster'] == 2, '버스정류장ARS번호'].values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xLdxqOiYQOwO","executionInfo":{"status":"ok","timestamp":1681301071427,"user_tz":-540,"elapsed":601,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"4b00f130-9897-4608-ad45-287b73c4c557"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5194, 5204, 5205, 5217, 5218, 5220, 5230, 5234, 5192, 5499])"]},"metadata":{},"execution_count":577}]},{"cell_type":"markdown","source":["# PyCaret"],"metadata":{"id":"-2h5z32mWmAp"}},{"cell_type":"code","source":["data.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":582},"id":"VtVmnUB3FVrD","executionInfo":{"status":"ok","timestamp":1681565000564,"user_tz":-540,"elapsed":512,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"4325928c-0447-44e3-fcc9-93d7a691a9bb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   표준버스정류장ID  버스정류장ARS번호      낮시간대총승객수     밤시간대총승객수        일일총승객수  대규모점포  \\\n","0  101000252        5118  14247.166667  1726.166667  30186.000000    0.0   \n","1  104000001        5001  15681.333333  3756.500000  39580.666667    0.0   \n","2  104000002        5002  11281.500000  2924.500000  27634.833333    1.0   \n","3  104000003        5003  15281.500000  4266.833333  36958.000000    0.0   \n","4  104000004        5004  18719.500000  6868.500000  48762.500000    1.0   \n","\n","     사업체수    종사자수     낮시간대 생활인구     밤시간대 생활인구  환승가능노선수  starbucks_distance  \\\n","0  2350.0  7380.0  9.197688e+07  1.254655e+08      3.0          740.041174   \n","1  2152.0  6363.0  8.353262e+07  9.590508e+07      7.0          350.338493   \n","2  2389.0  9834.0  1.168754e+08  1.109856e+08      7.0          413.992846   \n","3  2350.0  7380.0  9.197688e+07  1.254655e+08      6.0          318.242961   \n","4  1214.0  6229.0  1.030211e+08  7.910175e+07      6.0          238.122978   \n","\n","   market_distance  count        총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율   행정동  \n","0        93.098861    1.0  3.238924e+08        0.146777      0.232630  중곡2동  \n","1       723.257352    3.0  2.708308e+08        0.145448      0.223181  중곡1동  \n","2       764.569212    3.0  3.436799e+08        0.115274      0.350698   군자동  \n","3       562.888420    1.0  3.238924e+08        0.146777      0.232630  중곡2동  \n","4       637.437346    1.0  2.736997e+08        0.129653      0.268713    능동  "],"text/html":["\n","  <div 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비율</th>\n","      <th>행정동</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>101000252</td>\n","      <td>5118</td>\n","      <td>14247.166667</td>\n","      <td>1726.166667</td>\n","      <td>30186.000000</td>\n","      <td>0.0</td>\n","      <td>2350.0</td>\n","      <td>7380.0</td>\n","      <td>9.197688e+07</td>\n","      <td>1.254655e+08</td>\n","      <td>3.0</td>\n","      <td>740.041174</td>\n","      <td>93.098861</td>\n","      <td>1.0</td>\n","      <td>3.238924e+08</td>\n","      <td>0.146777</td>\n","      <td>0.232630</td>\n","      <td>중곡2동</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>104000001</td>\n","      <td>5001</td>\n","      <td>15681.333333</td>\n","      <td>3756.500000</td>\n","      <td>39580.666667</td>\n","      <td>0.0</td>\n","      <td>2152.0</td>\n","      <td>6363.0</td>\n","      <td>8.353262e+07</td>\n","      <td>9.590508e+07</td>\n","      <td>7.0</td>\n","      <td>350.338493</td>\n","      <td>723.257352</td>\n","      <td>3.0</td>\n","      <td>2.708308e+08</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>중곡1동</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>104000002</td>\n","      <td>5002</td>\n","      <td>11281.500000</td>\n","      <td>2924.500000</td>\n","      <td>27634.833333</td>\n","      <td>1.0</td>\n","      <td>2389.0</td>\n","      <td>9834.0</td>\n","      <td>1.168754e+08</td>\n","      <td>1.109856e+08</td>\n","      <td>7.0</td>\n","      <td>413.992846</td>\n","      <td>764.569212</td>\n","      <td>3.0</td>\n","      <td>3.436799e+08</td>\n","      <td>0.115274</td>\n","      <td>0.350698</td>\n","      <td>군자동</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>104000003</td>\n","      <td>5003</td>\n","      <td>15281.500000</td>\n","      <td>4266.833333</td>\n","      <td>36958.000000</td>\n","      <td>0.0</td>\n","      <td>2350.0</td>\n","      <td>7380.0</td>\n","      <td>9.197688e+07</td>\n","      <td>1.254655e+08</td>\n","      <td>6.0</td>\n","      <td>318.242961</td>\n","      <td>562.888420</td>\n","      <td>1.0</td>\n","      <td>3.238924e+08</td>\n","      <td>0.146777</td>\n","      <td>0.232630</td>\n","      <td>중곡2동</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>104000004</td>\n","      <td>5004</td>\n","      <td>18719.500000</td>\n","      <td>6868.500000</td>\n","      <td>48762.500000</td>\n","      <td>1.0</td>\n","      <td>1214.0</td>\n","      <td>6229.0</td>\n","      <td>1.030211e+08</td>\n","      <td>7.910175e+07</td>\n","      <td>6.0</td>\n","      <td>238.122978</td>\n","      <td>637.437346</td>\n","      <td>1.0</td>\n","      <td>2.736997e+08</td>\n","      <td>0.129653</td>\n","      <td>0.268713</td>\n","      <td>능동</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-ade7fb57-6a1e-43c5-af33-305e984f1fdf');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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'버스정류장ARS번호', '행정동', '대규모점포', '사업체수', '종사자수', '낮시간대 생활인구', '밤시간대 생활인구', '총생활인구수', '65세 이상 생활인구 비율', '1020 생활인구 비율'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"id":"Flvt82-kFnNY","executionInfo":{"status":"ok","timestamp":1682428533321,"user_tz":-540,"elapsed":547,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"2d8864e0-cb6e-419f-b306-ac58eacb8ebd"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7fc028c2fa60>"],"text/html":["<style type=\"text/css\">\n","#T_ec102_row5_col1, #T_ec102_row9_col1 {\n","  background-color: lightgreen;\n","}\n","</style>\n","<table id=\"T_ec102\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_ec102_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n","      <th id=\"T_ec102_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_ec102_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_ec102_row0_col0\" class=\"data row0 col0\" >Session id</td>\n","      <td id=\"T_ec102_row0_col1\" class=\"data row0 col1\" >22</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_ec102_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n","      <td id=\"T_ec102_row1_col1\" class=\"data row1 col1\" >(269, 18)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_ec102_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n","      <td id=\"T_ec102_row2_col1\" class=\"data row2 col1\" >(269, 7)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_ec102_row3_col0\" class=\"data row3 col0\" >Ignore features</td>\n","      <td id=\"T_ec102_row3_col1\" class=\"data row3 col1\" >11</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_ec102_row4_col0\" class=\"data row4 col0\" >Numeric features</td>\n","      <td id=\"T_ec102_row4_col1\" class=\"data row4 col1\" >7</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_ec102_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n","      <td id=\"T_ec102_row5_col1\" class=\"data row5 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_ec102_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n","      <td id=\"T_ec102_row6_col1\" class=\"data row6 col1\" >simple</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_ec102_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n","      <td id=\"T_ec102_row7_col1\" class=\"data row7 col1\" >mean</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_ec102_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n","      <td id=\"T_ec102_row8_col1\" class=\"data row8 col1\" >mode</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_ec102_row9_col0\" class=\"data row9 col0\" >Normalize</td>\n","      <td id=\"T_ec102_row9_col1\" class=\"data row9 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n","      <td id=\"T_ec102_row10_col0\" class=\"data row10 col0\" >Normalize method</td>\n","      <td id=\"T_ec102_row10_col1\" class=\"data row10 col1\" >zscore</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n","      <td id=\"T_ec102_row11_col0\" class=\"data row11 col0\" >CPU Jobs</td>\n","      <td id=\"T_ec102_row11_col1\" class=\"data row11 col1\" >-1</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n","      <td id=\"T_ec102_row12_col0\" class=\"data row12 col0\" >Use GPU</td>\n","      <td id=\"T_ec102_row12_col1\" class=\"data row12 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n","      <td id=\"T_ec102_row13_col0\" class=\"data row13 col0\" >Log Experiment</td>\n","      <td id=\"T_ec102_row13_col1\" class=\"data row13 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n","      <td id=\"T_ec102_row14_col0\" class=\"data row14 col0\" >Experiment Name</td>\n","      <td id=\"T_ec102_row14_col1\" class=\"data row14 col1\" >cluster-default-name</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_ec102_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n","      <td id=\"T_ec102_row15_col0\" class=\"data row15 col0\" >USI</td>\n","      <td id=\"T_ec102_row15_col1\" class=\"data row15 col1\" >7dd1</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}}]},{"cell_type":"code","source":["# 모델 생성 (Functional API)\n","optics = create_model('optics')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["e1ddc47f7e63404c91d86a139c4abde2","4bdcc710d3bc44b7a7016d09600e9c4c","8bf87f0847694e568baf909e4556f978","3d665a928e974381b9247883b088d6dd","fab4e5ab71c24755abef7b0e90c36261","6e2b2d3925f0436fbd8110f596e73c3a","2b6a1afaaff8452cace085545e126c6a","2ed99dad8a7d4c95a32d910669cfe268","4f2e0a86b69f4677a3924511d8363ed5","fe671398493d4b23b70e1993e00433bb","e2d77213def345ca9599188e38a6815d"]},"id":"zhKqn9-hGLdt","executionInfo":{"status":"ok","timestamp":1682428534554,"user_tz":-540,"elapsed":1242,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"7890a248-f595-47f0-bdb0-5500ab44bdef"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7fc0309569a0>"],"text/html":["<style type=\"text/css\">\n","</style>\n","<table id=\"T_d3650\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_d3650_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n","      <th id=\"T_d3650_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n","      <th id=\"T_d3650_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n","      <th id=\"T_d3650_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n","      <th id=\"T_d3650_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n","      <th id=\"T_d3650_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_d3650_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_d3650_row0_col0\" class=\"data row0 col0\" >-0.2177</td>\n","      <td id=\"T_d3650_row0_col1\" class=\"data row0 col1\" >8.1978</td>\n","      <td id=\"T_d3650_row0_col2\" class=\"data row0 col2\" >1.7348</td>\n","      <td id=\"T_d3650_row0_col3\" class=\"data row0 col3\" >0</td>\n","      <td id=\"T_d3650_row0_col4\" class=\"data row0 col4\" >0</td>\n","      <td id=\"T_d3650_row0_col5\" class=\"data row0 col5\" >0</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e1ddc47f7e63404c91d86a139c4abde2"}},"metadata":{"application/vnd.jupyter.widget-view+json":{"colab":{"custom_widget_manager":{"url":"https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"}}}}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML 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size 800x550 with 0 Axes>"]},"metadata":{}}]},{"cell_type":"code","source":["# 클러스터링 결과 원본데이터 받아오기\n","s = setup(data, normalize=True, session_id=22, ignore_features=['표준버스정류장ID', '버스정류장ARS번호', '행정동', '대규모점포', '사업체수', '종사자수', '낮시간대 생활인구', '밤시간대 생활인구', '총생활인구수', '65세 이상 생활인구 비율', '1020 생활인구 비율'])\n","\n","result = assign_model(optics)\n","\n","df_cluster = pd.concat([result, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"id":"Kb6k9GsEHwXe"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 클러스터 별 행정동 개수 파악 (잘됐는지)\n","df_cluster.groupby(by='Cluster', axis=0, as_index=False)['행정동'].nunique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":457},"id":"iftSUNsyH0yV","executionInfo":{"status":"ok","timestamp":1681566004544,"user_tz":-540,"elapsed":5,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"870a77ec-6a43-45db-e62b-21b79f3f4f39"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Cluster  행정동\n","0   Cluster -1   15\n","1    Cluster 0    6\n","2    Cluster 1    3\n","3   Cluster 10    3\n","4   Cluster 11    5\n","5    Cluster 2    4\n","6    Cluster 3    1\n","7    Cluster 4    1\n","8    Cluster 5    3\n","9    Cluster 6    4\n","10   Cluster 7    4\n","11   Cluster 8    4\n","12   Cluster 9    1"],"text/html":["\n","  <div id=\"df-b20ef52f-af0d-4d49-9964-3d8cc4265f55\">\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"," 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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":26}]},{"cell_type":"code","source":["# 건대입구 사거리는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"1PZ8xf1pH9vG","executionInfo":{"status":"ok","timestamp":1681566010566,"user_tz":-540,"elapsed":5,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"92685efd-5537-470e-e859-051844625b17"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수      밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","114  35146.332031  10774.666992  86699.335938     12.0           59.584316   \n","\n","     market_distance  count     Cluster  행정동  버스정류장ARS번호  \n","114       328.849854    5.0  Cluster -1  화양동        5232  "],"text/html":["\n","  <div id=\"df-8ce72a89-bad2-4843-8440-0b76023d2fe7\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>114</th>\n","      <td>35146.332031</td>\n","      <td>10774.666992</td>\n","      <td>86699.335938</td>\n","      <td>12.0</td>\n","      <td>59.584316</td>\n","      <td>328.849854</td>\n","      <td>5.0</td>\n","      <td>Cluster -1</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ce72a89-bad2-4843-8440-0b76023d2fe7')\"\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","  <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-8ce72a89-bad2-4843-8440-0b76023d2fe7 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":27}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"trBcG4JXH_pN","executionInfo":{"status":"ok","timestamp":1681566012374,"user_tz":-540,"elapsed":5,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"dab44107-0485-4a92-82e4-34b0ac8507f7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","89   20957.0    4974.0  53744.167969      4.0            97.78656   \n","\n","    market_distance  count     Cluster   행정동  버스정류장ARS번호  \n","89       241.094788    1.0  Cluster 11  자양3동        5204  "],"text/html":["\n","  <div id=\"df-27bf917e-f7f7-438e-af99-f330e3d2e950\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>89</th>\n","      <td>20957.0</td>\n","      <td>4974.0</td>\n","      <td>53744.167969</td>\n","      <td>4.0</td>\n","      <td>97.78656</td>\n","      <td>241.094788</td>\n","      <td>1.0</td>\n","      <td>Cluster 11</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27bf917e-f7f7-438e-af99-f330e3d2e950')\"\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","  <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-27bf917e-f7f7-438e-af99-f330e3d2e950 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":28}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5169]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"7Foiw8imILMs","executionInfo":{"status":"ok","timestamp":1681566014493,"user_tz":-540,"elapsed":5,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"5d35aedb-1a3d-483d-84d9-868736213f86"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수    밤시간대총승객수       일일총승객수  환승가능노선수  starbucks_distance  \\\n","68    3450.0  276.166656  7934.666504      1.0          340.466675   \n","\n","    market_distance  count     Cluster  행정동  버스정류장ARS번호  \n","68      1968.143066    1.0  Cluster -1  광장동        5169  "],"text/html":["\n","  <div id=\"df-652367cb-8697-4fe0-80ae-8fe19bd0dc2a\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>68</th>\n","      <td>3450.0</td>\n","      <td>276.166656</td>\n","      <td>7934.666504</td>\n","      <td>1.0</td>\n","      <td>340.466675</td>\n","      <td>1968.143066</td>\n","      <td>1.0</td>\n","      <td>Cluster -1</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-652367cb-8697-4fe0-80ae-8fe19bd0dc2a')\"\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","  <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-652367cb-8697-4fe0-80ae-8fe19bd0dc2a button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":29}]},{"cell_type":"code","source":["# 같은 클러스터 확인\n","df_cluster.loc[df_cluster['Cluster'] == 'Cluster 11']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"JiRtQSLgIVr2","executionInfo":{"status":"ok","timestamp":1681565865092,"user_tz":-540,"elapsed":474,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"19726fe9-6a23-48f8-eba1-1ad2aa249d69"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수     밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","4    18719.500000  6868.500000  48762.500000      6.0          238.122986   \n","6    19870.000000  4863.500000  44842.500000      5.0           78.215050   \n","7    26958.833984  6447.333496  65456.500000      7.0          216.765747   \n","53   24268.166016  4937.500000  54723.332031      8.0          223.763870   \n","89   20957.000000  4974.000000  53744.167969      4.0           97.786560   \n","100  20126.666016  5016.500000  52191.332031      4.0          128.844650   \n","\n","     market_distance  count     Cluster   행정동  버스정류장ARS번호  \n","4         637.437317    1.0  Cluster 11    능동        5004  \n","6         702.566589    1.0  Cluster 11  구의2동        5006  \n","7         557.314453    1.0  Cluster 11    능동        5007  \n","53        601.368591    1.0  Cluster 11  구의1동        5148  \n","89        241.094788    1.0  Cluster 11  자양3동        5204  \n","100       215.016281    1.0  Cluster 11  자양4동        5218  "],"text/html":["\n","  <div id=\"df-95f0d08e-402b-4c4c-af3d-3f475eb581d6\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>4</th>\n","      <td>18719.500000</td>\n","      <td>6868.500000</td>\n","      <td>48762.500000</td>\n","      <td>6.0</td>\n","      <td>238.122986</td>\n","      <td>637.437317</td>\n","      <td>1.0</td>\n","      <td>Cluster 11</td>\n","      <td>능동</td>\n","      <td>5004</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>19870.000000</td>\n","      <td>4863.500000</td>\n","      <td>44842.500000</td>\n","      <td>5.0</td>\n","      <td>78.215050</td>\n","      <td>702.566589</td>\n","      <td>1.0</td>\n","      <td>Cluster 11</td>\n","      <td>구의2동</td>\n","      <td>5006</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>26958.833984</td>\n","      <td>6447.333496</td>\n","      <td>65456.500000</td>\n","      <td>7.0</td>\n","      <td>216.765747</td>\n","      <td>557.314453</td>\n","      <td>1.0</td>\n","      <td>Cluster 11</td>\n","      <td>능동</td>\n","      <td>5007</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>24268.166016</td>\n","      <td>4937.500000</td>\n","      <td>54723.332031</td>\n","      <td>8.0</td>\n","      <td>223.763870</td>\n","      <td>601.368591</td>\n","      <td>1.0</td>\n","      <td>Cluster 11</td>\n","      <td>구의1동</td>\n","      <td>5148</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>20957.000000</td>\n","      <td>4974.000000</td>\n","      <td>53744.167969</td>\n","      <td>4.0</td>\n","      <td>97.786560</td>\n","      <td>241.094788</td>\n","      <td>1.0</td>\n","      <td>Cluster 11</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>20126.666016</td>\n","      <td>5016.500000</td>\n","      <td>52191.332031</td>\n","      <td>4.0</td>\n","      <td>128.844650</td>\n","      <td>215.016281</td>\n","      <td>1.0</td>\n","      <td>Cluster 11</td>\n","      <td>자양4동</td>\n","      <td>5218</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95f0d08e-402b-4c4c-af3d-3f475eb581d6')\"\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 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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":21}]},{"cell_type":"markdown","source":["## hclust (AgglomerativeClustering)"],"metadata":{"id":"uVrUInN6Is8q"}},{"cell_type":"code","source":["# 모델 생성 (Functional API)\n","hclust = create_model('hclust', num_clusters=5)\n","\n","evaluate_model(hclust)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["f8b4947e76424fbbba00ba4a7521f604","ed30469918d042409a717a00349b6f72","1a8db57fa2d74adb8eced44810c7dde7","f8b77702b50a4aa2ad673bf68bd924a6","b9c8898c982e4e6fa0fcd7754a90b11d","34b9b146f5094238bae5392cc58e0b4e","7ece58d77f8449289f9c06948290f32e","8a6e08ba78314ff2aed126b5d323105e","c50ae99df63341db8b3fb5bc0fcdd98d","ac3adeb7fba24d08be6027224bd7e667","6b32225326944772a176fe3a22f0773d","cba91ab44ab44e41937f69cca18ff4df","9fe16b6619564b4d97585e242053716a","d9a6361df9cd43c299d4dd101961dc6f","d4de136f98d4428584ea2f5b3585f0bf","e1a242ef1f4a4a319a664a3f60b46fd5","41385ac05fe749d4b63e7bfa7657b232","c2f1e22fd3aa4418a7319060521f79ea"]},"id":"Y7tPsPtkKGMM","executionInfo":{"status":"ok","timestamp":1682428855287,"user_tz":-540,"elapsed":830,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"e9dc605b-6cd5-400b-a404-2aeaff1470dc"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML 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class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_4437e_row0_col0\" class=\"data row0 col0\" >0.2301</td>\n","      <td id=\"T_4437e_row0_col1\" class=\"data row0 col1\" >108.0804</td>\n","      <td id=\"T_4437e_row0_col2\" class=\"data row0 col2\" >1.1212</td>\n","      <td id=\"T_4437e_row0_col3\" class=\"data row0 col3\" >0</td>\n","      <td id=\"T_4437e_row0_col4\" class=\"data row0 col4\" >0</td>\n","      <td id=\"T_4437e_row0_col5\" class=\"data row0 col5\" >0</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/3 [00:00<?, 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size 800x550 with 0 Axes>"]},"metadata":{}}]},{"cell_type":"code","source":["# 클러스터링 결과 원본데이터 받아오기\n","result = assign_model(hclust)\n","\n","df_cluster = pd.concat([result, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"id":"PemILrUgKWNH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 건대입구 사거리는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1681566363461,"user_tz":-540,"elapsed":487,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"8b84833a-b509-4c7c-efc2-2fc92661d874","id":"snC9AHxoKgP3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수      밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","114  35146.332031  10774.666992  86699.335938     12.0           59.584316   \n","\n","     market_distance  count    Cluster  행정동  버스정류장ARS번호  \n","114       328.849854    5.0  Cluster 1  화양동        5232  "],"text/html":["\n","  <div id=\"df-a8478707-8af8-45c6-ad56-d2e858f00820\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>114</th>\n","      <td>35146.332031</td>\n","      <td>10774.666992</td>\n","      <td>86699.335938</td>\n","      <td>12.0</td>\n","      <td>59.584316</td>\n","      <td>328.849854</td>\n","      <td>5.0</td>\n","      <td>Cluster 1</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8478707-8af8-45c6-ad56-d2e858f00820')\"\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 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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":40}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1681566363922,"user_tz":-540,"elapsed":7,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"894e331b-e98f-4028-cd95-ab46aeffa19b","id":"C9YcS8jYKgP3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","89   20957.0    4974.0  53744.167969      4.0            97.78656   \n","\n","    market_distance  count    Cluster   행정동  버스정류장ARS번호  \n","89       241.094788    1.0  Cluster 1  자양3동        5204  "],"text/html":["\n","  <div id=\"df-1e27d909-d23a-4a5f-9322-c3997f52f916\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>89</th>\n","      <td>20957.0</td>\n","      <td>4974.0</td>\n","      <td>53744.167969</td>\n","      <td>4.0</td>\n","      <td>97.78656</td>\n","      <td>241.094788</td>\n","      <td>1.0</td>\n","      <td>Cluster 1</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e27d909-d23a-4a5f-9322-c3997f52f916')\"\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","  <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-1e27d909-d23a-4a5f-9322-c3997f52f916 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":41}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5169]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1681566363923,"user_tz":-540,"elapsed":7,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"3aeacd1d-e63b-42f8-f259-04537ed8ac23","id":"TZJ4Cp1BKgP4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수    밤시간대총승객수       일일총승객수  환승가능노선수  starbucks_distance  \\\n","68    3450.0  276.166656  7934.666504      1.0          340.466675   \n","\n","    market_distance  count    Cluster  행정동  버스정류장ARS번호  \n","68      1968.143066    1.0  Cluster 2  광장동        5169  "],"text/html":["\n","  <div id=\"df-9c58f9e5-da70-49b3-8b9f-d1d9e5326de2\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>68</th>\n","      <td>3450.0</td>\n","      <td>276.166656</td>\n","      <td>7934.666504</td>\n","      <td>1.0</td>\n","      <td>340.466675</td>\n","      <td>1968.143066</td>\n","      <td>1.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c58f9e5-da70-49b3-8b9f-d1d9e5326de2')\"\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","  <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-9c58f9e5-da70-49b3-8b9f-d1d9e5326de2 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":42}]},{"cell_type":"code","source":["# 같은 클러스터 확인\n","df_cluster.loc[df_cluster['Cluster'] == 'Cluster 2']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1681566627428,"user_tz":-540,"elapsed":503,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"5543907a-41b7-43f1-e983-b806717ca69b","id":"GiqtdQ1mKgP4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        낮시간대총승객수    밤시간대총승객수       일일총승객수  환승가능노선수  starbucks_distance  \\\n","64    546.166687   82.833336  1134.666626      1.0          373.926788   \n","67    465.500000   25.333334   925.500000      1.0          319.514618   \n","68   3450.000000  276.166656  7934.666504      1.0          340.466675   \n","143  1037.000000   97.750000  3182.500000      1.0          172.961563   \n","166  2042.500000   58.000000  5398.500000      0.0          314.694946   \n","169   515.500000    8.166667  1081.000000      0.0          196.043945   \n","170   155.666672    2.000000   238.833328      0.0          357.567047   \n","171  1687.500000   61.000000  3112.166748      0.0          308.642761   \n","172   829.333313   22.500000  1439.833374      0.0           73.730568   \n","174   691.833313   46.166668  1484.166626      0.0           86.409698   \n","176  1345.000000   21.000000  2118.333252      0.0          320.031006   \n","177    73.666664    2.500000   140.666672      0.0           63.256069   \n","179   371.333344   27.500000   772.000000      0.0          319.714203   \n","184   574.250000   30.125000  1178.500000      0.0          329.220459   \n","205  1455.500000   17.500000  4152.000000      0.0          228.283569   \n","209  2269.333252   51.500000  5333.833496      0.0          168.190598   \n","213  1082.000000   44.833332  2674.166748      0.0          166.274200   \n","216   312.000000    9.500000   724.333313      0.0          329.969208   \n","217  4456.666504  191.666672  8063.333496      1.0          506.725250   \n","220   214.666672   10.166667   427.166656      0.0          436.262878   \n","221     3.857143    1.142857     9.714286      0.0          312.130463   \n","223    14.166667    2.166667    27.666666      0.0          180.651581   \n","229   455.666656   12.500000   948.666687      0.0          203.005447   \n","231   411.500000   31.333334   739.333313      0.0          398.595886   \n","249   295.333344   13.333333   456.833344      0.0          185.994751   \n","250   664.333313   22.000000  1389.500000      0.0          139.367630   \n","251    52.833332    6.500000   125.333336      0.0           80.913086   \n","257   171.666672   15.166667   288.000000      0.0          251.195267   \n","258    42.833332    1.000000    53.333332      0.0          335.233215   \n","265   459.666656   86.833336   884.833313      0.0          119.009697   \n","266   217.333328   19.000000   376.500000      0.0          145.110886   \n","\n","     market_distance  count    Cluster   행정동  버스정류장ARS번호  \n","64       1407.516968    0.0  Cluster 2  구의3동        5163  \n","67       2005.090698    1.0  Cluster 2   광장동        5168  \n","68       1968.143066    1.0  Cluster 2   광장동        5169  \n","143      1949.511230    0.0  Cluster 2   광장동        5265  \n","166      1389.900879    0.0  Cluster 2  구의1동        5679  \n","169      1646.050781    0.0  Cluster 2   광장동        5670  \n","170      1564.355713    0.0  Cluster 2   광장동        5600  \n","171      1686.519531    0.0  Cluster 2   광장동        5597  \n","172      1815.708496    0.0  Cluster 2   광장동        5595  \n","174      1883.186279    0.0  Cluster 2   광장동        5591  \n","176      1862.403076    0.0  Cluster 2   광장동        5587  \n","177      1828.479248    0.0  Cluster 2   광장동        5581  \n","179      1693.185181    0.0  Cluster 2   광장동        5578  \n","184      1389.286377    0.0  Cluster 2  구의3동        5565  \n","205      1756.303711    0.0  Cluster 2   광장동        5533  \n","209      1969.072021    0.0  Cluster 2   광장동        5528  \n","213      2050.070801    0.0  Cluster 2   광장동        5514  \n","216      2087.576660    0.0  Cluster 2   광장동        5509  \n","217      2082.053711    0.0  Cluster 2   광장동        5504  \n","220      2028.390015    0.0  Cluster 2   광장동        5744  \n","221      2086.644531    0.0  Cluster 2   광장동        5742  \n","223      2063.807617    0.0  Cluster 2   광장동        5740  \n","229      1827.239502    0.0  Cluster 2   광장동        5728  \n","231      1433.653442    0.0  Cluster 2  구의3동        5724  \n","249      2015.354492    0.0  Cluster 2   광장동        5732  \n","250      1913.354004    0.0  Cluster 2   광장동        5519  \n","251      1904.724854    0.0  Cluster 2   광장동        5584  \n","257      1661.970947    0.0  Cluster 2   광장동        5993  \n","258      1585.940430    0.0  Cluster 2   광장동        5992  \n","265      1498.395386    0.0  Cluster 2   광장동        5990  \n","266      1499.883301    0.0  Cluster 2   광장동        5671  "],"text/html":["\n","  <div id=\"df-4950c0aa-27eb-4f2c-ba34-2fd0db6aa822\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>64</th>\n","      <td>546.166687</td>\n","      <td>82.833336</td>\n","      <td>1134.666626</td>\n","      <td>1.0</td>\n","      <td>373.926788</td>\n","      <td>1407.516968</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>구의3동</td>\n","      <td>5163</td>\n","    </tr>\n","    <tr>\n","      <th>67</th>\n","      <td>465.500000</td>\n","      <td>25.333334</td>\n","      <td>925.500000</td>\n","      <td>1.0</td>\n","      <td>319.514618</td>\n","      <td>2005.090698</td>\n","      <td>1.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5168</td>\n","    </tr>\n","    <tr>\n","      <th>68</th>\n","      <td>3450.000000</td>\n","      <td>276.166656</td>\n","      <td>7934.666504</td>\n","      <td>1.0</td>\n","      <td>340.466675</td>\n","      <td>1968.143066</td>\n","      <td>1.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","    <tr>\n","      <th>143</th>\n","      <td>1037.000000</td>\n","      <td>97.750000</td>\n","      <td>3182.500000</td>\n","      <td>1.0</td>\n","      <td>172.961563</td>\n","      <td>1949.511230</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5265</td>\n","    </tr>\n","    <tr>\n","      <th>166</th>\n","      <td>2042.500000</td>\n","      <td>58.000000</td>\n","      <td>5398.500000</td>\n","      <td>0.0</td>\n","      <td>314.694946</td>\n","      <td>1389.900879</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>구의1동</td>\n","      <td>5679</td>\n","    </tr>\n","    <tr>\n","      <th>169</th>\n","      <td>515.500000</td>\n","      <td>8.166667</td>\n","      <td>1081.000000</td>\n","      <td>0.0</td>\n","      <td>196.043945</td>\n","      <td>1646.050781</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5670</td>\n","    </tr>\n","    <tr>\n","      <th>170</th>\n","      <td>155.666672</td>\n","      <td>2.000000</td>\n","      <td>238.833328</td>\n","      <td>0.0</td>\n","      <td>357.567047</td>\n","      <td>1564.355713</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5600</td>\n","    </tr>\n","    <tr>\n","      <th>171</th>\n","      <td>1687.500000</td>\n","      <td>61.000000</td>\n","      <td>3112.166748</td>\n","      <td>0.0</td>\n","      <td>308.642761</td>\n","      <td>1686.519531</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5597</td>\n","    </tr>\n","    <tr>\n","      <th>172</th>\n","      <td>829.333313</td>\n","      <td>22.500000</td>\n","      <td>1439.833374</td>\n","      <td>0.0</td>\n","      <td>73.730568</td>\n","      <td>1815.708496</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5595</td>\n","    </tr>\n","    <tr>\n","      <th>174</th>\n","      <td>691.833313</td>\n","      <td>46.166668</td>\n","      <td>1484.166626</td>\n","      <td>0.0</td>\n","      <td>86.409698</td>\n","      <td>1883.186279</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5591</td>\n","    </tr>\n","    <tr>\n","      <th>176</th>\n","      <td>1345.000000</td>\n","      <td>21.000000</td>\n","      <td>2118.333252</td>\n","      <td>0.0</td>\n","      <td>320.031006</td>\n","      <td>1862.403076</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5587</td>\n","    </tr>\n","    <tr>\n","      <th>177</th>\n","      <td>73.666664</td>\n","      <td>2.500000</td>\n","      <td>140.666672</td>\n","      <td>0.0</td>\n","      <td>63.256069</td>\n","      <td>1828.479248</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5581</td>\n","    </tr>\n","    <tr>\n","      <th>179</th>\n","      <td>371.333344</td>\n","      <td>27.500000</td>\n","      <td>772.000000</td>\n","      <td>0.0</td>\n","      <td>319.714203</td>\n","      <td>1693.185181</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5578</td>\n","    </tr>\n","    <tr>\n","      <th>184</th>\n","      <td>574.250000</td>\n","      <td>30.125000</td>\n","      <td>1178.500000</td>\n","      <td>0.0</td>\n","      <td>329.220459</td>\n","      <td>1389.286377</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>구의3동</td>\n","      <td>5565</td>\n","    </tr>\n","    <tr>\n","      <th>205</th>\n","      <td>1455.500000</td>\n","      <td>17.500000</td>\n","      <td>4152.000000</td>\n","      <td>0.0</td>\n","      <td>228.283569</td>\n","      <td>1756.303711</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5533</td>\n","    </tr>\n","    <tr>\n","      <th>209</th>\n","      <td>2269.333252</td>\n","      <td>51.500000</td>\n","      <td>5333.833496</td>\n","      <td>0.0</td>\n","      <td>168.190598</td>\n","      <td>1969.072021</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5528</td>\n","    </tr>\n","    <tr>\n","      <th>213</th>\n","      <td>1082.000000</td>\n","      <td>44.833332</td>\n","      <td>2674.166748</td>\n","      <td>0.0</td>\n","      <td>166.274200</td>\n","      <td>2050.070801</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5514</td>\n","    </tr>\n","    <tr>\n","      <th>216</th>\n","      <td>312.000000</td>\n","      <td>9.500000</td>\n","      <td>724.333313</td>\n","      <td>0.0</td>\n","      <td>329.969208</td>\n","      <td>2087.576660</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5509</td>\n","    </tr>\n","    <tr>\n","      <th>217</th>\n","      <td>4456.666504</td>\n","      <td>191.666672</td>\n","      <td>8063.333496</td>\n","      <td>1.0</td>\n","      <td>506.725250</td>\n","      <td>2082.053711</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5504</td>\n","    </tr>\n","    <tr>\n","      <th>220</th>\n","      <td>214.666672</td>\n","      <td>10.166667</td>\n","      <td>427.166656</td>\n","      <td>0.0</td>\n","      <td>436.262878</td>\n","      <td>2028.390015</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5744</td>\n","    </tr>\n","    <tr>\n","      <th>221</th>\n","      <td>3.857143</td>\n","      <td>1.142857</td>\n","      <td>9.714286</td>\n","      <td>0.0</td>\n","      <td>312.130463</td>\n","      <td>2086.644531</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5742</td>\n","    </tr>\n","    <tr>\n","      <th>223</th>\n","      <td>14.166667</td>\n","      <td>2.166667</td>\n","      <td>27.666666</td>\n","      <td>0.0</td>\n","      <td>180.651581</td>\n","      <td>2063.807617</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5740</td>\n","    </tr>\n","    <tr>\n","      <th>229</th>\n","      <td>455.666656</td>\n","      <td>12.500000</td>\n","      <td>948.666687</td>\n","      <td>0.0</td>\n","      <td>203.005447</td>\n","      <td>1827.239502</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5728</td>\n","    </tr>\n","    <tr>\n","      <th>231</th>\n","      <td>411.500000</td>\n","      <td>31.333334</td>\n","      <td>739.333313</td>\n","      <td>0.0</td>\n","      <td>398.595886</td>\n","      <td>1433.653442</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>구의3동</td>\n","      <td>5724</td>\n","    </tr>\n","    <tr>\n","      <th>249</th>\n","      <td>295.333344</td>\n","      <td>13.333333</td>\n","      <td>456.833344</td>\n","      <td>0.0</td>\n","      <td>185.994751</td>\n","      <td>2015.354492</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5732</td>\n","    </tr>\n","    <tr>\n","      <th>250</th>\n","      <td>664.333313</td>\n","      <td>22.000000</td>\n","      <td>1389.500000</td>\n","      <td>0.0</td>\n","      <td>139.367630</td>\n","      <td>1913.354004</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5519</td>\n","    </tr>\n","    <tr>\n","      <th>251</th>\n","      <td>52.833332</td>\n","      <td>6.500000</td>\n","      <td>125.333336</td>\n","      <td>0.0</td>\n","      <td>80.913086</td>\n","      <td>1904.724854</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5584</td>\n","    </tr>\n","    <tr>\n","      <th>257</th>\n","      <td>171.666672</td>\n","      <td>15.166667</td>\n","      <td>288.000000</td>\n","      <td>0.0</td>\n","      <td>251.195267</td>\n","      <td>1661.970947</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5993</td>\n","    </tr>\n","    <tr>\n","      <th>258</th>\n","      <td>42.833332</td>\n","      <td>1.000000</td>\n","      <td>53.333332</td>\n","      <td>0.0</td>\n","      <td>335.233215</td>\n","      <td>1585.940430</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5992</td>\n","    </tr>\n","    <tr>\n","      <th>265</th>\n","      <td>459.666656</td>\n","      <td>86.833336</td>\n","      <td>884.833313</td>\n","      <td>0.0</td>\n","      <td>119.009697</td>\n","      <td>1498.395386</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5990</td>\n","    </tr>\n","    <tr>\n","      <th>266</th>\n","      <td>217.333328</td>\n","      <td>19.000000</td>\n","      <td>376.500000</td>\n","      <td>0.0</td>\n","      <td>145.110886</td>\n","      <td>1499.883301</td>\n","      <td>0.0</td>\n","      <td>Cluster 2</td>\n","      <td>광장동</td>\n","      <td>5671</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4950c0aa-27eb-4f2c-ba34-2fd0db6aa822')\"\n","      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'버스정류장ARS번호'].values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eJq6ymbPKcWw","executionInfo":{"status":"ok","timestamp":1681566570925,"user_tz":-540,"elapsed":503,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"c69c6104-a036-4565-b0a9-4bb62c300794"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5163, 5168, 5169, 5265, 5679, 5670, 5600, 5597, 5595, 5591, 5587,\n","       5581, 5578, 5565, 5533, 5528, 5514, 5509, 5504, 5744, 5742, 5740,\n","       5728, 5724, 5732, 5519, 5584, 5993, 5992, 5990, 5671])"]},"metadata":{},"execution_count":46}]},{"cell_type":"markdown","source":["### 행정동 데이터 첨가"],"metadata":{"id":"OBbLPQ8ZNi97"}},{"cell_type":"code","source":["# 클러스터링 결과 원본데이터 받아오기\n","s = setup(data, normalize=True, session_id=22, ignore_features=['표준버스정류장ID', '버스정류장ARS번호', '행정동', '대규모점포', '사업체수', '종사자수', '낮시간대 생활인구', '밤시간대 생활인구'])\n","\n","sc = create_model('sc', num_clusters=4)\n","\n","evaluate_model(sc)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["64d8ad12e62240a9844f2118d7bb5517","75de95bd8bae411ea80066ce567735b8","f106d13cb6ae431e9eefe502cfba0946","ce060927eda143c7b11f527c235d836e","4facc435ccc34d52bc770bf9ce58728a","cd9ac74fcf93425ab76076d51e1b35f7","83b8fa34d7b34240badfc759f60be5f3","07512a84c2554e13b359cabc7eea5786","170103f691c842959f7dd1c7ea6b2c9e","d6ab9a072ab94b40989f9f0b975de7fe","0d12ded181294391adf07290eadda32e","700d2dd3b7b44d71991e152e02c8d341","bda20b5927294eafa37db4e1bcdcd074","fe5ea4c8f64c41ce9504fd6126ff59f5","2e54766ba2a14a2ea0e2e48faf0b39f3","26f6bb020737414ba776adee87c77f25","2954253d75d04c9291494d41b0d2e3e5","73e10c7c068f44338387f54ab6c67d08"]},"id":"FiZeQp_6K5Xj","executionInfo":{"status":"ok","timestamp":1682428869558,"user_tz":-540,"elapsed":2424,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"0c5655e2-bf34-42a4-dab5-4e119a8c4d3b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7fc02801e220>"],"text/html":["<style type=\"text/css\">\n","#T_18ed0_row5_col1, #T_18ed0_row9_col1 {\n","  background-color: lightgreen;\n","}\n","</style>\n","<table id=\"T_18ed0\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_18ed0_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n","      <th id=\"T_18ed0_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_18ed0_row0_col0\" class=\"data row0 col0\" >Session id</td>\n","      <td id=\"T_18ed0_row0_col1\" class=\"data row0 col1\" >22</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_18ed0_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n","      <td id=\"T_18ed0_row1_col1\" class=\"data row1 col1\" >(269, 18)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_18ed0_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n","      <td id=\"T_18ed0_row2_col1\" class=\"data row2 col1\" >(269, 10)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_18ed0_row3_col0\" class=\"data row3 col0\" >Ignore features</td>\n","      <td id=\"T_18ed0_row3_col1\" class=\"data row3 col1\" >8</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_18ed0_row4_col0\" class=\"data row4 col0\" >Numeric features</td>\n","      <td id=\"T_18ed0_row4_col1\" class=\"data row4 col1\" >10</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_18ed0_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n","      <td id=\"T_18ed0_row5_col1\" class=\"data row5 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_18ed0_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n","      <td id=\"T_18ed0_row6_col1\" class=\"data row6 col1\" >simple</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_18ed0_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n","      <td id=\"T_18ed0_row7_col1\" class=\"data row7 col1\" >mean</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_18ed0_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n","      <td id=\"T_18ed0_row8_col1\" class=\"data row8 col1\" >mode</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_18ed0_row9_col0\" class=\"data row9 col0\" >Normalize</td>\n","      <td id=\"T_18ed0_row9_col1\" class=\"data row9 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n","      <td id=\"T_18ed0_row10_col0\" class=\"data row10 col0\" >Normalize method</td>\n","      <td id=\"T_18ed0_row10_col1\" class=\"data row10 col1\" >zscore</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n","      <td id=\"T_18ed0_row11_col0\" class=\"data row11 col0\" >CPU Jobs</td>\n","      <td id=\"T_18ed0_row11_col1\" class=\"data row11 col1\" >-1</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n","      <td id=\"T_18ed0_row12_col0\" class=\"data row12 col0\" >Use GPU</td>\n","      <td id=\"T_18ed0_row12_col1\" class=\"data row12 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n","      <td id=\"T_18ed0_row13_col0\" class=\"data row13 col0\" >Log Experiment</td>\n","      <td id=\"T_18ed0_row13_col1\" class=\"data row13 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n","      <td id=\"T_18ed0_row14_col0\" class=\"data row14 col0\" >Experiment Name</td>\n","      <td id=\"T_18ed0_row14_col1\" class=\"data row14 col1\" >cluster-default-name</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_18ed0_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n","      <td id=\"T_18ed0_row15_col0\" class=\"data row15 col0\" >USI</td>\n","      <td id=\"T_18ed0_row15_col1\" class=\"data row15 col1\" >535d</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7fc02829e310>"],"text/html":["<style type=\"text/css\">\n","</style>\n","<table id=\"T_87799\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_87799_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n","      <th id=\"T_87799_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n","      <th id=\"T_87799_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n","      <th id=\"T_87799_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n","      <th id=\"T_87799_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n","      <th id=\"T_87799_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_87799_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_87799_row0_col0\" class=\"data row0 col0\" >0.3744</td>\n","      <td id=\"T_87799_row0_col1\" class=\"data row0 col1\" >48.9628</td>\n","      <td id=\"T_87799_row0_col2\" class=\"data row0 col2\" >0.7632</td>\n","      <td id=\"T_87799_row0_col3\" class=\"data row0 col3\" >0</td>\n","      <td id=\"T_87799_row0_col4\" class=\"data row0 col4\" >0</td>\n","      <td id=\"T_87799_row0_col5\" class=\"data row0 col5\" >0</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/3 [00:00<?, 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size 800x550 with 0 Axes>"]},"metadata":{}}]},{"cell_type":"code","source":["# 클러스터링 결과 원본데이터 받아오기\n","result = assign_model(sc)\n","\n","df_cluster = pd.concat([result, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"id":"ur14EhpHN4gZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 건대입구 사거리는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568153172,"user_tz":-540,"elapsed":6,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"ab8b3c2e-b5f2-43eb-d22a-547e2d6595ce","id":"pVOsEzmBN4gZ"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수      밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","114  35146.332031  10774.666992  86699.335938     12.0           59.584316   \n","\n","     market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","114       328.849854    5.0  652387776.0         0.08093      0.490105   \n","\n","       Cluster  행정동  버스정류장ARS번호  \n","114  Cluster 2  화양동        5232  "],"text/html":["\n","  <div id=\"df-bb12f548-5d45-4636-acca-66630393e4e6\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>총생활인구수</th>\n","      <th>65세 이상 생활인구 비율</th>\n","      <th>1020 생활인구 비율</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>114</th>\n","      <td>35146.332031</td>\n","      <td>10774.666992</td>\n","      <td>86699.335938</td>\n","      <td>12.0</td>\n","      <td>59.584316</td>\n","      <td>328.849854</td>\n","      <td>5.0</td>\n","      <td>652387776.0</td>\n","      <td>0.08093</td>\n","      <td>0.490105</td>\n","      <td>Cluster 2</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb12f548-5d45-4636-acca-66630393e4e6')\"\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\" 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5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568153675,"user_tz":-540,"elapsed":9,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"395dac4e-3ecd-4996-a1ff-2c6c013bf2fd","id":"toXRQIyrN4ga"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","89   20957.0    4974.0  53744.167969      4.0            97.78656   \n","\n","    market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","89       241.094788    1.0  412792672.0        0.137371      0.249907   \n","\n","      Cluster   행정동  버스정류장ARS번호  \n","89  Cluster 2  자양3동        5204  "],"text/html":["\n","  <div id=\"df-60907251-6817-4c32-8a31-424726a172ce\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<td>0.137371</td>\n","      <td>0.249907</td>\n","      <td>Cluster 2</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60907251-6817-4c32-8a31-424726a172ce')\"\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 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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":69}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5169]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568153675,"user_tz":-540,"elapsed":8,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"49078073-bf65-4002-d6b0-9c01deb01786","id":"RgKhunWsN4ga"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수    밤시간대총승객수       일일총승객수  환승가능노선수  starbucks_distance  \\\n","68    3450.0  276.166656  7934.666504      1.0          340.466675   \n","\n","    market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","68      1968.143066    1.0  638349888.0        0.118303      0.274873   \n","\n","      Cluster  행정동  버스정류장ARS번호  \n","68  Cluster 1  광장동        5169  "],"text/html":["\n","  <div id=\"df-446f318c-3072-4141-ae6e-7562fd269573\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<td>0.118303</td>\n","      <td>0.274873</td>\n","      <td>Cluster 1</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-446f318c-3072-4141-ae6e-7562fd269573')\"\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 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2']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":549},"executionInfo":{"status":"ok","timestamp":1681568159955,"user_tz":-540,"elapsed":506,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"275e7c7b-065a-458e-ed8d-96b196b74435","id":"6uqcilPiN4ga"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수      밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","1    15681.333008   3756.500000  39580.667969      7.0          350.338501   \n","3    15281.500000   4266.833496  36958.000000      6.0          318.242950   \n","4    18719.500000   6868.500000  48762.500000      6.0          238.122986   \n","6    19870.000000   4863.500000  44842.500000      5.0           78.215050   \n","7    26958.833984   6447.333496  65456.500000      7.0          216.765747   \n","53   24268.166016   4937.500000  54723.332031      8.0          223.763870   \n","89   20957.000000   4974.000000  53744.167969      4.0           97.786560   \n","100  20126.666016   5016.500000  52191.332031      4.0          128.844650   \n","114  35146.332031  10774.666992  86699.335938     12.0           59.584316   \n","\n","     market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","1         723.257324    3.0  270830848.0        0.145448      0.223181   \n","3         562.888428    1.0  323892352.0        0.146777      0.232630   \n","4         637.437317    1.0  273699712.0        0.129653      0.268713   \n","6         702.566589    1.0  375967552.0        0.158670      0.236506   \n","7         557.314453    1.0  273699712.0        0.129653      0.268713   \n","53        601.368591    1.0  443511968.0        0.127300      0.264467   \n","89        241.094788    1.0  412792672.0        0.137371      0.249907   \n","100       215.016281    1.0  441175776.0        0.142101      0.270933   \n","114       328.849854    5.0  652387776.0        0.080930      0.490105   \n","\n","       Cluster   행정동  버스정류장ARS번호  \n","1    Cluster 2  중곡1동        5001  \n","3    Cluster 2  중곡2동        5003  \n","4    Cluster 2    능동        5004  \n","6    Cluster 2  구의2동        5006  \n","7    Cluster 2    능동        5007  \n","53   Cluster 2  구의1동        5148  \n","89   Cluster 2  자양3동        5204  \n","100  Cluster 2  자양4동        5218  \n","114  Cluster 2   화양동        5232  "],"text/html":["\n","  <div id=\"df-c2f86200-f78f-4933-a977-e7265dcd1637\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>총생활인구수</th>\n","      <th>65세 이상 생활인구 비율</th>\n","      <th>1020 생활인구 비율</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>15681.333008</td>\n","      <td>3756.500000</td>\n","      <td>39580.667969</td>\n","      <td>7.0</td>\n","      <td>350.338501</td>\n","      <td>723.257324</td>\n","      <td>3.0</td>\n","      <td>270830848.0</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>Cluster 2</td>\n","      <td>중곡1동</td>\n","      <td>5001</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>15281.500000</td>\n","      <td>4266.833496</td>\n","      <td>36958.000000</td>\n","      <td>6.0</td>\n","      <td>318.242950</td>\n","      <td>562.888428</td>\n","      <td>1.0</td>\n","      <td>323892352.0</td>\n","      <td>0.146777</td>\n","      <td>0.232630</td>\n","      <td>Cluster 2</td>\n","      <td>중곡2동</td>\n","      <td>5003</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>18719.500000</td>\n","      <td>6868.500000</td>\n","      <td>48762.500000</td>\n","      <td>6.0</td>\n","      <td>238.122986</td>\n","      <td>637.437317</td>\n","      <td>1.0</td>\n","      <td>273699712.0</td>\n","      <td>0.129653</td>\n","      <td>0.268713</td>\n","      <td>Cluster 2</td>\n","      <td>능동</td>\n","      <td>5004</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>19870.000000</td>\n","      <td>4863.500000</td>\n","      <td>44842.500000</td>\n","      <td>5.0</td>\n","      <td>78.215050</td>\n","      <td>702.566589</td>\n","      <td>1.0</td>\n","      <td>375967552.0</td>\n","      <td>0.158670</td>\n","      <td>0.236506</td>\n","      <td>Cluster 2</td>\n","      <td>구의2동</td>\n","      <td>5006</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>26958.833984</td>\n","      <td>6447.333496</td>\n","      <td>65456.500000</td>\n","      <td>7.0</td>\n","      <td>216.765747</td>\n","      <td>557.314453</td>\n","      <td>1.0</td>\n","      <td>273699712.0</td>\n","      <td>0.129653</td>\n","      <td>0.268713</td>\n","      <td>Cluster 2</td>\n","      <td>능동</td>\n","      <td>5007</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>24268.166016</td>\n","      <td>4937.500000</td>\n","      <td>54723.332031</td>\n","      <td>8.0</td>\n","      <td>223.763870</td>\n","      <td>601.368591</td>\n","      <td>1.0</td>\n","      <td>443511968.0</td>\n","      <td>0.127300</td>\n","      <td>0.264467</td>\n","      <td>Cluster 2</td>\n","      <td>구의1동</td>\n","      <td>5148</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>20957.000000</td>\n","      <td>4974.000000</td>\n","      <td>53744.167969</td>\n","      <td>4.0</td>\n","      <td>97.786560</td>\n","      <td>241.094788</td>\n","      <td>1.0</td>\n","      <td>412792672.0</td>\n","      <td>0.137371</td>\n","      <td>0.249907</td>\n","      <td>Cluster 2</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>20126.666016</td>\n","      <td>5016.500000</td>\n","      <td>52191.332031</td>\n","      <td>4.0</td>\n","      <td>128.844650</td>\n","      <td>215.016281</td>\n","      <td>1.0</td>\n","      <td>441175776.0</td>\n","      <td>0.142101</td>\n","      <td>0.270933</td>\n","      <td>Cluster 2</td>\n","      <td>자양4동</td>\n","      <td>5218</td>\n","    </tr>\n","    <tr>\n","      <th>114</th>\n","      <td>35146.332031</td>\n","      <td>10774.666992</td>\n","      <td>86699.335938</td>\n","      <td>12.0</td>\n","      <td>59.584316</td>\n","      <td>328.849854</td>\n","      <td>5.0</td>\n","      <td>652387776.0</td>\n","      <td>0.080930</td>\n","      <td>0.490105</td>\n","      <td>Cluster 2</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2f86200-f78f-4933-a977-e7265dcd1637')\"\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 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-c2f86200-f78f-4933-a977-e7265dcd1637');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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'버스정류장ARS번호'].values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cOzHqy-GN9II","executionInfo":{"status":"ok","timestamp":1681567265590,"user_tz":-540,"elapsed":574,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"f8fce3b8-b21d-4112-992e-37ec2b0f2f13"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5118, 5001, 5002, 5003, 5004, 5005, 5006, 5007, 5140, 5141, 5145,\n","       5148, 5175, 5188, 5189, 5190, 5191, 5193, 5194, 5204, 5205, 5216,\n","       5217, 5218, 5220, 5232, 5250, 5251, 5252, 5253, 5162, 5192, 5249,\n","       5011, 5594, 5582])"]},"metadata":{},"execution_count":56}]},{"cell_type":"markdown","source":["## sc"],"metadata":{"id":"ppp5i83tOC4g"}},{"cell_type":"code","source":["# 클러스터링 결과 원본데이터 받아오기\n","s = setup(data, normalize=True, session_id=22, ignore_features=['표준버스정류장ID', '버스정류장ARS번호', '행정동', '대규모점포', '사업체수', '종사자수', '낮시간대 생활인구', '밤시간대 생활인구', '총생활인구수', '65세 이상 생활인구 비율', '1020 생활인구 비율'])\n","\n","result = assign_model(optics)\n","\n","df_cluster = pd.concat([result, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"executionInfo":{"status":"ok","timestamp":1682429033453,"user_tz":-540,"elapsed":1570,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"46d2ec7d-2eb1-435d-f403-7f5c73d68f94","id":"_4eiXbdYQzUv"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7fc01bbfef10>"],"text/html":["<style type=\"text/css\">\n","#T_a181c_row5_col1, #T_a181c_row9_col1 {\n","  background-color: lightgreen;\n","}\n","</style>\n","<table id=\"T_a181c\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_a181c_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n","      <th id=\"T_a181c_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_a181c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_a181c_row0_col0\" class=\"data row0 col0\" >Session id</td>\n","      <td id=\"T_a181c_row0_col1\" class=\"data row0 col1\" >22</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_a181c_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n","      <td id=\"T_a181c_row1_col1\" class=\"data row1 col1\" >(269, 18)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_a181c_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n","      <td id=\"T_a181c_row2_col1\" class=\"data row2 col1\" >(269, 7)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_a181c_row3_col0\" class=\"data row3 col0\" >Ignore features</td>\n","      <td id=\"T_a181c_row3_col1\" class=\"data row3 col1\" >11</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_a181c_row4_col0\" class=\"data row4 col0\" >Numeric features</td>\n","      <td id=\"T_a181c_row4_col1\" class=\"data row4 col1\" >7</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_a181c_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n","      <td id=\"T_a181c_row5_col1\" class=\"data row5 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_a181c_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n","      <td id=\"T_a181c_row6_col1\" class=\"data row6 col1\" >simple</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_a181c_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n","      <td id=\"T_a181c_row7_col1\" class=\"data row7 col1\" >mean</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_a181c_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n","      <td id=\"T_a181c_row8_col1\" class=\"data row8 col1\" >mode</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_a181c_row9_col0\" class=\"data row9 col0\" >Normalize</td>\n","      <td id=\"T_a181c_row9_col1\" class=\"data row9 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n","      <td id=\"T_a181c_row10_col0\" class=\"data row10 col0\" >Normalize method</td>\n","      <td id=\"T_a181c_row10_col1\" class=\"data row10 col1\" >zscore</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n","      <td id=\"T_a181c_row11_col0\" class=\"data row11 col0\" >CPU Jobs</td>\n","      <td id=\"T_a181c_row11_col1\" class=\"data row11 col1\" >-1</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n","      <td id=\"T_a181c_row12_col0\" class=\"data row12 col0\" >Use GPU</td>\n","      <td id=\"T_a181c_row12_col1\" class=\"data row12 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n","      <td id=\"T_a181c_row13_col0\" class=\"data row13 col0\" >Log Experiment</td>\n","      <td id=\"T_a181c_row13_col1\" class=\"data row13 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n","      <td id=\"T_a181c_row14_col0\" class=\"data row14 col0\" >Experiment Name</td>\n","      <td id=\"T_a181c_row14_col1\" class=\"data row14 col1\" >cluster-default-name</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_a181c_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n","      <td id=\"T_a181c_row15_col0\" class=\"data row15 col0\" >USI</td>\n","      <td id=\"T_a181c_row15_col1\" class=\"data row15 col1\" >4ed1</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}}]},{"cell_type":"code","source":["# 모델 생성 (Functional API)\n","sc = create_model('birch', num_clusters=7)\n","\n","evaluate_model(sc)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["3b0bd9243c6c411d8b8c026c0e55a68c","c7298fcd334e4d7ba053e4895e75de3a","e43fd9a20db0466cb3d21d9d7022a6cf","d1475329048548a3916843cf58b35889","6aff6dc4c19940748a032b1d51c638c3","26ab9c8a8050447cb5be2b1387875b85","6a4468aa73ee4d00b444fc8d8b480338","41637b3762674ceeb8e198006a946604","308fe1b563264beb87849cfc3d9459e5","91fe6fdc977a4a7f9a561aa0b885c9c2","fc96f8fc02ee4979bcbedd79e22dae95","ed7484dce5e445afbb025ec1724d5095","3040daac775c416b904a3dd2a8751f2d","6b3e54e6a4004f699f249c053cb35b35","ad7c27cdf0864a3e8b9d4d0f6b688cf8","a069e96288cd414e8f25c808639e205c","8db354a5ee704181a62582a10c9427d8","ccc4bd72625241ff93343fa1dd0f1e72"]},"executionInfo":{"status":"ok","timestamp":1682429114699,"user_tz":-540,"elapsed":857,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"13ed4541-a16a-4f1d-d717-96cb0a09676c","id":"UFYe84JwQzUv"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7fc01bddaa30>"],"text/html":["<style type=\"text/css\">\n","</style>\n","<table id=\"T_63032\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_63032_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n","      <th id=\"T_63032_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n","      <th id=\"T_63032_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n","      <th id=\"T_63032_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n","      <th id=\"T_63032_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n","      <th id=\"T_63032_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_63032_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_63032_row0_col0\" class=\"data row0 col0\" >0.2593</td>\n","      <td id=\"T_63032_row0_col1\" class=\"data row0 col1\" >94.4395</td>\n","      <td id=\"T_63032_row0_col2\" class=\"data row0 col2\" >1.0332</td>\n","      <td id=\"T_63032_row0_col3\" class=\"data row0 col3\" >0</td>\n","      <td id=\"T_63032_row0_col4\" class=\"data row0 col4\" >0</td>\n","      <td id=\"T_63032_row0_col5\" class=\"data row0 col5\" >0</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/3 [00:00<?, 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클러스터링 결과 원본데이터 받아오기\n","result = assign_model(sc)\n","\n","df_cluster = pd.concat([result, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"id":"iZZRLG2qQzUw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 건대입구 사거리는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1681568237999,"user_tz":-540,"elapsed":65,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"7ad08006-35f3-4dc4-f177-dbc6742b2ea2","id":"-k9wMIx9QzUw"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수      밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","114  35146.332031  10774.666992  86699.335938     12.0           59.584316   \n","\n","     market_distance  count    Cluster  행정동  버스정류장ARS번호  \n","114       328.849854    5.0  Cluster 2  화양동        5232  "],"text/html":["\n","  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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":83}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1681568238012,"user_tz":-540,"elapsed":77,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"42c9dc33-581c-40ea-fe9d-3088d7bf2d0d","id":"-JoK7wxdQzUw"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","89   20957.0    4974.0  53744.167969      4.0            97.78656   \n","\n","    market_distance  count    Cluster   행정동  버스정류장ARS번호  \n","89       241.094788    1.0  Cluster 2  자양3동        5204  "],"text/html":["\n","  <div id=\"df-f43c55fa-28b6-40d5-b1e6-dd4784c0395b\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>89</th>\n","      <td>20957.0</td>\n","      <td>4974.0</td>\n","      <td>53744.167969</td>\n","      <td>4.0</td>\n","      <td>97.78656</td>\n","      <td>241.094788</td>\n","      <td>1.0</td>\n","      <td>Cluster 2</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f43c55fa-28b6-40d5-b1e6-dd4784c0395b')\"\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","  <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-f43c55fa-28b6-40d5-b1e6-dd4784c0395b button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":84}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5169]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1681568238448,"user_tz":-540,"elapsed":7,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"82d7bc5b-e657-42b6-9337-f27e05781098","id":"DocA2rVmQzUw"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수    밤시간대총승객수       일일총승객수  환승가능노선수  starbucks_distance  \\\n","68    3450.0  276.166656  7934.666504      1.0          340.466675   \n","\n","    market_distance  count    Cluster  행정동  버스정류장ARS번호  \n","68      1968.143066    1.0  Cluster 6  광장동        5169  "],"text/html":["\n","  <div id=\"df-fa7893c9-02d1-4d7b-a220-322dd3e9c686\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>68</th>\n","      <td>3450.0</td>\n","      <td>276.166656</td>\n","      <td>7934.666504</td>\n","      <td>1.0</td>\n","      <td>340.466675</td>\n","      <td>1968.143066</td>\n","      <td>1.0</td>\n","      <td>Cluster 6</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa7893c9-02d1-4d7b-a220-322dd3e9c686')\"\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","  <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-fa7893c9-02d1-4d7b-a220-322dd3e9c686 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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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":85}]},{"cell_type":"code","source":["# 같은 클러스터 확인\n","df_cluster.loc[df_cluster['Cluster'] == 'Cluster 6']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"status":"ok","timestamp":1681568246132,"user_tz":-540,"elapsed":490,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"aa4cbb50-bbee-47cf-d64e-29f795f2c08c","id":"SFEX8NkTQzUw"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        낮시간대총승객수     밤시간대총승객수       일일총승객수  환승가능노선수  starbucks_distance  \\\n","8    1519.000000  1228.833374  4874.166504      3.0          571.938110   \n","10   1305.166626   227.000000  2739.166748      1.0          241.688660   \n","11   3237.666748   972.166687  8972.333008      2.0          226.030991   \n","12   1051.333374   138.833328  2374.666748      2.0          314.448303   \n","13   1647.500000   420.166656  3661.500000      1.0          383.417450   \n","..           ...          ...          ...      ...                 ...   \n","263   328.500000     2.833333   870.333313      0.0         1021.091675   \n","264  2642.166748   214.333328  5278.500000      0.0          242.431580   \n","265   459.666656    86.833336   884.833313      0.0          119.009697   \n","266   217.333328    19.000000   376.500000      0.0          145.110886   \n","267   500.666656    29.666666  1005.333313      0.0          603.259033   \n","\n","     market_distance  count    Cluster   행정동  버스정류장ARS번호  \n","8         569.341797    1.0  Cluster 6  중곡1동        5101  \n","10        579.848022    0.0  Cluster 6  중곡1동        5103  \n","11        587.225281    0.0  Cluster 6  중곡1동        5104  \n","12        445.938416    0.0  Cluster 6  중곡1동        5105  \n","13        373.463531    0.0  Cluster 6  중곡1동        5106  \n","..               ...    ...        ...   ...         ...  \n","263       482.331116    1.0  Cluster 6  중곡4동        5500  \n","264       507.920258    0.0  Cluster 6  중곡3동        5755  \n","265      1498.395386    0.0  Cluster 6   광장동        5990  \n","266      1499.883301    0.0  Cluster 6   광장동        5671  \n","267       437.388733    0.0  Cluster 6  중곡3동        5511  \n","\n","[188 rows x 10 columns]"],"text/html":["\n","  <div id=\"df-cd39d955-289f-4f33-8427-270e5ceeee5f\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>8</th>\n","      <td>1519.000000</td>\n","      <td>1228.833374</td>\n","      <td>4874.166504</td>\n","      <td>3.0</td>\n","      <td>571.938110</td>\n","      <td>569.341797</td>\n","      <td>1.0</td>\n","      <td>Cluster 6</td>\n","      <td>중곡1동</td>\n","      <td>5101</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1305.166626</td>\n","      <td>227.000000</td>\n","      <td>2739.166748</td>\n","      <td>1.0</td>\n","      <td>241.688660</td>\n","      <td>579.848022</td>\n","      <td>0.0</td>\n","      <td>Cluster 6</td>\n","      <td>중곡1동</td>\n","      <td>5103</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>3237.666748</td>\n","      <td>972.166687</td>\n","      <td>8972.333008</td>\n","      <td>2.0</td>\n","      <td>226.030991</td>\n","      <td>587.225281</td>\n","      <td>0.0</td>\n","      <td>Cluster 6</td>\n","      <td>중곡1동</td>\n","      <td>5104</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>1051.333374</td>\n","      <td>138.833328</td>\n","      <td>2374.666748</td>\n","      <td>2.0</td>\n","      <td>314.448303</td>\n","      <td>445.938416</td>\n","      <td>0.0</td>\n","      <td>Cluster 6</td>\n","      <td>중곡1동</td>\n","      <td>5105</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>1647.500000</td>\n","      <td>420.166656</td>\n","      <td>3661.500000</td>\n","      <td>1.0</td>\n","      <td>383.417450</td>\n","      <td>373.463531</td>\n","      <td>0.0</td>\n","      <td>Cluster 6</td>\n","      <td>중곡1동</td>\n","      <td>5106</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","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>263</th>\n","      <td>328.500000</td>\n","      <td>2.833333</td>\n","      <td>870.333313</td>\n","      <td>0.0</td>\n","      <td>1021.091675</td>\n","      <td>482.331116</td>\n","      <td>1.0</td>\n","      <td>Cluster 6</td>\n","      <td>중곡4동</td>\n","      <td>5500</td>\n","    </tr>\n","    <tr>\n","      <th>264</th>\n","      <td>2642.166748</td>\n","      <td>214.333328</td>\n","      <td>5278.500000</td>\n","      <td>0.0</td>\n","      <td>242.431580</td>\n","      <td>507.920258</td>\n","      <td>0.0</td>\n","      <td>Cluster 6</td>\n","      <td>중곡3동</td>\n","      <td>5755</td>\n","    </tr>\n","    <tr>\n","      <th>265</th>\n","      <td>459.666656</td>\n","      <td>86.833336</td>\n","      <td>884.833313</td>\n","      <td>0.0</td>\n","      <td>119.009697</td>\n","      <td>1498.395386</td>\n","      <td>0.0</td>\n","      <td>Cluster 6</td>\n","      <td>광장동</td>\n","      <td>5990</td>\n","    </tr>\n","    <tr>\n","      <th>266</th>\n","      <td>217.333328</td>\n","      <td>19.000000</td>\n","      <td>376.500000</td>\n","      <td>0.0</td>\n","      <td>145.110886</td>\n","      <td>1499.883301</td>\n","      <td>0.0</td>\n","      <td>Cluster 6</td>\n","      <td>광장동</td>\n","      <td>5671</td>\n","    </tr>\n","    <tr>\n","      <th>267</th>\n","      <td>500.666656</td>\n","      <td>29.666666</td>\n","      <td>1005.333313</td>\n","      <td>0.0</td>\n","      <td>603.259033</td>\n","      <td>437.388733</td>\n","      <td>0.0</td>\n","      <td>Cluster 6</td>\n","      <td>중곡3동</td>\n","      <td>5511</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>188 rows × 10 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd39d955-289f-4f33-8427-270e5ceeee5f')\"\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 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.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-cd39d955-289f-4f33-8427-270e5ceeee5f 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-cd39d955-289f-4f33-8427-270e5ceeee5f');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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'버스정류장ARS번호'].values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681567265590,"user_tz":-540,"elapsed":574,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"f8fce3b8-b21d-4112-992e-37ec2b0f2f13","id":"tOIQYHh5QzUw"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5118, 5001, 5002, 5003, 5004, 5005, 5006, 5007, 5140, 5141, 5145,\n","       5148, 5175, 5188, 5189, 5190, 5191, 5193, 5194, 5204, 5205, 5216,\n","       5217, 5218, 5220, 5232, 5250, 5251, 5252, 5253, 5162, 5192, 5249,\n","       5011, 5594, 5582])"]},"metadata":{},"execution_count":56}]},{"cell_type":"markdown","source":["### 행정동 포함"],"metadata":{"id":"KvWxf99USfoP"}},{"cell_type":"code","source":["# 클러스터링 결과 원본데이터 받아오기\n","s = setup(data, normalize=True, session_id=22, ignore_features=['표준버스정류장ID', '버스정류장ARS번호', '행정동', '대규모점포', '사업체수', '종사자수', '낮시간대 생활인구', '밤시간대 생활인구'])\n","\n","result = assign_model(optics)\n","\n","df_cluster = pd.concat([result, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"executionInfo":{"status":"ok","timestamp":1681568500593,"user_tz":-540,"elapsed":505,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"1300b4e7-c2a0-4ec8-fc0a-dbde73318b81","id":"AphuVXfySp_i"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7f2a04dbb1f0>"],"text/html":["<style type=\"text/css\">\n","#T_2683f_row5_col1, #T_2683f_row9_col1 {\n","  background-color: lightgreen;\n","}\n","</style>\n","<table id=\"T_2683f\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_2683f_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n","      <th id=\"T_2683f_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_2683f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_2683f_row0_col0\" class=\"data row0 col0\" >Session id</td>\n","      <td id=\"T_2683f_row0_col1\" class=\"data row0 col1\" >22</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_2683f_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n","      <td id=\"T_2683f_row1_col1\" class=\"data row1 col1\" >(269, 18)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_2683f_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n","      <td id=\"T_2683f_row2_col1\" class=\"data row2 col1\" >(269, 10)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_2683f_row3_col0\" class=\"data row3 col0\" >Ignore features</td>\n","      <td id=\"T_2683f_row3_col1\" class=\"data row3 col1\" >8</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_2683f_row4_col0\" class=\"data row4 col0\" >Numeric features</td>\n","      <td id=\"T_2683f_row4_col1\" class=\"data row4 col1\" >10</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_2683f_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n","      <td id=\"T_2683f_row5_col1\" class=\"data row5 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_2683f_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n","      <td id=\"T_2683f_row6_col1\" class=\"data row6 col1\" >simple</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_2683f_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n","      <td id=\"T_2683f_row7_col1\" class=\"data row7 col1\" >mean</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_2683f_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n","      <td id=\"T_2683f_row8_col1\" class=\"data row8 col1\" >mode</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_2683f_row9_col0\" class=\"data row9 col0\" >Normalize</td>\n","      <td id=\"T_2683f_row9_col1\" class=\"data row9 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n","      <td id=\"T_2683f_row10_col0\" class=\"data row10 col0\" >Normalize method</td>\n","      <td id=\"T_2683f_row10_col1\" class=\"data row10 col1\" >zscore</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n","      <td id=\"T_2683f_row11_col0\" class=\"data row11 col0\" >CPU Jobs</td>\n","      <td id=\"T_2683f_row11_col1\" class=\"data row11 col1\" >-1</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n","      <td id=\"T_2683f_row12_col0\" class=\"data row12 col0\" >Use GPU</td>\n","      <td id=\"T_2683f_row12_col1\" class=\"data row12 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n","      <td id=\"T_2683f_row13_col0\" class=\"data row13 col0\" >Log Experiment</td>\n","      <td id=\"T_2683f_row13_col1\" class=\"data row13 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n","      <td id=\"T_2683f_row14_col0\" class=\"data row14 col0\" >Experiment Name</td>\n","      <td id=\"T_2683f_row14_col1\" class=\"data row14 col1\" >cluster-default-name</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_2683f_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n","      <td id=\"T_2683f_row15_col0\" class=\"data row15 col0\" >USI</td>\n","      <td id=\"T_2683f_row15_col1\" class=\"data row15 col1\" >e86d</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}}]},{"cell_type":"code","source":["# 모델 생성 (Functional API)\n","sc = create_model('sc', num_clusters=7)\n","\n","evaluate_model(sc)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["f69c5b2e8614455fb27acc6a2db254ad","02a12a28dfff4769b1b940e1430b967b","fbb24f08a24b4ee19429a4ab6617d9a3","cc6376371b704e99b2a54c84f115ca5f","bf5a25a2bc784f40ae344fabf4b18548","8883275d0286415caa632c292ce5f097","a6fe4aef38174076ac3aed67b2e9c933","64f4b8c781fb4dc79e284b8cfa0b1b19","71733d6e8a6644c7a0fb4467edb9f24c","cd3db0d1795a47ea9edd4ea6e61723bf","68e3c38afa7d4d1d88e1e39dbe23e6c6","5cefa63bc35e4f4592ffe2af109a0d4c","9b8b73277b414ce0a9c5064cfbde5676","4b59177f8d0e4c6abbdf733d5611c725","fb0b909488aa458bac27a001e57f81f8","8270cb1daac04aa7b7ccdf29c1ccb1b1","3c41c50ad57f44cd9116ad498e6158e9","fce5ac4c87444a3e94aa7d518c18e1ce"]},"executionInfo":{"status":"ok","timestamp":1681568507743,"user_tz":-540,"elapsed":1675,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"6f39e685-ce56-476a-8e75-7af92c1beccb","id":"yImBDBrrSp_j"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7f2a044b8af0>"],"text/html":["<style type=\"text/css\">\n","</style>\n","<table id=\"T_9d6af\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_9d6af_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n","      <th id=\"T_9d6af_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n","      <th id=\"T_9d6af_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n","      <th id=\"T_9d6af_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n","      <th id=\"T_9d6af_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n","      <th id=\"T_9d6af_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_9d6af_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_9d6af_row0_col0\" class=\"data row0 col0\" >0.2735</td>\n","      <td id=\"T_9d6af_row0_col1\" class=\"data row0 col1\" >39.2691</td>\n","      <td id=\"T_9d6af_row0_col2\" class=\"data row0 col2\" >1.0509</td>\n","      <td id=\"T_9d6af_row0_col3\" class=\"data row0 col3\" >0</td>\n","      <td id=\"T_9d6af_row0_col4\" class=\"data row0 col4\" >0</td>\n","      <td id=\"T_9d6af_row0_col5\" class=\"data row0 col5\" >0</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/3 [00:00<?, 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클러스터링 결과 원본데이터 받아오기\n","result = assign_model(sc)\n","\n","df_cluster = pd.concat([result, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"id":"ZFvfqcFKSp_j"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 건대입구 사거리는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568517231,"user_tz":-540,"elapsed":15,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"eb61aaad-4ad5-4055-dd27-2552ea830f57","id":"dbbHnBZsSp_j"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수      밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","114  35146.332031  10774.666992  86699.335938     12.0           59.584316   \n","\n","     market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","114       328.849854    5.0  652387776.0         0.08093      0.490105   \n","\n","       Cluster  행정동  버스정류장ARS번호  \n","114  Cluster 2  화양동        5232  "],"text/html":["\n","  <div id=\"df-27098f4c-a45b-4293-9854-f57da963ceb5\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>총생활인구수</th>\n","      <th>65세 이상 생활인구 비율</th>\n","      <th>1020 생활인구 비율</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>114</th>\n","      <td>35146.332031</td>\n","      <td>10774.666992</td>\n","      <td>86699.335938</td>\n","      <td>12.0</td>\n","      <td>59.584316</td>\n","      <td>328.849854</td>\n","      <td>5.0</td>\n","      <td>652387776.0</td>\n","      <td>0.08093</td>\n","      <td>0.490105</td>\n","      <td>Cluster 2</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27098f4c-a45b-4293-9854-f57da963ceb5')\"\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 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5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568517231,"user_tz":-540,"elapsed":13,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"534c3d12-de92-490e-a27c-7a9788cd8245","id":"PbR3GqPESp_j"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","89   20957.0    4974.0  53744.167969      4.0            97.78656   \n","\n","    market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","89       241.094788    1.0  412792672.0        0.137371      0.249907   \n","\n","      Cluster   행정동  버스정류장ARS번호  \n","89  Cluster 2  자양3동        5204  "],"text/html":["\n","  <div id=\"df-9788c5af-47c8-442e-b82f-cff9c60e25b5\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<td>0.137371</td>\n","      <td>0.249907</td>\n","      <td>Cluster 2</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9788c5af-47c8-442e-b82f-cff9c60e25b5')\"\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 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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":92}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5169]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568517232,"user_tz":-540,"elapsed":13,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"da74e42a-c05c-4000-b1a9-7e2fa9fd13b7","id":"DxYsgt-bSp_j"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수    밤시간대총승객수       일일총승객수  환승가능노선수  starbucks_distance  \\\n","68    3450.0  276.166656  7934.666504      1.0          340.466675   \n","\n","    market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","68      1968.143066    1.0  638349888.0        0.118303      0.274873   \n","\n","      Cluster  행정동  버스정류장ARS번호  \n","68  Cluster 1  광장동        5169  "],"text/html":["\n","  <div id=\"df-85623788-fa35-44e9-a099-601bb34b5e29\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<td>0.118303</td>\n","      <td>0.274873</td>\n","      <td>Cluster 1</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85623788-fa35-44e9-a099-601bb34b5e29')\"\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 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2']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":549},"executionInfo":{"status":"ok","timestamp":1681568520839,"user_tz":-540,"elapsed":924,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"84830cdc-f3b4-4a96-c5e3-da149943470b","id":"ldB1mi-3Sp_j"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수      밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","1    15681.333008   3756.500000  39580.667969      7.0          350.338501   \n","3    15281.500000   4266.833496  36958.000000      6.0          318.242950   \n","4    18719.500000   6868.500000  48762.500000      6.0          238.122986   \n","6    19870.000000   4863.500000  44842.500000      5.0           78.215050   \n","7    26958.833984   6447.333496  65456.500000      7.0          216.765747   \n","53   24268.166016   4937.500000  54723.332031      8.0          223.763870   \n","89   20957.000000   4974.000000  53744.167969      4.0           97.786560   \n","100  20126.666016   5016.500000  52191.332031      4.0          128.844650   \n","114  35146.332031  10774.666992  86699.335938     12.0           59.584316   \n","\n","     market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","1         723.257324    3.0  270830848.0        0.145448      0.223181   \n","3         562.888428    1.0  323892352.0        0.146777      0.232630   \n","4         637.437317    1.0  273699712.0        0.129653      0.268713   \n","6         702.566589    1.0  375967552.0        0.158670      0.236506   \n","7         557.314453    1.0  273699712.0        0.129653      0.268713   \n","53        601.368591    1.0  443511968.0        0.127300      0.264467   \n","89        241.094788    1.0  412792672.0        0.137371      0.249907   \n","100       215.016281    1.0  441175776.0        0.142101      0.270933   \n","114       328.849854    5.0  652387776.0        0.080930      0.490105   \n","\n","       Cluster   행정동  버스정류장ARS번호  \n","1    Cluster 2  중곡1동        5001  \n","3    Cluster 2  중곡2동        5003  \n","4    Cluster 2    능동        5004  \n","6    Cluster 2  구의2동        5006  \n","7    Cluster 2    능동        5007  \n","53   Cluster 2  구의1동        5148  \n","89   Cluster 2  자양3동        5204  \n","100  Cluster 2  자양4동        5218  \n","114  Cluster 2   화양동        5232  "],"text/html":["\n","  <div id=\"df-becb983c-a345-47f8-a403-275417c196b1\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>총생활인구수</th>\n","      <th>65세 이상 생활인구 비율</th>\n","      <th>1020 생활인구 비율</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>15681.333008</td>\n","      <td>3756.500000</td>\n","      <td>39580.667969</td>\n","      <td>7.0</td>\n","      <td>350.338501</td>\n","      <td>723.257324</td>\n","      <td>3.0</td>\n","      <td>270830848.0</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>Cluster 2</td>\n","      <td>중곡1동</td>\n","      <td>5001</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>15281.500000</td>\n","      <td>4266.833496</td>\n","      <td>36958.000000</td>\n","      <td>6.0</td>\n","      <td>318.242950</td>\n","      <td>562.888428</td>\n","      <td>1.0</td>\n","      <td>323892352.0</td>\n","      <td>0.146777</td>\n","      <td>0.232630</td>\n","      <td>Cluster 2</td>\n","      <td>중곡2동</td>\n","      <td>5003</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>18719.500000</td>\n","      <td>6868.500000</td>\n","      <td>48762.500000</td>\n","      <td>6.0</td>\n","      <td>238.122986</td>\n","      <td>637.437317</td>\n","      <td>1.0</td>\n","      <td>273699712.0</td>\n","      <td>0.129653</td>\n","      <td>0.268713</td>\n","      <td>Cluster 2</td>\n","      <td>능동</td>\n","      <td>5004</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>19870.000000</td>\n","      <td>4863.500000</td>\n","      <td>44842.500000</td>\n","      <td>5.0</td>\n","      <td>78.215050</td>\n","      <td>702.566589</td>\n","      <td>1.0</td>\n","      <td>375967552.0</td>\n","      <td>0.158670</td>\n","      <td>0.236506</td>\n","      <td>Cluster 2</td>\n","      <td>구의2동</td>\n","      <td>5006</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>26958.833984</td>\n","      <td>6447.333496</td>\n","      <td>65456.500000</td>\n","      <td>7.0</td>\n","      <td>216.765747</td>\n","      <td>557.314453</td>\n","      <td>1.0</td>\n","      <td>273699712.0</td>\n","      <td>0.129653</td>\n","      <td>0.268713</td>\n","      <td>Cluster 2</td>\n","      <td>능동</td>\n","      <td>5007</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>24268.166016</td>\n","      <td>4937.500000</td>\n","      <td>54723.332031</td>\n","      <td>8.0</td>\n","      <td>223.763870</td>\n","      <td>601.368591</td>\n","      <td>1.0</td>\n","      <td>443511968.0</td>\n","      <td>0.127300</td>\n","      <td>0.264467</td>\n","      <td>Cluster 2</td>\n","      <td>구의1동</td>\n","      <td>5148</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>20957.000000</td>\n","      <td>4974.000000</td>\n","      <td>53744.167969</td>\n","      <td>4.0</td>\n","      <td>97.786560</td>\n","      <td>241.094788</td>\n","      <td>1.0</td>\n","      <td>412792672.0</td>\n","      <td>0.137371</td>\n","      <td>0.249907</td>\n","      <td>Cluster 2</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>20126.666016</td>\n","      <td>5016.500000</td>\n","      <td>52191.332031</td>\n","      <td>4.0</td>\n","      <td>128.844650</td>\n","      <td>215.016281</td>\n","      <td>1.0</td>\n","      <td>441175776.0</td>\n","      <td>0.142101</td>\n","      <td>0.270933</td>\n","      <td>Cluster 2</td>\n","      <td>자양4동</td>\n","      <td>5218</td>\n","    </tr>\n","    <tr>\n","      <th>114</th>\n","      <td>35146.332031</td>\n","      <td>10774.666992</td>\n","      <td>86699.335938</td>\n","      <td>12.0</td>\n","      <td>59.584316</td>\n","      <td>328.849854</td>\n","      <td>5.0</td>\n","      <td>652387776.0</td>\n","      <td>0.080930</td>\n","      <td>0.490105</td>\n","      <td>Cluster 2</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-becb983c-a345-47f8-a403-275417c196b1')\"\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 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'버스정류장ARS번호'].values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681567265590,"user_tz":-540,"elapsed":574,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"f8fce3b8-b21d-4112-992e-37ec2b0f2f13","id":"6__6Twl3Sp_j"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5118, 5001, 5002, 5003, 5004, 5005, 5006, 5007, 5140, 5141, 5145,\n","       5148, 5175, 5188, 5189, 5190, 5191, 5193, 5194, 5204, 5205, 5216,\n","       5217, 5218, 5220, 5232, 5250, 5251, 5252, 5253, 5162, 5192, 5249,\n","       5011, 5594, 5582])"]},"metadata":{},"execution_count":56}]},{"cell_type":"markdown","source":["## ap"],"metadata":{"id":"-weMlgM-S2vl"}},{"cell_type":"code","source":["# 클러스터링 결과 원본데이터 받아오기\n","s = setup(data, normalize=True, session_id=22, ignore_features=['표준버스정류장ID', '버스정류장ARS번호', '행정동', '대규모점포', '사업체수', '종사자수', '낮시간대 생활인구', '밤시간대 생활인구'])\n","\n","# 모델 생성 (Functional API)\n","ap = create_model('ap', num_clusters=6)\n","\n","evaluate_model(ap)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["274ccc198ccf4711b54ef88011e3a501","12885f1e40384fa6842e5c3d00ab363f","1d2dea6d87d6467c88e2b9ae7e48e908","627110400e704ea09d6a9788473fca72","b0604073a99f42958a0a593c05429587","fc8db775cba14f6e9fb0a5bcbd646b40","a87b345150ed44d08715d1e5317edd0c","e11b28ff046d452987990663c73df394","19672a19a0234dc9bca14a357a9d9001","b30d4414603b4070b32412aa0fb0d189","019700c71b8d44f884852db72731bd18","95e00db52b3743bcbf301875f8b7bb69","f85ff383dc484b7f8d30c1a8e6221d15","a8e9be1c081146a6a4f9e4a2d74fded4","4f238c471b9048e98c1de39c16ed6446","82d9e7c41d6e4d8190d45b23f6e5fc4e","643e25adc7d54c06a2433e198618c449","6690919b81ca4a19850c283043563255"]},"executionInfo":{"status":"ok","timestamp":1681568776130,"user_tz":-540,"elapsed":1382,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"3355a3c0-413b-4226-b677-31e8073f2ed2","id":"FpY4C25RTdkN"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7f2a0443d5e0>"],"text/html":["<style type=\"text/css\">\n","#T_0a2b3_row5_col1, #T_0a2b3_row9_col1 {\n","  background-color: lightgreen;\n","}\n","</style>\n","<table id=\"T_0a2b3\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_0a2b3_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n","      <th id=\"T_0a2b3_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_0a2b3_row0_col0\" class=\"data row0 col0\" >Session id</td>\n","      <td id=\"T_0a2b3_row0_col1\" class=\"data row0 col1\" >22</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n","      <td id=\"T_0a2b3_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n","      <td id=\"T_0a2b3_row1_col1\" class=\"data row1 col1\" >(269, 18)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n","      <td id=\"T_0a2b3_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n","      <td id=\"T_0a2b3_row2_col1\" class=\"data row2 col1\" >(269, 10)</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n","      <td id=\"T_0a2b3_row3_col0\" class=\"data row3 col0\" >Ignore features</td>\n","      <td id=\"T_0a2b3_row3_col1\" class=\"data row3 col1\" >8</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n","      <td id=\"T_0a2b3_row4_col0\" class=\"data row4 col0\" >Numeric features</td>\n","      <td id=\"T_0a2b3_row4_col1\" class=\"data row4 col1\" >10</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n","      <td id=\"T_0a2b3_row5_col0\" class=\"data row5 col0\" >Preprocess</td>\n","      <td id=\"T_0a2b3_row5_col1\" class=\"data row5 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n","      <td id=\"T_0a2b3_row6_col0\" class=\"data row6 col0\" >Imputation type</td>\n","      <td id=\"T_0a2b3_row6_col1\" class=\"data row6 col1\" >simple</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n","      <td id=\"T_0a2b3_row7_col0\" class=\"data row7 col0\" >Numeric imputation</td>\n","      <td id=\"T_0a2b3_row7_col1\" class=\"data row7 col1\" >mean</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n","      <td id=\"T_0a2b3_row8_col0\" class=\"data row8 col0\" >Categorical imputation</td>\n","      <td id=\"T_0a2b3_row8_col1\" class=\"data row8 col1\" >mode</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n","      <td id=\"T_0a2b3_row9_col0\" class=\"data row9 col0\" >Normalize</td>\n","      <td id=\"T_0a2b3_row9_col1\" class=\"data row9 col1\" >True</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n","      <td id=\"T_0a2b3_row10_col0\" class=\"data row10 col0\" >Normalize method</td>\n","      <td id=\"T_0a2b3_row10_col1\" class=\"data row10 col1\" >zscore</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n","      <td id=\"T_0a2b3_row11_col0\" class=\"data row11 col0\" >CPU Jobs</td>\n","      <td id=\"T_0a2b3_row11_col1\" class=\"data row11 col1\" >-1</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n","      <td id=\"T_0a2b3_row12_col0\" class=\"data row12 col0\" >Use GPU</td>\n","      <td id=\"T_0a2b3_row12_col1\" class=\"data row12 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n","      <td id=\"T_0a2b3_row13_col0\" class=\"data row13 col0\" >Log Experiment</td>\n","      <td id=\"T_0a2b3_row13_col1\" class=\"data row13 col1\" >False</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n","      <td id=\"T_0a2b3_row14_col0\" class=\"data row14 col0\" >Experiment Name</td>\n","      <td id=\"T_0a2b3_row14_col1\" class=\"data row14 col1\" >cluster-default-name</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_0a2b3_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n","      <td id=\"T_0a2b3_row15_col0\" class=\"data row15 col0\" >USI</td>\n","      <td id=\"T_0a2b3_row15_col1\" class=\"data row15 col1\" >8eef</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<pandas.io.formats.style.Styler at 0x7f2a0443d7f0>"],"text/html":["<style type=\"text/css\">\n","</style>\n","<table id=\"T_c6d3c\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_c6d3c_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n","      <th id=\"T_c6d3c_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n","      <th id=\"T_c6d3c_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n","      <th id=\"T_c6d3c_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n","      <th id=\"T_c6d3c_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n","      <th id=\"T_c6d3c_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_c6d3c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n","      <td id=\"T_c6d3c_row0_col0\" class=\"data row0 col0\" >0.3087</td>\n","      <td id=\"T_c6d3c_row0_col1\" class=\"data row0 col1\" >76.0569</td>\n","      <td id=\"T_c6d3c_row0_col2\" class=\"data row0 col2\" >1.0838</td>\n","      <td id=\"T_c6d3c_row0_col3\" class=\"data row0 col3\" >0</td>\n","      <td id=\"T_c6d3c_row0_col4\" class=\"data row0 col4\" >0</td>\n","      <td id=\"T_c6d3c_row0_col5\" class=\"data row0 col5\" >0</td>\n","    </tr>\n","  </tbody>\n","</table>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Processing:   0%|          | 0/3 [00:00<?, 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클러스터링 결과 원본데이터 받아오기\n","result = assign_model(sc)\n","\n","df_cluster = pd.concat([result, data[['행정동', '버스정류장ARS번호']]], axis=1)"],"metadata":{"id":"5-R8iHNTTdkO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 건대입구 사거리는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5232]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568785374,"user_tz":-540,"elapsed":14,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"7301f950-26e6-4be2-f609-de810656364c","id":"UqONbYYlTdkO"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         낮시간대총승객수      밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","114  35146.332031  10774.666992  86699.335938     12.0           59.584316   \n","\n","     market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","114       328.849854    5.0  652387776.0         0.08093      0.490105   \n","\n","       Cluster  행정동  버스정류장ARS번호  \n","114  Cluster 2  화양동        5232  "],"text/html":["\n","  <div id=\"df-19580281-67ce-424c-9520-f3d12a425564\">\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>낮시간대총승객수</th>\n","      <th>밤시간대총승객수</th>\n","      <th>일일총승객수</th>\n","      <th>환승가능노선수</th>\n","      <th>starbucks_distance</th>\n","      <th>market_distance</th>\n","      <th>count</th>\n","      <th>총생활인구수</th>\n","      <th>65세 이상 생활인구 비율</th>\n","      <th>1020 생활인구 비율</th>\n","      <th>Cluster</th>\n","      <th>행정동</th>\n","      <th>버스정류장ARS번호</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>114</th>\n","      <td>35146.332031</td>\n","      <td>10774.666992</td>\n","      <td>86699.335938</td>\n","      <td>12.0</td>\n","      <td>59.584316</td>\n","      <td>328.849854</td>\n","      <td>5.0</td>\n","      <td>652387776.0</td>\n","      <td>0.08093</td>\n","      <td>0.490105</td>\n","      <td>Cluster 2</td>\n","      <td>화양동</td>\n","      <td>5232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19580281-67ce-424c-9520-f3d12a425564')\"\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 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5204]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568785374,"user_tz":-540,"elapsed":12,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"b1b00fec-31ce-4bae-b3f6-4b6f7321d4ba","id":"wBW__lD5TdkO"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수  밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","89   20957.0    4974.0  53744.167969      4.0            97.78656   \n","\n","    market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","89       241.094788    1.0  412792672.0        0.137371      0.249907   \n","\n","      Cluster   행정동  버스정류장ARS번호  \n","89  Cluster 2  자양3동        5204  "],"text/html":["\n","  <div id=\"df-1ed55fb5-313a-4d62-bddc-cbe19b2a222e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<td>0.137371</td>\n","      <td>0.249907</td>\n","      <td>Cluster 2</td>\n","      <td>자양3동</td>\n","      <td>5204</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ed55fb5-313a-4d62-bddc-cbe19b2a222e')\"\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 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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":106}]},{"cell_type":"code","source":["# 모범사례는 어느 클러스터에?\n","df_cluster.loc[df_cluster['버스정류장ARS번호'] == 5169]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"executionInfo":{"status":"ok","timestamp":1681568785375,"user_tz":-540,"elapsed":12,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"772bb6a4-04f7-49a8-990f-c4cf880ccefa","id":"n3-GFVUqTdkO"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    낮시간대총승객수    밤시간대총승객수       일일총승객수  환승가능노선수  starbucks_distance  \\\n","68    3450.0  276.166656  7934.666504      1.0          340.466675   \n","\n","    market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","68      1968.143066    1.0  638349888.0        0.118303      0.274873   \n","\n","      Cluster  행정동  버스정류장ARS번호  \n","68  Cluster 1  광장동        5169  "],"text/html":["\n","  <div id=\"df-8000c2e8-6727-4b62-9c4c-192683c6c68f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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<td>0.118303</td>\n","      <td>0.274873</td>\n","      <td>Cluster 1</td>\n","      <td>광장동</td>\n","      <td>5169</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8000c2e8-6727-4b62-9c4c-192683c6c68f')\"\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 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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":107}]},{"cell_type":"code","source":["# 같은 클러스터 확인\n","df_cluster.loc[df_cluster['Cluster'] == 'Cluster 1']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":658},"executionInfo":{"status":"ok","timestamp":1681568797594,"user_tz":-540,"elapsed":1116,"user":{"displayName":"강창진","userId":"11872539475704303310"}},"outputId":"1c495b32-c9d4-4fdc-ac41-97596d57bf8b","id":"6ZNdWPWQTdkO"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        낮시간대총승객수     밤시간대총승객수        일일총승객수  환승가능노선수  starbucks_distance  \\\n","8    1519.000000  1228.833374   4874.166504      3.0          571.938110   \n","9    1342.833374   798.166687   3710.333252      4.0          646.614136   \n","10   1305.166626   227.000000   2739.166748      1.0          241.688660   \n","11   3237.666748   972.166687   8972.333008      2.0          226.030991   \n","12   1051.333374   138.833328   2374.666748      2.0          314.448303   \n","..           ...          ...           ...      ...                 ...   \n","264  2642.166748   214.333328   5278.500000      0.0          242.431580   \n","265   459.666656    86.833336    884.833313      0.0          119.009697   \n","266   217.333328    19.000000    376.500000      0.0          145.110886   \n","267   500.666656    29.666666   1005.333313      0.0          603.259033   \n","268  8809.500000  1776.166626  22553.333984      0.0          542.553833   \n","\n","     market_distance  count       총생활인구수  65세 이상 생활인구 비율  1020 생활인구 비율  \\\n","8         569.341797    1.0  270830848.0        0.145448      0.223181   \n","9         536.140442    1.0  270830848.0        0.145448      0.223181   \n","10        579.848022    0.0  270830848.0        0.145448      0.223181   \n","11        587.225281    0.0  270830848.0        0.145448      0.223181   \n","12        445.938416    0.0  270830848.0        0.145448      0.223181   \n","..               ...    ...          ...             ...           ...   \n","264       507.920258    0.0  270780160.0        0.173462      0.204332   \n","265      1498.395386    0.0  638349888.0        0.118303      0.274873   \n","266      1499.883301    0.0  638349888.0        0.118303      0.274873   \n","267       437.388733    0.0  270780160.0        0.173462      0.204332   \n","268      1179.830322    0.0  536459488.0        0.130642      0.244171   \n","\n","       Cluster   행정동  버스정류장ARS번호  \n","8    Cluster 1  중곡1동        5101  \n","9    Cluster 1  중곡1동        5102  \n","10   Cluster 1  중곡1동        5103  \n","11   Cluster 1  중곡1동        5104  \n","12   Cluster 1  중곡1동        5105  \n","..         ...   ...         ...  \n","264  Cluster 1  중곡3동        5755  \n","265  Cluster 1   광장동        5990  \n","266  Cluster 1   광장동        5671  \n","267  Cluster 1  중곡3동        5511  \n","268  Cluster 1  구의3동        5999  \n","\n","[221 rows x 13 columns]"],"text/html":["\n","  <div id=\"df-a754ed8b-7820-4de9-a6b0-b5de2011bb49\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","       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<td>270830848.0</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>Cluster 1</td>\n","      <td>중곡1동</td>\n","      <td>5101</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1342.833374</td>\n","      <td>798.166687</td>\n","      <td>3710.333252</td>\n","      <td>4.0</td>\n","      <td>646.614136</td>\n","      <td>536.140442</td>\n","      <td>1.0</td>\n","      <td>270830848.0</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>Cluster 1</td>\n","      <td>중곡1동</td>\n","      <td>5102</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1305.166626</td>\n","      <td>227.000000</td>\n","      <td>2739.166748</td>\n","      <td>1.0</td>\n","      <td>241.688660</td>\n","      <td>579.848022</td>\n","      <td>0.0</td>\n","      <td>270830848.0</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>Cluster 1</td>\n","      <td>중곡1동</td>\n","      <td>5103</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>3237.666748</td>\n","      <td>972.166687</td>\n","      <td>8972.333008</td>\n","      <td>2.0</td>\n","      <td>226.030991</td>\n","      <td>587.225281</td>\n","      <td>0.0</td>\n","      <td>270830848.0</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>Cluster 1</td>\n","      <td>중곡1동</td>\n","      <td>5104</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>1051.333374</td>\n","      <td>138.833328</td>\n","      <td>2374.666748</td>\n","      <td>2.0</td>\n","      <td>314.448303</td>\n","      <td>445.938416</td>\n","      <td>0.0</td>\n","      <td>270830848.0</td>\n","      <td>0.145448</td>\n","      <td>0.223181</td>\n","      <td>Cluster 1</td>\n","      <td>중곡1동</td>\n","      <td>5105</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","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>264</th>\n","      <td>2642.166748</td>\n","      <td>214.333328</td>\n","      <td>5278.500000</td>\n","      <td>0.0</td>\n","      <td>242.431580</td>\n","      <td>507.920258</td>\n","      <td>0.0</td>\n","      <td>270780160.0</td>\n","      <td>0.173462</td>\n","      <td>0.204332</td>\n","      <td>Cluster 1</td>\n","      <td>중곡3동</td>\n","      <td>5755</td>\n","    </tr>\n","    <tr>\n","      <th>265</th>\n","      <td>459.666656</td>\n","      <td>86.833336</td>\n","      <td>884.833313</td>\n","      <td>0.0</td>\n","      <td>119.009697</td>\n","      <td>1498.395386</td>\n","      <td>0.0</td>\n","      <td>638349888.0</td>\n","      <td>0.118303</td>\n","      <td>0.274873</td>\n","      <td>Cluster 1</td>\n","      <td>광장동</td>\n","      <td>5990</td>\n","    </tr>\n","    <tr>\n","      <th>266</th>\n","      <td>217.333328</td>\n","      <td>19.000000</td>\n","      <td>376.500000</td>\n","      <td>0.0</td>\n","      <td>145.110886</td>\n","      <td>1499.883301</td>\n","      <td>0.0</td>\n","      <td>638349888.0</td>\n","      <td>0.118303</td>\n","      <td>0.274873</td>\n","      <td>Cluster 1</td>\n","      <td>광장동</td>\n","      <td>5671</td>\n","    </tr>\n","    <tr>\n","      <th>267</th>\n","      <td>500.666656</td>\n","      <td>29.666666</td>\n","      <td>1005.333313</td>\n","      <td>0.0</td>\n","      <td>603.259033</td>\n","      <td>437.388733</td>\n","      <td>0.0</td>\n","      <td>270780160.0</td>\n","      <td>0.173462</td>\n","      <td>0.204332</td>\n","      <td>Cluster 1</td>\n","      <td>중곡3동</td>\n","      <td>5511</td>\n","    </tr>\n","    <tr>\n","      <th>268</th>\n","      <td>8809.500000</td>\n","      <td>1776.166626</td>\n","      <td>22553.333984</td>\n","      <td>0.0</td>\n","      <td>542.553833</td>\n","   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