Preprocessing.ipynb

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      "metadata": {
        "id": "Ij9gDXom0C1c"
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      "source": [
        " import pandas as pd\n",
        "from sklearn.impute import KNNImputer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 일자별 시간대별 대여내역 및 미세먼지 집계"
      ],
      "metadata": {
        "id": "TJ5yNYB76RNa"
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      "source": [
        "rentdust_2020=pd.DataFrame(columns=['대여일자','측정시간','count','SO2','CO','O3','NO2','PM10','PM25'])\n",
        "rentdust_2021=pd.DataFrame(columns=['대여일자','측정시간','count','SO2','CO','O3','NO2','PM10','PM25'])\n",
        "rentdust_2022=pd.DataFrame(columns=['대여일자','측정시간','count','SO2','CO','O3','NO2','PM10','PM25'])"
      ],
      "metadata": {
        "id": "SEm72wDd9mVB"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "#2020\n",
        "for i in range(12):\n",
        "  lst=['01','02','03','04','05','06','07','08','09','10','11','12']\n",
        "  rent_record=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/b_2020'+str(lst[i])+'.csv')\n",
        "  rent_record1=rent_record[['대여일자','대여소번호','측정시간']]\n",
        "  rent_record2=rent_record[['대여일자','SO2','CO','O3','NO2','PM10','PM25','측정시간']]\n",
        "  hourly_count=rent_record1.groupby(['대여일자','측정시간']).agg('count')\n",
        "  hourly_dust=rent_record2.groupby(['대여일자','측정시간']).agg('max')\n",
        "  rentdust=hourly_count.join(hourly_dust)\n",
        "  rentdust.columns=['count','SO2','CO','O3','NO2','PM10','PM25']\n",
        "  rentdust.reset_index(inplace=True)\n",
        "  rentdust_2020=rentdust_2020.append(rentdust)\n",
        "rentdust_2020['D-H']=rentdust_2020['대여일자']+'-'+rentdust_2020['측정시간'].astype(str)\n",
        "rentdust_2020=rentdust_2020[['대여일자','측정시간','D-H','SO2','CO','O3','NO2','PM10','PM25','count']]\n",
        "imputer0=KNNImputer(n_neighbors=2)\n",
        "imputing0=rentdust_2020[['SO2','CO','O3','NO2','PM10','PM25']]\n",
        "imputed0=pd.DataFrame(imputer0.fit_transform(imputing0))\n",
        "imputed0.columns=[['SO2','CO','O3','NO2','PM10','PM25']]\n",
        "rentdust_2020['SO2']=imputed0['SO2']\n",
        "rentdust_2020['CO']=imputed0['CO']\n",
        "rentdust_2020['NO2']=imputed0['NO2']\n",
        "rentdust_2020['PM10']=imputed0['PM10']\n",
        "rentdust_2020['PM25']=imputed0['PM25']\n",
        "rentdust_2020"
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            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (9,10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
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              "           대여일자 측정시간            D-H    SO2   CO     O3    NO2  PM10  PM25  \\\n",
              "0    2020-01-01    0   2020-01-01-0  0.004  1.0  0.002  0.047  45.0  33.0   \n",
              "1    2020-01-01    1   2020-01-01-1  0.004  0.7  0.002  0.044  25.0  18.0   \n",
              "2    2020-01-01    2   2020-01-01-2  0.004  0.7  0.002  0.043  26.0  18.0   \n",
              "3    2020-01-01    3   2020-01-01-3  0.004  0.8  0.001  0.043  29.0  20.0   \n",
              "4    2020-01-01    4   2020-01-01-4  0.003  0.6  0.002  0.040  26.0  18.0   \n",
              "..          ...  ...            ...    ...  ...    ...    ...   ...   ...   \n",
              "739  2020-12-31   19  2020-12-31-19  0.003  1.0  0.010  0.037  93.0  70.0   \n",
              "740  2020-12-31   20  2020-12-31-20  0.004  0.8  0.013  0.052  53.0  40.0   \n",
              "741  2020-12-31   21  2020-12-31-21  0.004  0.7  0.017  0.046  51.0  40.0   \n",
              "742  2020-12-31   22  2020-12-31-22  0.003  0.9  0.017  0.053  48.0  42.0   \n",
              "743  2020-12-31   23  2020-12-31-23  0.003  0.7  0.022  0.045  51.0  39.0   \n",
              "\n",
              "    count  \n",
              "0      50  \n",
              "1      17  \n",
              "2      30  \n",
              "3      12  \n",
              "4      14  \n",
              "..    ...  \n",
              "739    43  \n",
              "740    45  \n",
              "741    30  \n",
              "742    18  \n",
              "743    14  \n",
              "\n",
              "[8732 rows x 10 columns]"
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              "      <th></th>\n",
              "      <th>대여일자</th>\n",
              "      <th>측정시간</th>\n",
              "      <th>D-H</th>\n",
              "      <th>SO2</th>\n",
              "      <th>CO</th>\n",
              "      <th>O3</th>\n",
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              "      <th>0</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-01-01-0</td>\n",
              "      <td>0.004</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.002</td>\n",
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              "      <td>0.004</td>\n",
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              "      <td>0.002</td>\n",
              "      <td>0.044</td>\n",
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              "      <td>18.0</td>\n",
              "      <td>17</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2</td>\n",
              "      <td>2020-01-01-2</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.7</td>\n",
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              "      <td>26.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30</td>\n",
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              "      <th>3</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>3</td>\n",
              "      <td>2020-01-01-3</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.043</td>\n",
              "      <td>29.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>4</td>\n",
              "      <td>2020-01-01-4</td>\n",
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              "      <td>0.6</td>\n",
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              "    <tr>\n",
              "      <th>739</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>19</td>\n",
              "      <td>2020-12-31-19</td>\n",
              "      <td>0.003</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.037</td>\n",
              "      <td>93.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>43</td>\n",
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              "      <th>740</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>20</td>\n",
              "      <td>2020-12-31-20</td>\n",
              "      <td>0.004</td>\n",
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              "      <td>53.0</td>\n",
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              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>21</td>\n",
              "      <td>2020-12-31-21</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.046</td>\n",
              "      <td>51.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>30</td>\n",
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              "    <tr>\n",
              "      <th>742</th>\n",
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              "      <td>22</td>\n",
              "      <td>2020-12-31-22</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.053</td>\n",
              "      <td>48.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>18</td>\n",
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              "      <th>743</th>\n",
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              "      <td>14</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "#2021\n",
        "for i in range(12):\n",
        "  lst=['01','02','03','04','05','06','07','08','09','10','11','12']\n",
        "  rent_record=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/b_2021'+str(lst[i])+'.csv')\n",
        "  rent_record1=rent_record[['대여일자','대여소번호','측정시간']]\n",
        "  rent_record2=rent_record[['대여일자','SO2','CO','O3','NO2','PM10','PM25','측정시간']]\n",
        "  hourly_count=rent_record1.groupby(['대여일자','측정시간']).agg('count')\n",
        "  hourly_dust=rent_record2.groupby(['대여일자','측정시간']).agg('max')\n",
        "  rentdust=hourly_count.join(hourly_dust)\n",
        "  rentdust.columns=['count','SO2','CO','O3','NO2','PM10','PM25']\n",
        "  rentdust.reset_index(inplace=True)\n",
        "  rentdust_2021=rentdust_2021.append(rentdust)\n",
        "rentdust_2021['D-H']=rentdust_2021['대여일자']+'-'+rentdust_2021['측정시간'].astype(str)\n",
        "rentdust_2021=rentdust_2021[['대여일자','측정시간','D-H','SO2','CO','O3','NO2','PM10','PM25','count']]\n",
        "imputer0=KNNImputer(n_neighbors=2)\n",
        "imputing0=rentdust_2021[['SO2','CO','O3','NO2','PM10','PM25']]\n",
        "imputed0=pd.DataFrame(imputer0.fit_transform(imputing0))\n",
        "imputed0.columns=[['SO2','CO','O3','NO2','PM10','PM25']]\n",
        "rentdust_2021['SO2']=imputed0['SO2']\n",
        "rentdust_2021['CO']=imputed0['CO']\n",
        "rentdust_2021['NO2']=imputed0['NO2']\n",
        "rentdust_2021['PM10']=imputed0['PM10']\n",
        "rentdust_2021['PM25']=imputed0['PM25']\n",
        "rentdust_2021"
      ],
      "metadata": {
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          "height": 458
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        "id": "lCoirlF_-epW",
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            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
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            "text/plain": [
              "           대여일자 측정시간            D-H    SO2   CO     O3    NO2   PM10  PM25  \\\n",
              "0    2021-01-01    0   2021-01-01-0  0.003  0.5  0.026  0.014   34.0  19.0   \n",
              "1    2021-01-01    1   2021-01-01-1  0.002  0.5  0.022  0.016   24.0  14.0   \n",
              "2    2021-01-01    2   2021-01-01-2  0.002  0.6  0.018  0.020   25.0  14.0   \n",
              "3    2021-01-01    3   2021-01-01-3  0.002  0.6  0.013  0.025   27.0  16.0   \n",
              "4    2021-01-01    4   2021-01-01-4  0.003  0.6  0.011  0.027   23.0  13.0   \n",
              "..          ...  ...            ...    ...  ...    ...    ...    ...   ...   \n",
              "738  2021-12-31   19  2021-12-31-19  0.004  0.7  0.026  0.021  126.0  76.0   \n",
              "739  2021-12-31   20  2021-12-31-20  0.004  0.5  0.025  0.013  104.0  47.0   \n",
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              "\n",
              "    count  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 355
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2022\n",
        "for i in range(3):\n",
        "  lst=['01','02','03']\n",
        "  rent_record=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/b_2022'+str(lst[i])+'.csv')\n",
        "  rent_record1=rent_record[['대여일자','대여소번호','측정시간']]\n",
        "  rent_record2=rent_record[['대여일자','SO2','CO','O3','NO2','PM10','PM25','측정시간']]\n",
        "  hourly_count=rent_record1.groupby(['대여일자','측정시간']).agg('count')\n",
        "  hourly_dust=rent_record2.groupby(['대여일자','측정시간']).agg('max')\n",
        "  rentdust=hourly_count.join(hourly_dust)\n",
        "  rentdust.columns=['count','SO2','CO','O3','NO2','PM10','PM25']\n",
        "  rentdust.reset_index(inplace=True)\n",
        "  rentdust_2022=rentdust_2022.append(rentdust)\n",
        "rentdust_2022['D-H']=rentdust_2022['대여일자']+'-'+rentdust_2022['측정시간'].astype(str)\n",
        "rentdust_2022=rentdust_2022[['대여일자','측정시간','D-H','SO2','CO','O3','NO2','PM10','PM25','count']]\n",
        "imputer0=KNNImputer(n_neighbors=2)\n",
        "imputing0=rentdust_2022[['SO2','CO','O3','NO2','PM10','PM25']]\n",
        "imputed0=pd.DataFrame(imputer0.fit_transform(imputing0))\n",
        "imputed0.columns=[['SO2','CO','O3','NO2','PM10','PM25']]\n",
        "rentdust_2022['SO2']=imputed0['SO2']\n",
        "rentdust_2022['CO']=imputed0['CO']\n",
        "rentdust_2022['NO2']=imputed0['NO2']\n",
        "rentdust_2022['PM10']=imputed0['PM10']\n",
        "rentdust_2022['PM25']=imputed0['PM25']\n",
        "rentdust_2022"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "qrXa5yAe-sSw",
        "outputId": "fb244bba-5d69-4415-9f4a-7bb6a08fd789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           대여일자 측정시간            D-H    SO2   CO     O3    NO2  PM10  PM25  \\\n",
              "0    2022-01-01    0   2022-01-01-0  0.005  1.2  0.002  0.064  42.0  26.0   \n",
              "1    2022-01-01    1   2022-01-01-1  0.003  0.4  0.026  0.016  23.0  12.0   \n",
              "2    2022-01-01    2   2022-01-01-2  0.003  0.4  0.022  0.020  20.0   9.0   \n",
              "3    2022-01-01    3   2022-01-01-3  0.003  0.5  0.014  0.028  20.0   9.0   \n",
              "4    2022-01-01    4   2022-01-01-4  0.003  0.5  0.016  0.027  19.0  10.0   \n",
              "..          ...  ...            ...    ...  ...    ...    ...   ...   ...   \n",
              "737  2022-03-31   19  2022-03-31-19  0.004  0.5  0.044  0.013  35.0  26.0   \n",
              "738  2022-03-31   20  2022-03-31-20  0.004  0.5  0.036  0.016  31.0  23.0   \n",
              "739  2022-03-31   21  2022-03-31-21  0.003  0.5  0.032  0.019  31.0  22.0   \n",
              "740  2022-03-31   22  2022-03-31-22  0.004  0.6  0.038  0.026  28.0  21.0   \n",
              "741  2022-03-31   23  2022-03-31-23  0.004  0.6  0.036  0.032  27.0  22.0   \n",
              "\n",
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              "0      28  \n",
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              "      <td>0.5</td>\n",
              "      <td>0.044</td>\n",
              "      <td>0.013</td>\n",
              "      <td>35.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>738</th>\n",
              "      <td>2022-03-31</td>\n",
              "      <td>20</td>\n",
              "      <td>2022-03-31-20</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.036</td>\n",
              "      <td>0.016</td>\n",
              "      <td>31.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>739</th>\n",
              "      <td>2022-03-31</td>\n",
              "      <td>21</td>\n",
              "      <td>2022-03-31-21</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.032</td>\n",
              "      <td>0.019</td>\n",
              "      <td>31.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>2022-03-31</td>\n",
              "      <td>22</td>\n",
              "      <td>2022-03-31-22</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.026</td>\n",
              "      <td>28.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>2022-03-31</td>\n",
              "      <td>23</td>\n",
              "      <td>2022-03-31-23</td>\n",
              "      <td>0.004</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.036</td>\n",
              "      <td>0.032</td>\n",
              "      <td>27.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2157 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0524827-b4df-413d-91d1-553d6428954e')\"\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-d0524827-b4df-413d-91d1-553d6428954e 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-d0524827-b4df-413d-91d1-553d6428954e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 356
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 기상 관측치 전처리"
      ],
      "metadata": {
        "id": "YHnePUi7BRUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#2020\n",
        "weather=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/weather_2020.csv',encoding='CP949')\n",
        "weather['시간']=pd.to_numeric(weather['일시'].str[11:13])\n",
        "weather['날짜']=weather['일시'].str[:10]\n",
        "weather.columns=['지점','지점명','일시','기온','강수량','풍속','습도','현지기압','전운량','시정','시간','날짜']\n",
        "weather=weather[['날짜','시간','기온','강수량','풍속','습도','현지기압','전운량','시정']]\n",
        "\n",
        "#강수량 결측치 0으로 대체\n",
        "weather['강수량']=weather['강수량'].fillna(0)\n",
        "\n",
        "#강수량 제외한 값 보간\n",
        "imputer1=KNNImputer(n_neighbors=2)\n",
        "imputing1=weather[['기온','습도','현지기압','전운량','시정']]\n",
        "imputed1=pd.DataFrame(imputer1.fit_transform(imputing1))\n",
        "imputed1.columns=[['기온','습도','기압','전운량','시정']]\n",
        "weather['기온']=imputed1['기온']\n",
        "weather['습도']=imputed1['습도']\n",
        "weather['기압']=imputed1['기압']\n",
        "weather['전운량']=imputed1['전운량']\n",
        "weather['시정']=imputed1['시정']\n",
        "weather['D-H']=weather['날짜']+'-'+weather['시간'].astype(str)\n",
        "weather_2020=weather[['날짜','시간','D-H','기온','강수량','풍속','습도','현지기압','전운량','시정']]"
      ],
      "metadata": {
        "id": "G60rN5RuBOVp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2021\n",
        "weather=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/weather_2021.csv',encoding='CP949')\n",
        "weather['시간']=pd.to_numeric(weather['일시'].str[11:13])\n",
        "weather['날짜']=weather['일시'].str[:10]\n",
        "weather.columns=['지점','지점명','일시','기온','강수량','풍속','습도','현지기압','전운량','시정','시간','날짜']\n",
        "weather=weather[['날짜','시간','기온','강수량','풍속','습도','현지기압','전운량','시정']]\n",
        "\n",
        "#강수량 결측치 0으로 대체\n",
        "weather['강수량']=weather['강수량'].fillna(0)\n",
        "\n",
        "#강수량 제외한 값 보간\n",
        "imputer1=KNNImputer(n_neighbors=2)\n",
        "imputing1=weather[['기온','습도','현지기압','전운량','시정']]\n",
        "imputed1=pd.DataFrame(imputer1.fit_transform(imputing1))\n",
        "imputed1.columns=[['기온','습도','기압','전운량','시정']]\n",
        "weather['기온']=imputed1['기온']\n",
        "weather['습도']=imputed1['습도']\n",
        "weather['기압']=imputed1['기압']\n",
        "weather['전운량']=imputed1['전운량']\n",
        "weather['시정']=imputed1['시정']\n",
        "weather['D-H']=weather['날짜']+'-'+weather['시간'].astype(str)\n",
        "weather_2021=weather[['날짜','시간','D-H','기온','강수량','풍속','습도','현지기압','전운량','시정']]"
      ],
      "metadata": {
        "id": "xvqXgCn3jqwx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2022\n",
        "weather=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/weather_2022.csv',encoding='CP949')\n",
        "weather['시간']=pd.to_numeric(weather['일시'].str[11:13])\n",
        "weather['날짜']=weather['일시'].str[:10]\n",
        "weather.columns=['지점','지점명','일시','기온','강수량','풍속','습도','현지기압','전운량','시정','시간','날짜']\n",
        "weather=weather[['날짜','시간','기온','강수량','풍속','습도','현지기압','전운량','시정']]\n",
        "\n",
        "#강수량 결측치 0으로 대체\n",
        "weather['강수량']=weather['강수량'].fillna(0)\n",
        "\n",
        "#강수량 제외한 값 보간\n",
        "imputer1=KNNImputer(n_neighbors=2)\n",
        "imputing1=weather[['기온','습도','현지기압','전운량','시정']]\n",
        "imputed1=pd.DataFrame(imputer1.fit_transform(imputing1))\n",
        "imputed1.columns=[['기온','습도','기압','전운량','시정']]\n",
        "weather['기온']=imputed1['기온']\n",
        "weather['습도']=imputed1['습도']\n",
        "weather['기압']=imputed1['기압']\n",
        "weather['전운량']=imputed1['전운량']\n",
        "weather['시정']=imputed1['시정']\n",
        "weather['D-H']=weather['날짜']+'-'+weather['시간'].astype(str)\n",
        "weather_2022=weather[['날짜','시간','D-H','기온','강수량','풍속','습도','현지기압','전운량','시정']]"
      ],
      "metadata": {
        "id": "QI1E4bx8juw0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#데이터 병합\n",
        "최종 데이터셋 생성"
      ],
      "metadata": {
        "id": "J6Z6s0e_j2fH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_2020=weather_2020.merge(rentdust_2020,how='left',on='D-H')\n",
        "data_2021=weather_2021.merge(rentdust_2021,how='left',on='D-H')\n",
        "data_2022=weather_2022.merge(rentdust_2022,how='left',on='D-H')"
      ],
      "metadata": {
        "id": "brDOkUMFkRXq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#중간저장\n",
        "data_2020.to_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/final_data/2020_ip1.csv')\n",
        "data_2021.to_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/final_data/2021_ip1.csv')\n",
        "data_2022.to_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/final_data/2022_ip1.csv')"
      ],
      "metadata": {
        "id": "FssVXLS8k5Eo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#다시 로드 후 병합\n",
        "data_2020=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/final_data/2020_ip1.csv',index_col=0)\n",
        "data_2021=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/final_data/2021_ip1.csv',index_col=0)\n",
        "data_2022=pd.read_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/final_data/2022_ip1.csv',index_col=0)\n",
        "all_data=data_2020.append(data_2021).append(data_2022) #병합"
      ],
      "metadata": {
        "id": "xhbBiflDXzmC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_imputed=all_data.assign(\n",
        "                           wind_a=all_data['풍속'].interpolate(method='slinear'),\n",
        "                           baro_a=all_data['현지기압'].interpolate(method='slinear'),\n",
        "                           SO2_a=all_data['SO2'].interpolate(method='slinear'),\n",
        "                           CO_a=all_data['CO'].interpolate(method='slinear'),\n",
        "                           O3_a=all_data['O3'].interpolate(method='slinear'),\n",
        "                           NO2_a=all_data['NO2'].interpolate(method='slinear'),\n",
        "                           PM10_a=all_data['PM10'].interpolate(method='slinear'),\n",
        "                           PM25_a=all_data['PM25'].interpolate(method='slinear'),\n",
        "                           count_a=all_data['count'].interpolate(method='slinear')) #선형보간"
      ],
      "metadata": {
        "id": "stlZo6brf-yB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#정리하고 저장\n",
        "all_data=all_imputed[['날짜','시간','D-H','기온','강수량','wind_a','습도','baro_a','전운량','시정','SO2_a','CO_a','O3_a','NO2_a','PM10_a','PM25_a','count_a']]\n",
        "all_data.columns=['날짜','시간','D-H','기온','강수량','풍속','습도','\b기압','전운량','시정','SO2','CO','O3','NO2','PM10','PM25','count']\n",
        "all_data.to_csv('/content/drive/MyDrive/종로 따릉이 대여예측/dataset/final_data/combined_data_with_imputation.csv')"
      ],
      "metadata": {
        "id": "bOfA05M9YAmj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}