태풍폭염변수추가.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2aeef84a",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hyeseung\\AppData\\Local\\Temp\\ipykernel_18100\\3739263994.py:3: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  result_data = pd.read_csv('C:/Users/hyeseung/Desktop/4-2/ML/최종데이터/result_data.csv')\n"
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       "      <th></th>\n",
       "      <th>연번</th>\n",
       "      <th>수송일자</th>\n",
       "      <th>호선</th>\n",
       "      <th>고유역번호(외부역코드)</th>\n",
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       "      <th>시군구명</th>\n",
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       "      <td>시청</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>37.563534</td>\n",
       "      <td>126.975271</td>\n",
       "      <td>중구</td>\n",
       "      <td>131390.0</td>\n",
       "      <td>386564.0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>209.0</td>\n",
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       "      <td>2022-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>202.0</td>\n",
       "      <td>을지로입구</td>\n",
       "      <td>승차</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>180.9892</td>\n",
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       "      <td>126.982569</td>\n",
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       "      <td>2</td>\n",
       "      <td>202.0</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>220.0</td>\n",
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       "      <td>25</td>\n",
       "      <td>2022-01-01</td>\n",
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       "      <td>72.0</td>\n",
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       "      <td>107.0</td>\n",
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       "      <td>54.0</td>\n",
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       "      <td>하차</td>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
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      "text/plain": [
       "       연번        수송일자   호선  고유역번호(외부역코드)         역명 승하차구분  06시이전  06-07시간대  \\\n",
       "0      21  2022-01-01    2         201.0         시청    승차   16.0      36.0   \n",
       "1      22  2022-01-01    2         201.0         시청    하차    9.0      88.0   \n",
       "2      23  2022-01-01    2         202.0      을지로입구    승차   41.0      39.0   \n",
       "3      24  2022-01-01    2         202.0      을지로입구    하차   38.0     156.0   \n",
       "4      25  2022-01-01    2         203.0      을지로3가    승차   24.0      36.0   \n",
       "...    ..         ...  ...           ...        ...   ...    ...       ...   \n",
       "60795   0  2023-08-31  2호선         248.0       양천구청    하차   38.0     109.0   \n",
       "60796   0  2023-08-31  2호선         249.0      신정네거리    승차  353.0     789.0   \n",
       "60797   0  2023-08-31  2호선         249.0      신정네거리    하차   44.0     107.0   \n",
       "60798   0  2023-08-31  2호선         250.0  용두(동대문구청)    승차   54.0      84.0   \n",
       "60799   0  2023-08-31  2호선         250.0  용두(동대문구청)    하차    5.0      49.0   \n",
       "\n",
       "       07-08시간대  08-09시간대  ...  cue  weekday  weekdaymean        lat  \\\n",
       "0          55.0     100.0  ...    0        5     180.9892  37.563534   \n",
       "1         196.0     209.0  ...    0        5     180.9892  37.563534   \n",
       "2          79.0     124.0  ...    0        5     180.9892  37.565998   \n",
       "3         220.0     707.0  ...    0        5     180.9892  37.565998   \n",
       "4          72.0      70.0  ...    0        5     180.9892  37.566292   \n",
       "...         ...       ...  ...  ...      ...          ...        ...   \n",
       "60795     245.0     413.0  ...    0        3       1.0000  37.512194   \n",
       "60796    2299.0    1928.0  ...    0        3       1.0000  37.520218   \n",
       "60797     181.0     440.0  ...    0        3       1.0000  37.520218   \n",
       "60798     262.0     434.0  ...    0        3       1.0000        NaN   \n",
       "60799     124.0     236.0  ...    0        3       1.0000        NaN   \n",
       "\n",
       "              lng  시군구명    주민등록인구      종사자수     사업체수   백명당_시설수  \n",
       "0      126.975271    중구  131390.0  386564.0  70308.0  1.959053  \n",
       "1      126.975271    중구  131390.0  386564.0  70308.0  1.959053  \n",
       "2      126.982569    중구  131390.0  386564.0  70308.0  1.959053  \n",
       "3      126.982569    중구  131390.0  386564.0  70308.0  1.959053  \n",
       "4      126.991773    중구  131390.0  386564.0  70308.0  1.959053  \n",
       "...           ...   ...       ...       ...      ...       ...  \n",
       "60795  126.865193   양천구  442345.0  133776.0  37929.0  0.337067  \n",
       "60796  126.852849   양천구  442345.0  133776.0  37929.0  0.337067  \n",
       "60797  126.852849   양천구  442345.0  133776.0  37929.0  0.337067  \n",
       "60798         NaN   NaN       NaN       NaN      NaN       NaN  \n",
       "60799         NaN   NaN       NaN       NaN      NaN       NaN  \n",
       "\n",
       "[60800 rows x 36 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "result_data = pd.read_csv('C:/Users/hyeseung/Desktop/4-2/ML/최종데이터/result_data.csv')\n",
    "result_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6069bec6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>\"2023-08-01\"</td>\n",
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       "      <th>1</th>\n",
       "      <td>\"2023-08-03\"</td>\n",
       "      <td>\"2023-08-04\"</td>\n",
       "      <td>\"2023-08-05\"</td>\n",
       "      <td>\"2023-08-06\"</td>\n",
       "      <td>\"2023-08-07\"</td>\n",
       "      <td>\"2023-08-08\"</td>\n",
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       "      <th>2</th>\n",
       "      <td>\"2023-08-09\"</td>\n",
       "      <td>\"2023-08-10\"</td>\n",
       "      <td>\"2023-08-11\"</td>\n",
       "      <td>\"2022-09-14\"</td>\n",
       "      <td>\"2022-09-15\"</td>\n",
       "      <td>\"2022-09-16\"</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\"2022-09-17\"</td>\n",
       "      <td>\"2022-09-18\"</td>\n",
       "      <td>\"2022-09-19\"</td>\n",
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       "      <td>\"2022-08-29\"</td>\n",
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       "      <th>4</th>\n",
       "      <td>\"2022-08-30\"</td>\n",
       "      <td>\"2022-08-31\"</td>\n",
       "      <td>\"2022-09-01\"</td>\n",
       "      <td>\"2022-09-02\"</td>\n",
       "      <td>\"2022-09-03\"</td>\n",
       "      <td>\"2022-09-04\"</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>\"2022-09-05\"</td>\n",
       "      <td>\"2022-09-06\"</td>\n",
       "      <td>\"2022-07-31\"</td>\n",
       "      <td>\"2022-08-01\"</td>\n",
       "      <td>\"2022-07-28\"</td>\n",
       "      <td>\"2022-07-29\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>\"2022-07-30\"</td>\n",
       "      <td>\"2022-07-01\"</td>\n",
       "      <td>\"2022-07-02\"</td>\n",
       "      <td>\"2022-07-03\"</td>\n",
       "      <td>\"2022-07-04\"</td>\n",
       "      <td>\"2022-07-05\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>\"2021-09-07\"</td>\n",
       "      <td>\"2021-09-08\"</td>\n",
       "      <td>\"2021-09-09\"</td>\n",
       "      <td>\"2021-09-10\"</td>\n",
       "      <td>\"2021-09-11\"</td>\n",
       "      <td>\"2021-09-12\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>\"2021-09-13\"</td>\n",
       "      <td>\"2021-09-14\"</td>\n",
       "      <td>\"2021-09-15\"</td>\n",
       "      <td>\"2021-09-16\"</td>\n",
       "      <td>\"2021-09-17\"</td>\n",
       "      <td>\"2021-09-18\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\"2021-08-20\"</td>\n",
       "      <td>\"2021-08-21\"</td>\n",
       "      <td>\"2021-08-22\"</td>\n",
       "      <td>\"2021-08-23\"</td>\n",
       "      <td>\"2021-08-24\"</td>\n",
       "      <td>\"2021-08-04\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>\"2021-08-05\"</td>\n",
       "      <td>\"2021-08-06\"</td>\n",
       "      <td>\"2021-08-07\"</td>\n",
       "      <td>\"2021-08-08\"</td>\n",
       "      <td>\"2021-08-09\"</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        태풍 영향 일자    Unnamed: 1    Unnamed: 2    Unnamed: 3    Unnamed: 4  \\\n",
       "0   \"2023-07-28\"  \"2023-07-29\"  \"2023-07-30\"  \"2023-07-31\"  \"2023-08-01\"   \n",
       "1   \"2023-08-03\"  \"2023-08-04\"  \"2023-08-05\"  \"2023-08-06\"  \"2023-08-07\"   \n",
       "2   \"2023-08-09\"  \"2023-08-10\"  \"2023-08-11\"  \"2022-09-14\"  \"2022-09-15\"   \n",
       "3   \"2022-09-17\"  \"2022-09-18\"  \"2022-09-19\"  \"2022-09-20\"  \"2022-08-28\"   \n",
       "4   \"2022-08-30\"  \"2022-08-31\"  \"2022-09-01\"  \"2022-09-02\"  \"2022-09-03\"   \n",
       "5   \"2022-09-05\"  \"2022-09-06\"  \"2022-07-31\"  \"2022-08-01\"  \"2022-07-28\"   \n",
       "6   \"2022-07-30\"  \"2022-07-01\"  \"2022-07-02\"  \"2022-07-03\"  \"2022-07-04\"   \n",
       "7   \"2021-09-07\"  \"2021-09-08\"  \"2021-09-09\"  \"2021-09-10\"  \"2021-09-11\"   \n",
       "8   \"2021-09-13\"  \"2021-09-14\"  \"2021-09-15\"  \"2021-09-16\"  \"2021-09-17\"   \n",
       "9   \"2021-08-20\"  \"2021-08-21\"  \"2021-08-22\"  \"2021-08-23\"  \"2021-08-24\"   \n",
       "10  \"2021-08-05\"  \"2021-08-06\"  \"2021-08-07\"  \"2021-08-08\"  \"2021-08-09\"   \n",
       "\n",
       "      Unnamed: 5  \n",
       "0   \"2023-08-02\"  \n",
       "1   \"2023-08-08\"  \n",
       "2   \"2022-09-16\"  \n",
       "3   \"2022-08-29\"  \n",
       "4   \"2022-09-04\"  \n",
       "5   \"2022-07-29\"  \n",
       "6   \"2022-07-05\"  \n",
       "7   \"2021-09-12\"  \n",
       "8   \"2021-09-18\"  \n",
       "9   \"2021-08-04\"  \n",
       "10           NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "태풍영향일자=pd.read_excel('C:/Users/hyeseung/Desktop/4-2/ML/최종데이터/태풍영향일자.xlsx')\n",
    "태풍영향일자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "303fdf2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>수송일자</th>\n",
       "      <th>태풍</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         수송일자  태풍\n",
       "0  2022-01-01   0\n",
       "1  2022-01-01   0\n",
       "2  2022-01-01   0\n",
       "3  2022-01-01   0\n",
       "4  2022-01-01   0\n",
       "5  2022-01-01   0\n",
       "6  2022-01-01   0\n",
       "7  2022-01-01   0\n",
       "8  2022-01-01   0\n",
       "9  2022-01-01   0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 태풍영향일자 데이터를 리스트로 변환\n",
    "태풍_dates = 태풍영향일자.values.flatten()\n",
    "태풍_dates = [str(date).replace('\"', '') for date in 태풍_dates if str(date) != 'nan']\n",
    "\n",
    "# '수송일자'가 태풍영향일자에 포함되는지 확인하여 '태풍' 변수 생성\n",
    "result_data['태풍'] = result_data['수송일자'].apply(lambda x: 1 if x in 태풍_dates else 0)\n",
    "\n",
    "# 결과 확인\n",
    "result_data[['수송일자', '태풍']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "89920237",
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'C:/Users/hyeseung/Desktop/4-2/ML/최종데이터/태풍추가_data.xlsx'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Save the processed data to a new Excel file\u001b[39;00m\n\u001b[0;32m      2\u001b[0m file_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC:/Users/hyeseung/Desktop/4-2/ML/최종데이터/태풍추가_data.xlsx\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 3\u001b[0m result_data\u001b[38;5;241m.\u001b[39mto_excel(file_path, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      5\u001b[0m file_path\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:2374\u001b[0m, in \u001b[0;36mNDFrame.to_excel\u001b[1;34m(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes, storage_options)\u001b[0m\n\u001b[0;32m   2361\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mformats\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexcel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ExcelFormatter\n\u001b[0;32m   2363\u001b[0m formatter \u001b[38;5;241m=\u001b[39m ExcelFormatter(\n\u001b[0;32m   2364\u001b[0m     df,\n\u001b[0;32m   2365\u001b[0m     na_rep\u001b[38;5;241m=\u001b[39mna_rep,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2372\u001b[0m     inf_rep\u001b[38;5;241m=\u001b[39minf_rep,\n\u001b[0;32m   2373\u001b[0m )\n\u001b[1;32m-> 2374\u001b[0m formatter\u001b[38;5;241m.\u001b[39mwrite(\n\u001b[0;32m   2375\u001b[0m     excel_writer,\n\u001b[0;32m   2376\u001b[0m     sheet_name\u001b[38;5;241m=\u001b[39msheet_name,\n\u001b[0;32m   2377\u001b[0m     startrow\u001b[38;5;241m=\u001b[39mstartrow,\n\u001b[0;32m   2378\u001b[0m     startcol\u001b[38;5;241m=\u001b[39mstartcol,\n\u001b[0;32m   2379\u001b[0m     freeze_panes\u001b[38;5;241m=\u001b[39mfreeze_panes,\n\u001b[0;32m   2380\u001b[0m     engine\u001b[38;5;241m=\u001b[39mengine,\n\u001b[0;32m   2381\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[0;32m   2382\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\formats\\excel.py:944\u001b[0m, in \u001b[0;36mExcelFormatter.write\u001b[1;34m(self, writer, sheet_name, startrow, startcol, freeze_panes, engine, storage_options)\u001b[0m\n\u001b[0;32m    940\u001b[0m     need_save \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    941\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    942\u001b[0m     \u001b[38;5;66;03m# error: Cannot instantiate abstract class 'ExcelWriter' with abstract\u001b[39;00m\n\u001b[0;32m    943\u001b[0m     \u001b[38;5;66;03m# attributes 'engine', 'save', 'supported_extensions' and 'write_cells'\u001b[39;00m\n\u001b[1;32m--> 944\u001b[0m     writer \u001b[38;5;241m=\u001b[39m ExcelWriter(  \u001b[38;5;66;03m# type: ignore[abstract]\u001b[39;00m\n\u001b[0;32m    945\u001b[0m         writer, engine\u001b[38;5;241m=\u001b[39mengine, storage_options\u001b[38;5;241m=\u001b[39mstorage_options\n\u001b[0;32m    946\u001b[0m     )\n\u001b[0;32m    947\u001b[0m     need_save \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    949\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_openpyxl.py:60\u001b[0m, in \u001b[0;36mOpenpyxlWriter.__init__\u001b[1;34m(self, path, engine, date_format, datetime_format, mode, storage_options, if_sheet_exists, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mopenpyxl\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mworkbook\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Workbook\n\u001b[0;32m     58\u001b[0m engine_kwargs \u001b[38;5;241m=\u001b[39m combine_kwargs(engine_kwargs, kwargs)\n\u001b[1;32m---> 60\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\n\u001b[0;32m     61\u001b[0m     path,\n\u001b[0;32m     62\u001b[0m     mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[0;32m     63\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[0;32m     64\u001b[0m     if_sheet_exists\u001b[38;5;241m=\u001b[39mif_sheet_exists,\n\u001b[0;32m     65\u001b[0m     engine_kwargs\u001b[38;5;241m=\u001b[39mengine_kwargs,\n\u001b[0;32m     66\u001b[0m )\n\u001b[0;32m     68\u001b[0m \u001b[38;5;66;03m# ExcelWriter replaced \"a\" by \"r+\" to allow us to first read the excel file from\u001b[39;00m\n\u001b[0;32m     69\u001b[0m \u001b[38;5;66;03m# the file and later write to it\u001b[39;00m\n\u001b[0;32m     70\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode:  \u001b[38;5;66;03m# Load from existing workbook\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1313\u001b[0m, in \u001b[0;36mExcelWriter.__init__\u001b[1;34m(self, path, engine, date_format, datetime_format, mode, storage_options, if_sheet_exists, engine_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m   1309\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handles \u001b[38;5;241m=\u001b[39m IOHandles(\n\u001b[0;32m   1310\u001b[0m     cast(IO[\u001b[38;5;28mbytes\u001b[39m], path), compression\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m}\n\u001b[0;32m   1311\u001b[0m )\n\u001b[0;32m   1312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path, ExcelWriter):\n\u001b[1;32m-> 1313\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[0;32m   1314\u001b[0m         path, mode, storage_options\u001b[38;5;241m=\u001b[39mstorage_options, is_text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1315\u001b[0m     )\n\u001b[0;32m   1316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cur_sheet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1318\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m date_format \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:865\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m    857\u001b[0m             handle,\n\u001b[0;32m    858\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    861\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    862\u001b[0m         )\n\u001b[0;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m--> 865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n\u001b[0;32m    866\u001b[0m     handles\u001b[38;5;241m.\u001b[39mappend(handle)\n\u001b[0;32m    868\u001b[0m \u001b[38;5;66;03m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'C:/Users/hyeseung/Desktop/4-2/ML/최종데이터/태풍추가_data.xlsx'"
     ]
    }
   ],
   "source": [
    "# Save the processed data to a new Excel file\n",
    "file_path = \"C:/Users/hyeseung/Desktop/4-2/ML/최종데이터/태풍추가_data.xlsx\"\n",
    "result_data.to_excel(file_path, index=False)\n",
    "\n",
    "file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "92a9fb4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>연도</th>\n",
       "      <th>날짜</th>\n",
       "      <th>지점</th>\n",
       "      <th>관측값</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>702</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>703</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>705</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>706</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>710</td>\n",
       "      <td>서울</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022</td>\n",
       "      <td>726</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022</td>\n",
       "      <td>727</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022</td>\n",
       "      <td>728</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022</td>\n",
       "      <td>729</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022</td>\n",
       "      <td>730</td>\n",
       "      <td>서울</td>\n",
       "      <td>36.1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023</td>\n",
       "      <td>619</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023</td>\n",
       "      <td>625</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2023</td>\n",
       "      <td>703</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2023</td>\n",
       "      <td>721</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023</td>\n",
       "      <td>728</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023</td>\n",
       "      <td>729</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2023</td>\n",
       "      <td>730</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023</td>\n",
       "      <td>731</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2023</td>\n",
       "      <td>801</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2023</td>\n",
       "      <td>802</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2023</td>\n",
       "      <td>803</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2023</td>\n",
       "      <td>804</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2023</td>\n",
       "      <td>805</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023</td>\n",
       "      <td>806</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2023</td>\n",
       "      <td>807</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2023</td>\n",
       "      <td>808</td>\n",
       "      <td>서울</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2023</td>\n",
       "      <td>818</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2023</td>\n",
       "      <td>819</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2023</td>\n",
       "      <td>821</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      연도   날짜  지점   관측값\n",
       "0   2022  702  서울  33.8\n",
       "1   2022  703  서울  34.2\n",
       "2   2022  705  서울  33.1\n",
       "3   2022  706  서울  33.3\n",
       "4   2022  710  서울  35.1\n",
       "5   2022  726  서울  33.1\n",
       "6   2022  727  서울  33.4\n",
       "7   2022  728  서울  34.4\n",
       "8   2022  729  서울  34.3\n",
       "9   2022  730  서울  36.1\n",
       "10  2023  619  서울  34.0\n",
       "11  2023  625  서울  33.1\n",
       "12  2023  703  서울  34.9\n",
       "13  2023  721  서울  34.3\n",
       "14  2023  728  서울  34.1\n",
       "15  2023  729  서울  33.3\n",
       "16  2023  730  서울  34.9\n",
       "17  2023  731  서울  33.4\n",
       "18  2023  801  서울  34.2\n",
       "19  2023  802  서울  33.7\n",
       "20  2023  803  서울  33.8\n",
       "21  2023  804  서울  34.4\n",
       "22  2023  805  서울  34.9\n",
       "23  2023  806  서울  33.0\n",
       "24  2023  807  서울  34.6\n",
       "25  2023  808  서울  35.8\n",
       "26  2023  818  서울  33.3\n",
       "27  2023  819  서울  33.6\n",
       "28  2023  821  서울  33.6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel('C:/Users/hyeseung/Desktop/4-2/ML/subwaydatas/9.폭염.xlsx')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b151fbcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['날짜'] = data['날짜'].astype(str)\n",
    "\n",
    "data['연도'] = data['연도'].astype(str)\n",
    "data['월'] = data['월'].astype(str)\n",
    "data['일'] = data['일'].astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "624efa6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>연도</th>\n",
       "      <th>날짜</th>\n",
       "      <th>지점</th>\n",
       "      <th>관측값</th>\n",
       "      <th>연도날짜</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>702</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.8</td>\n",
       "      <td>2022-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>703</td>\n",
       "      <td>서울</td>\n",
       "      <td>34.2</td>\n",
       "      <td>2022-07-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>705</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.1</td>\n",
       "      <td>2022-07-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>706</td>\n",
       "      <td>서울</td>\n",
       "      <td>33.3</td>\n",
       "      <td>2022-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>710</td>\n",
       "      <td>서울</td>\n",
       "      <td>35.1</td>\n",
       "      <td>2022-07-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도   날짜  지점   관측값        연도날짜\n",
       "0  2022  702  서울  33.8  2022-07-02\n",
       "1  2022  703  서울  34.2  2022-07-03\n",
       "2  2022  705  서울  33.1  2022-07-05\n",
       "3  2022  706  서울  33.3  2022-07-06\n",
       "4  2022  710  서울  35.1  2022-07-10"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# '날짜' 칼럼에서 월과 일 추출\n",
    "data['월'] = data['날짜'].str[:-2].str.pad(2, fillchar='0')\n",
    "data['일'] = data['날짜'].str[-2:]\n",
    "\n",
    "# '연도', '월', '일' 칼럼을 합쳐 'yyyy-mm-dd' 형태로 '연도날짜' 칼럼 생성\n",
    "data['연도날짜'] = data['연도'] + '-' + data['월'] + '-' + data['일']\n",
    "\n",
    "# '월'과 '일' 칼럼은 임시로 사용했으므로 삭제\n",
    "data = data.drop(columns=['월', '일'])\n",
    "\n",
    "# 변경된 데이터의 첫 5행 출력\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91913c48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2022-07-02',\n",
       " '2022-07-03',\n",
       " '2022-07-05',\n",
       " '2022-07-06',\n",
       " '2022-07-10',\n",
       " '2022-07-26',\n",
       " '2022-07-27',\n",
       " '2022-07-28',\n",
       " '2022-07-29',\n",
       " '2022-07-30',\n",
       " '2023-06-19',\n",
       " '2023-06-25',\n",
       " '2023-07-03',\n",
       " '2023-07-21',\n",
       " '2023-07-28',\n",
       " '2023-07-29',\n",
       " '2023-07-30',\n",
       " '2023-07-31',\n",
       " '2023-08-01',\n",
       " '2023-08-02',\n",
       " '2023-08-03',\n",
       " '2023-08-04',\n",
       " '2023-08-05',\n",
       " '2023-08-06',\n",
       " '2023-08-07',\n",
       " '2023-08-08',\n",
       " '2023-08-18',\n",
       " '2023-08-19',\n",
       " '2023-08-21']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 폭염 데이터를 리스트로 변환\n",
    "폭염_dates = data['연도날짜'].tolist()\n",
    "폭염_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9a75ea63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>수송일자</th>\n",
       "      <th>폭염</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         수송일자  폭염\n",
       "0  2022-01-01   0\n",
       "1  2022-01-01   0\n",
       "2  2022-01-01   0\n",
       "3  2022-01-01   0\n",
       "4  2022-01-01   0\n",
       "5  2022-01-01   0\n",
       "6  2022-01-01   0\n",
       "7  2022-01-01   0\n",
       "8  2022-01-01   0\n",
       "9  2022-01-01   0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# '수송일자'가 태풍영향일자에 포함되는지 확인하여 '태풍' 변수 생성\n",
    "result_data['폭염'] = result_data['수송일자'].apply(lambda x: 1 if x in 폭염_dates else 0)\n",
    "\n",
    "# 결과 확인\n",
    "result_data[['수송일자', '폭염']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cfeeb84d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:/Users/hyeseung/Desktop/4-2/ML/최종데이터/태풍폭염추가_data.xlsx'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the processed data to a new Excel file\n",
    "file_path = \"C:/Users/hyeseung/Desktop/4-2/ML/최종데이터/태풍폭염추가_data.xlsx\"\n",
    "result_data.to_excel(file_path, index=False)\n",
    "\n",
    "file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2886ec67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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