movie_data_cro_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "088c1df7-f18f-411c-b7d4-10fe46b62e7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No date\n",
      "0 플레인 removed\n",
      "KOBIS_일자별_통계정보_2024-05-31.xls 파일이 삭제되었습니다.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "from selenium.webdriver.common.alert import Alert\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "# 파일 경로 설정\n",
    "file_path = 'KOBIS_일자별_통계정보_2024-05-31.xls'\n",
    "\n",
    "df = pd.read_csv('chunk_6_ex.csv')\n",
    "# 컬럼 추가 및 초기화\n",
    "df['fd_rank'] = None\n",
    "df['w1_rank'] = None\n",
    "df['w2_rank'] = None\n",
    "df['w1_au'] = None\n",
    "df['w2_au'] = None\n",
    "df['w3_au'] = None\n",
    "\n",
    "options = webdriver.ChromeOptions()\n",
    "options.add_argument(\"chrome-win64/chromedriver.exe\")\n",
    "driver = webdriver.Chrome(options=options)\n",
    "\n",
    "\n",
    "\n",
    "def check_date(date, df):\n",
    "    if date in df['날짜'].values:\n",
    "        screen_count = df[df['날짜'] == date]['스크린수'].values[0]\n",
    "        if screen_count <= 9:\n",
    "            print(\"screen value:\", df[df['날짜'] == date]['스크린수'].values[0])\n",
    "            return \"no\"\n",
    "        else:\n",
    "            return \"yes\"\n",
    "    else:\n",
    "        print(\"No date\")\n",
    "        return \"no\"\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    movie_title = row['Movie_Title']\n",
    "    release_date = datetime.strptime(row['Release_Date'], '%Y-%m-%d')\n",
    "    movie_code = row['movieCd']\n",
    "    \n",
    " # 개봉일 전후 1주일 기간 설정\n",
    "    week1_end_date = (release_date + timedelta(days=6)).strftime('%Y-%m-%d')\n",
    "    week2_end_date = (release_date + timedelta(days=13)).strftime('%Y-%m-%d')\n",
    "    week3_end_date = (release_date + timedelta(days=20)).strftime('%Y-%m-%d')\n",
    "    start_date = release_date.strftime('%Y-%m-%d')\n",
    "\n",
    "    # 웹 페이지 열기\n",
    "    driver.get(\"https://www.kobis.or.kr/kobis/business/mast/mvie/searchMovieList.do\")  # 실제 페이지 URL로 변경\n",
    "\n",
    "    # 자바스크립트 함수 실행\n",
    "    driver.execute_script(f\"dtlExcelDn('movie','box','{movie_code}');\")\n",
    "\n",
    "\n",
    "    # 대화 상자 처리 (확인 버튼 클릭)\n",
    "    time.sleep(3)  # 대화 상자가 뜨는 시간을 기다림\n",
    "    alert = Alert(driver)\n",
    "    alert.accept()  # 확인 버튼 클릭\n",
    "\n",
    "    # 잠시 대기\n",
    "    time.sleep(10)\n",
    "\n",
    "    # HTML 파일 읽기\n",
    "    tables = pd.read_html(file_path, encoding='utf-8')\n",
    "\n",
    "    # 첫 번째 테이블을 데이터프레임으로 변환\n",
    "    temp_df = tables[1]\n",
    "\n",
    "    deci= check_date(week3_end_date, temp_df)\n",
    "    if deci == \"no\":\n",
    "        df = df[df['Movie_Title'] != movie_title]\n",
    "        print(index, movie_title,\"removed\")\n",
    "    else:\n",
    "        df.at[index, 'fd_rank'] = temp_df[temp_df['날짜'] == start_date]['순위'].values[0]\n",
    "        df.at[index, 'w1_rank'] = temp_df[temp_df['날짜'] == week1_end_date]['순위'].values[0]\n",
    "        df.at[index, 'w2_rank'] = temp_df[temp_df['날짜'] == week2_end_date]['순위'].values[0]\n",
    "        df.at[index, 'w1_au'] = temp_df[temp_df['날짜'] == week1_end_date]['누적관객수'].values[0]\n",
    "        df.at[index, 'w2_au'] = temp_df[temp_df['날짜'] == week2_end_date]['누적관객수'].values[0]\n",
    "        df.at[index, 'w3_au'] = temp_df[temp_df['날짜'] == week3_end_date]['누적관객수'].values[0]\n",
    "        print(index, movie_title,\"done!\")\n",
    "\n",
    "    if os.path.exists(file_path):\n",
    "        os.remove(file_path)\n",
    "        print(f\"{file_path} 파일이 삭제되었습니다.\")\n",
    "    else:\n",
    "        print(f\"{file_path} 파일이 존재하지 않습니다.\")\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "# Save the DataFrame to an Excel file\n",
    "cel_filename = \"preprosessing_ver1_chunk_6_ex.csv\"\n",
    "df.to_csv(cel_filename, index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6bde94b-7783-4a68-bde8-bfbd845931c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "from selenium.webdriver.common.alert import Alert\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "# 파일 경로 설정\n",
    "file_path = 'KOBIS_일자별_통계정보_2024-06-03.xls'\n",
    "\n",
    "df = pd.read_csv('chunk_6_ex.csv')\n",
    "# 컬럼 추가 및 초기화\n",
    "df['w3_rank'] = None\n",
    "df['w1_av_sales']= None\n",
    "df['w2_av_sales']= None\n",
    "df['w3_av_sales']= None\n",
    "\n",
    "options = webdriver.ChromeOptions()\n",
    "options.add_argument(\"chrome-win64/chromedriver.exe\")\n",
    "driver = webdriver.Chrome(options=options)\n",
    "\n",
    "def check_date(date, df):\n",
    "    if date in df['날짜'].values:\n",
    "        screen_count = df[df['날짜'] == date]['스크린수'].values[0]\n",
    "        if screen_count <= 9:\n",
    "            print(\"screen value:\", df[df['날짜'] == date]['스크린수'].values[0])\n",
    "            return \"no\"\n",
    "        else:\n",
    "            return \"yes\"\n",
    "    else:\n",
    "        print(\"No date\")\n",
    "        return \"no\"\n",
    "\n",
    "def calculate_average_sales(temp_df, start_date, end_date):\n",
    "    date_range = (temp_df['날짜'] >= start_date) & (temp_df['날짜'] <= end_date)\n",
    "    sales = temp_df[date_range]['좌석판매율'].str.replace('%', '').astype(float)\n",
    "    return sales.mean()\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    movie_title = row['Movie_Title']\n",
    "    release_date = datetime.strptime(row['Release_Date'], '%Y-%m-%d')\n",
    "    movie_code = row['movieCd']\n",
    "    \n",
    "    # 개봉일 전후 1주일 기간 설정\n",
    "    week1_end_date = (release_date + timedelta(days=6)).strftime('%Y-%m-%d')\n",
    "    week2_end_date = (release_date + timedelta(days=13)).strftime('%Y-%m-%d')\n",
    "    week3_end_date = (release_date + timedelta(days=20)).strftime('%Y-%m-%d')\n",
    "    start_date = release_date.strftime('%Y-%m-%d')\n",
    "\n",
    "    # 웹 페이지 열기\n",
    "    driver.get(\"https://www.kobis.or.kr/kobis/business/mast/mvie/searchMovieList.do\")  # 실제 페이지 URL로 변경\n",
    "\n",
    "    # 자바스크립트 함수 실행\n",
    "    driver.execute_script(f\"dtlExcelDn('movie','box','{movie_code}');\")\n",
    "\n",
    "    # 대화 상자 처리 (확인 버튼 클릭)\n",
    "    time.sleep(3)  # 대화 상자가 뜨는 시간을 기다림\n",
    "    alert = Alert(driver)\n",
    "    alert.accept()  # 확인 버튼 클릭\n",
    "\n",
    "    # 잠시 대기\n",
    "    time.sleep(10)\n",
    "\n",
    "    # HTML 파일 읽기\n",
    "    tables = pd.read_html(file_path, encoding='utf-8')\n",
    "\n",
    "    # 첫 번째 테이블을 데이터프레임으로 변환\n",
    "    temp_df = tables[1]\n",
    "\n",
    "    deci= check_date(week3_end_date, temp_df)\n",
    "    if deci == \"no\":\n",
    "        df = df[df['Movie_Title'] != movie_title]\n",
    "        print(index, movie_title,\"removed\")\n",
    "    else:\n",
    "        df.at[index, 'w3_rank'] = temp_df[temp_df['날짜'] == week3_end_date]['순위'].values[0]\n",
    "        \n",
    "        # 좌석판매율 평균 계산 및 추가\n",
    "        df.at[index, 'w1_av_sales'] = calculate_average_sales(temp_df, start_date, week1_end_date)\n",
    "        df.at[index, 'w2_av_sales'] = calculate_average_sales(temp_df, (release_date + timedelta(days=7)).strftime('%Y-%m-%d'), week2_end_date)\n",
    "        df.at[index, 'w3_av_sales'] = calculate_average_sales(temp_df, (release_date + timedelta(days=14)).strftime('%Y-%m-%d'), week3_end_date)\n",
    "        \n",
    "        print(index, movie_title,\"done!\")\n",
    "\n",
    "    if os.path.exists(file_path):\n",
    "        os.remove(file_path)\n",
    "        print(f\"{file_path} 파일이 삭제되었습니다.\")\n",
    "    else:\n",
    "        print(f\"{file_path} 파일이 존재하지 않습니다.\")\n",
    "\n",
    "# Save the DataFrame to an Excel file\n",
    "csv_filename = \"preprosessing_ver1_chunk_6_ex.csv\"\n",
    "df.to_csv(csv_filename, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d83bf87f-90a4-4af5-905b-917ca093ac49",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'updated_selected_columns_ver2.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 12\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# CSV 파일 읽기\u001b[39;00m\n\u001b[0;32m     11\u001b[0m file_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupdated_selected_columns_ver2.csv\u001b[39m\u001b[38;5;124m'\u001b[39m  \u001b[38;5;66;03m# 파일 경로를 지정하세요\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;66;03m# 독립 변수와 종속 변수 설정\u001b[39;00m\n\u001b[0;32m     15\u001b[0m X \u001b[38;5;241m=\u001b[39m df[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw1_au\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw1_av_sc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mslope\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:948\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    944\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m    945\u001b[0m )\n\u001b[0;32m    946\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 948\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:611\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    608\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    610\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 611\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    613\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    614\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1448\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1445\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1447\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1705\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1703\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1704\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1705\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1706\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1707\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1708\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1709\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1710\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1711\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1712\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1713\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1714\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1715\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1716\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\common.py:863\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    858\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    859\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    860\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    861\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    862\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 863\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    864\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    865\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    866\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    867\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    868\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    869\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    870\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    871\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    872\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[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'updated_selected_columns_ver2.csv'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "from selenium.webdriver.common.alert import Alert\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "# 파일 경로 설정\n",
    "file_path = 'KOBIS_일자별_통계정보_2024-05-31.xls'\n",
    "\n",
    "df = pd.read_csv('chunk_6_ex.csv')\n",
    "# 컬럼 추가 및 초기화\n",
    "df['w1_av_sc']=None\n",
    "df['w2_av_sc']=None\n",
    "df['w3_av_sc']=None\n",
    "\n",
    "options = webdriver.ChromeOptions()\n",
    "options.add_argument(\"chrome-win64/chromedriver.exe\")\n",
    "driver = webdriver.Chrome(options=options)\n",
    "\n",
    "\n",
    "\n",
    "def check_date(date, df):\n",
    "    if date in df['날짜'].values:\n",
    "        screen_count = df[df['날짜'] == date]['스크린수'].values[0]\n",
    "        if screen_count <= 9:\n",
    "            print(\"screen value:\", df[df['날짜'] == date]['스크린수'].values[0])\n",
    "            return \"no\"\n",
    "        else:\n",
    "            return \"yes\"\n",
    "    else:\n",
    "        print(\"No date\")\n",
    "        return \"no\"\n",
    "def calculate_average_screens(start_date, end_date, df):\n",
    "    mask = (df['날짜'] >= start_date) & (df['날짜'] <= end_date)\n",
    "    return df.loc[mask, '스크린수'].mean()\n",
    "\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    movie_title = row['Movie_Title']\n",
    "    release_date = datetime.strptime(row['Release_Date'], '%Y-%m-%d')\n",
    "    movie_code = row['movieCd']\n",
    "    \n",
    " # 개봉일 전후 1주일 기간 설정\n",
    "    week1_end_date = (release_date + timedelta(days=6)).strftime('%Y-%m-%d')\n",
    "    week2_end_date = (release_date + timedelta(days=13)).strftime('%Y-%m-%d')\n",
    "    week3_end_date = (release_date + timedelta(days=20)).strftime('%Y-%m-%d')\n",
    "    start_date = release_date.strftime('%Y-%m-%d')\n",
    "\n",
    "    week1_end_date_a = (release_date + timedelta(days=7)).strftime('%Y-%m-%d')\n",
    "    week2_end_date_a = (release_date + timedelta(days=14)).strftime('%Y-%m-%d')\n",
    "    week3_end_date_a = (release_date + timedelta(days=20)).strftime('%Y-%m-%d')\n",
    "\n",
    "    # 웹 페이지 열기\n",
    "    driver.get(\"https://www.kobis.or.kr/kobis/business/mast/mvie/searchMovieList.do\")  # 실제 페이지 URL로 변경\n",
    "\n",
    "    # 자바스크립트 함수 실행\n",
    "    driver.execute_script(f\"dtlExcelDn('movie','box','{movie_code}');\")\n",
    "\n",
    "\n",
    "    # 대화 상자 처리 (확인 버튼 클릭)\n",
    "    time.sleep(3)  # 대화 상자가 뜨는 시간을 기다림\n",
    "    alert = Alert(driver)\n",
    "    alert.accept()  # 확인 버튼 클릭\n",
    "\n",
    "    # 잠시 대기\n",
    "    time.sleep(10)\n",
    "\n",
    "    # HTML 파일 읽기\n",
    "    tables = pd.read_html(file_path, encoding='utf-8')\n",
    "\n",
    "    # 첫 번째 테이블을 데이터프레임으로 변환\n",
    "    temp_df = tables[1]\n",
    "\n",
    "    deci= check_date(week3_end_date, temp_df)\n",
    "    if deci == \"no\":\n",
    "        df = df[df['Movie_Title'] != movie_title]\n",
    "        print(index, movie_title,\"removed\")\n",
    "    else:\n",
    "        df.at[index, 'w1_av_sc'] = calculate_average_screens(start_date, week1_end_date, temp_df) #1~7\n",
    "        df.at[index, 'w2_av_sc'] = calculate_average_screens(week1_end_date_a, week2_end_date, temp_df) #8~14\n",
    "        df.at[index, 'w3_av_sc'] = calculate_average_screens(week2_end_date_a, week3_end_date, temp_df) #15~21\n",
    "        print(index, movie_title,\"done!\")\n",
    "\n",
    "    if os.path.exists(file_path):\n",
    "        os.remove(file_path)\n",
    "        print(f\"{file_path} 파일이 삭제되었습니다.\")\n",
    "    else:\n",
    "        print(f\"{file_path} 파일이 존재하지 않습니다.\")\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "# Save the DataFrame to an Excel file\n",
    "cel_filename = \"preprosessing_ver1_chunk_6_ex.csv\"\n",
    "df.to_csv(cel_filename, index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94475abd-1bf3-44fa-a2fc-ec7382528ffd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "행의 개수: 387\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# CSV 파일 경로 설정\n",
    "file_path = '0530_dataset/merged_dataset_0605.csv'\n",
    "\n",
    "# CSV 파일 읽기\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# 행의 개수 출력\n",
    "num_rows = len(df)\n",
    "print(f'행의 개수: {num_rows}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d67ee939-1d1c-4e86-ba61-c936556d1c8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "총 504개의 행을 100개씩 나눠서 CSV 파일로 저장했습니다.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# CSV 파일 경로 설정\n",
    "file_path = '0530_dataset/combined_trend_data_adjusted.csv'\n",
    "\n",
    "# CSV 파일 읽기\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# 총 행 개수\n",
    "total_rows = len(df)\n",
    "\n",
    "# 분할할 행 개수\n",
    "chunk_size = 100\n",
    "\n",
    "# CSV 파일을 나눌 디렉토리 설정\n",
    "output_dir = 'output_chunks'\n",
    "\n",
    "# 디렉토리 생성\n",
    "import os\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "# 분할된 파일 저장\n",
    "for i in range(0, total_rows, chunk_size):\n",
    "    chunk = df.iloc[i:i + chunk_size]\n",
    "    output_file = os.path.join(output_dir, f'chunk_{i // chunk_size + 1}.csv')\n",
    "    chunk.to_csv(output_file, index=False)\n",
    "\n",
    "print(f'총 {total_rows}개의 행을 {chunk_size}개씩 나눠서 CSV 파일로 저장했습니다.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "95f97bde-0e7b-4e77-b6a6-f5eff47e63e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "행의 개수: 50\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# CSV 파일 경로 설정\n",
    "file_path = 'chunk_1.csv'\n",
    "\n",
    "# CSV 파일 읽기\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# 행의 개수 출력\n",
    "num_rows = len(df)\n",
    "print(f'행의 개수: {num_rows}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "768f9de6-9675-4211-8050-a593fb06f057",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 첫 번째 CSV 파일 읽기\n",
    "df1 = pd.read_csv('preprosessing_ver1_chunk_1.csv')\n",
    "\n",
    "# 두 번째 CSV 파일 읽기\n",
    "df2 = pd.read_csv('preprosessing_ver1_chunk_2.csv')\n",
    "\n",
    "# 첫 번째 CSV 파일 읽기\n",
    "df3 = pd.read_csv('preprosessing_ver1_chunk_3.csv')\n",
    "\n",
    "# 두 번째 CSV 파일 읽기\n",
    "df4 = pd.read_csv('preprosessing_ver1_chunk_4.csv')\n",
    "\n",
    "# 데이터프레임을 수직으로 결합하기\n",
    "result_1 = pd.concat([df1, df2], axis=0)\n",
    "\n",
    "result_2 = pd.concat([df3, df4], axis=0)\n",
    "\n",
    "result = pd.concat([result_1, result_2], axis=0)\n",
    "\n",
    "\n",
    "# 결합된 데이터프레임을 새로운 CSV 파일로 저장하기\n",
    "result.to_csv('merged_file.csv', index=False)\n"
   ]
  }
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