dealing rsr, mer (light_ver_for_file_extracting).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdb9d8c2",
   "metadata": {},
   "source": [
    "# 23.04.26(수) 20, 30, 40 직선구간에서만 데이터 뽑기 [good] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da92154",
   "metadata": {},
   "source": [
    "## 23.04.25 rsr 파일을 _rsr.csv로 만들고, 각각의 도로별로 시간 average time 구하는 코드\n",
    "경로만 바꾸면 됨\\\n",
    "VehType또한 기준으로 포함시킴. 그러나 925와 926은 HGV로 구분"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc019b47",
   "metadata": {},
   "source": [
    "## 1. 아래 코드는 왜 그런지 모르겠는데, 마지막 폴더에 몰빵으로 다 저장됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b7d56ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20,30 완료\n",
      "30,10 완료\n",
      "30,20 완료\n",
      "30,30 완료\n",
      "모든파일 완료\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import csv\n",
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "# 입력받은 경로를 지정합니다.\n",
    "base_folder = r'D:\\Ramp Data'\n",
    "\n",
    "# 폴더 생성을 위한 구간별 값들을 지정합니다.\n",
    "ranges = ['10', '20', '30']\n",
    "combinations = [f'{x},{y}' for x in ranges for y in ranges]\n",
    "\n",
    "\n",
    "# 모든 폴더에 대해 반복\n",
    "for combination in combinations:\n",
    "    folder_path = os.path.join(base_folder, combination)\n",
    "    # XXX에 해당하는 숫자를 리스트로 만듭니다.\n",
    "    ##### xxx_list = [str(i).zfill(3) for i in range(58, 234)] # 직선구간 \n",
    "    if combination == '10,10':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(234, 410)] # 10,10\n",
    "    elif combination == '10,20':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(410, 586)] # 10,20\n",
    "    elif combination == '10,30':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(586, 762)] # 10,30\n",
    "    elif combination == '20,10':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(762, 938)] # 20,10\n",
    "    elif combination == '20,20':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(938, 1114)] # 20,20 #############\n",
    "    elif combination == '20,30':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(1114, 1290)] # 20,30\n",
    "    elif combination == '30,10':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(1290, 1466)] # 30,10\n",
    "    elif combination == '30,20':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(1466, 1642)] # 30,20\n",
    "    elif combination == '30,30':\n",
    "        xxx_list = [str(i).zfill(3) for i in range(1642, 1818)] # 30,30##############################################\n",
    "\n",
    "\n",
    "\n",
    "    # 각 XXX에 대해 A 값을 구합니다.\n",
    "    a_dict = {}\n",
    "    b_dict = {}\n",
    "    for i, xxx in enumerate(xxx_list):\n",
    "        a_dict[xxx] = str(i//11 + 20)\n",
    "        b_dict[xxx] = str((i%11)*  10)\n",
    "\n",
    "\n",
    "    # 각 Y에 대해 C 값을 구합니다.\n",
    "    c_dict = {'1': '1', '2': '2', '3': '3', '4':'4', '5':'5'}\n",
    "    #c_dict = {'1': '1', '2': '2', '3': '3', '4':'4', '5':'5', '6':'6', '7':'7', '8':'8', '9':'9'}\n",
    "\n",
    "    # rsr_files 폴더 내 모든 파일 경로 가져오기\n",
    "    file_paths = []\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            if file.endswith('.rsr'):\n",
    "                file_paths.append(os.path.join(root, file))\n",
    "\n",
    "\n",
    "    # 각 파일별로 csv 파일 생성\n",
    "    for file_path in file_paths:\n",
    "        # XXX 값을 추출합니다.\n",
    "        xxx = os.path.basename(file_path)[:7]\n",
    "\n",
    "        # A, B, C 값을 추출합니다. \n",
    "        number_check = xxx[3:7]\n",
    "        if number_check[0] == '0':\n",
    "            a_value = a_dict[xxx[4:7]] # 20~35  3자릿수 \n",
    "            b_value = b_dict[xxx[4:7]] # 0~100  3자릿수  \n",
    "        else :\n",
    "            a_value = a_dict[xxx[3:7]] # 20~35   4자릿수 \n",
    "            b_value = b_dict[xxx[3:7]] # 0~100   4자릿수  \n",
    "    #     print(\"a_value, b_value ; \", a_value, '       ', b_value)\n",
    "\n",
    "        c_value = c_dict[os.path.basename(file_path)[10]]\n",
    "        if(int(c_value)>5):\n",
    "            print('c_value : ', c_value)\n",
    "\n",
    "        # 새 파일 이름을 만듭니다.\n",
    "        csv_name = f\"{a_value};{b_value};{c_value}\"\n",
    "        csv_path = os.path.join(root, csv_name + '.csv')\n",
    "\n",
    "\n",
    "        # 파일 열기\n",
    "        with open(file_path, 'r', encoding='utf-8') as f:\n",
    "            # 첫 8줄은 건너뛰기\n",
    "            for i in range(9):\n",
    "                next(f)\n",
    "            # CSV 파일로 저장\n",
    "            with open(csv_path, 'w', newline='', encoding='utf-8') as csvfile:\n",
    "                csvwriter = csv.writer(csvfile, delimiter=',')\n",
    "                # 헤더 정보 추가\n",
    "                csvwriter.writerow(['Time', 'No', 'Veh', 'VehType', 'TravTm', 'Delay', 'Dist'])\n",
    "                # 데이터 추가\n",
    "                data = []\n",
    "                time_by_no = defaultdict(list)\n",
    "                for line in f:\n",
    "                    row = line.strip().split(';')\n",
    "                    data.append(row)\n",
    "                    time = float(row[4])\n",
    "                    no = int(row[1])\n",
    "                    veh_type = int(row[3])\n",
    "                    time_by_no[no, veh_type].append(time)\n",
    "                    csvwriter.writerow(row)\n",
    "\n",
    "\n",
    "        # 별도의 csv 파일 생성\n",
    "        avg_time_path = os.path.join(root, csv_name +'_avg_time.csv')\n",
    "        with open(avg_time_path, 'w', newline='', encoding='utf-8') as csvfile:\n",
    "            csvwriter = csv.writer(csvfile, delimiter=',')\n",
    "            # 헤더 정보 추가\n",
    "            csvwriter.writerow(['No', 'VehType', csv_name])\n",
    "            # 각 기준에 따른 TravTm 평균 구하기\n",
    "    #         no_criteria = [20, 30, 40] ##############################################################\n",
    "\n",
    "            no_criteria = [21,22,23,24,31,32,33,34,41,42,43,44]#############################################################\n",
    "            veh_type_criteria = [100, 925, 926, 'HGV','AVG']\n",
    "\n",
    "            # 각 기준에 따른 TravTm 평균 구하기\n",
    "            for no in no_criteria:\n",
    "                for veh_type in veh_type_criteria:\n",
    "                    times_925 = time_by_no[no, 925]\n",
    "                    times_926 = time_by_no[no, 926]\n",
    "                    times_100 = time_by_no[no, 100]\n",
    "\n",
    "                    if veh_type == 'HGV':\n",
    "                        if len(times_925) > 0 or len(times_926) > 0:\n",
    "                            avg_time = (sum(times_925) + sum(times_926)) / (len(times_925) + len(times_926))\n",
    "                        else:\n",
    "                            avg_time = 0\n",
    "                        csvwriter.writerow([no, veh_type, avg_time])\n",
    "\n",
    "                    elif veh_type==100:\n",
    "                        if len(times_100) > 0:\n",
    "                            avg_time = sum(times_100) / len(times_100)\n",
    "                        else:\n",
    "                            avg_time = 0\n",
    "                        csvwriter.writerow([no, 'LOV', avg_time])\n",
    "\n",
    "                    elif veh_type=='AVG':\n",
    "                        if len(times_925) > 0 or len(times_926) > 0 or len(times_100):\n",
    "                            avg_time = (sum(times_925) + sum(times_926) + sum(times_100)) / (len(times_925) + len(times_926) + len(times_100))\n",
    "                        else:\n",
    "                            avg_time = 0\n",
    "                        csvwriter.writerow([no, 'AVG', avg_time])\n",
    "    print(combination, '완료')\n",
    "print('모든파일 완료')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5ee7f1",
   "metadata": {},
   "source": [
    "## (X) 하위 CSV파일 모두 없애는 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2d4ddf4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "제거 완료\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "# 제거할 파일 확장자를 지정합니다.\n",
    "extension = '.csv'\n",
    "\n",
    "# 제거할 파일이 있는 폴더를 지정합니다.\n",
    "folder_path = r'D:\\0522Results(20,30,40 with CC2,CC4)'\n",
    "\n",
    "# 하위폴더까지 포함하여 모든 csv 파일의 경로를 찾습니다.\n",
    "csv_files = glob.glob(os.path.join(folder_path, '**', f'*{extension}'), recursive=True)\n",
    "\n",
    "# 모든 csv 파일을 제거합니다.\n",
    "for file_path in csv_files:\n",
    "    os.remove(file_path)\n",
    "\n",
    "print('제거 완료')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "918ea69a",
   "metadata": {},
   "source": [
    "## 2. (O) 모든 _avg_time.csv 파일 합치기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "14653b93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directory :  D:\\Ramp Data\\10,10\n",
      "10,10 완료\n",
      "directory :  D:\\Ramp Data\\10,20\n",
      "10,20 완료\n",
      "directory :  D:\\Ramp Data\\10,30\n",
      "10,30 완료\n",
      "directory :  D:\\Ramp Data\\20,10\n",
      "20,10 완료\n",
      "directory :  D:\\Ramp Data\\20,20\n",
      "20,20 완료\n",
      "directory :  D:\\Ramp Data\\20,30\n",
      "20,30 완료\n",
      "directory :  D:\\Ramp Data\\30,10\n",
      "30,10 완료\n",
      "directory :  D:\\Ramp Data\\30,20\n",
      "30,20 완료\n",
      "directory :  D:\\Ramp Data\\30,30\n",
      "30,30 완료\n",
      "완료\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# 입력받은 경로를 지정합니다.\n",
    "base_folder = r'D:\\Ramp Data'\n",
    "\n",
    "# 폴더 생성을 위한 구간별 값들을 지정합니다.\n",
    "ranges = ['10', '20', '30']\n",
    "combinations = [f'{x},{y}' for x in ranges for y in ranges]\n",
    "\n",
    "\n",
    "# 모든 폴더에 대해 반복\n",
    "for combination in combinations:\n",
    "    directory = os.path.join(base_folder, combination)\n",
    "    print(\"directory : \",directory)  # D:\\Ramp Data\\20,30 일걸?\n",
    "    \n",
    "    # 모든 _avg_time.csv 파일을 찾기 위한 경로 설정\n",
    "    search_term = '_avg_time.csv'\n",
    "    all_avg_time_path = os.path.join(directory, 'all_avg_time.csv')\n",
    "\n",
    "    # all_avg_time.csv 파일이 존재하면 삭제\n",
    "    if os.path.exists(all_avg_time_path):\n",
    "        os.remove(all_avg_time_path)\n",
    "\n",
    "    # 모든 _avg_time.csv 파일을 읽어서 데이터프레임으로 변환하여 리스트에 저장\n",
    "    df_list = []\n",
    "    for root, dirs, files in os.walk(directory):\n",
    "        for filename in files:\n",
    "            if filename.endswith(search_term):\n",
    "                filepath = os.path.join(root, filename)\n",
    "                temp_df = pd.read_csv(filepath, index_col=[0,1])\n",
    "                df_list.append(temp_df)\n",
    "\n",
    "    # 리스트에 있는 모든 데이터프레임을 병합하여 all_avg_time.csv 파일로 저장\n",
    "    all_df = pd.concat(df_list, axis=1)\n",
    "    all_df.to_csv(all_avg_time_path)\n",
    "    print(combination, '완료')\n",
    "    \n",
    "print('전체 완료')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42de2bea",
   "metadata": {},
   "source": [
    "### (X) Truck Mixed %를 몇%인지 입력하면 그 %에 해당하는 열만 뽑아서 새로운 Csv로 저장해주는 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "6cf730c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 데이터 파일 경로\n",
    "data_path = r\"C:\\Users\\user\\Desktop\\23.04.15\\0425Results\\all_avg_time.csv\"\n",
    "\n",
    "# B값 입력 입력하는곳!!!!!!!!!!!!!!!\n",
    "B = '10'\n",
    "\n",
    "# 데이터 불러오기\n",
    "df = pd.read_csv(data_path)\n",
    "\n",
    "# B값에 해당하는 column들만 추출하여 새로운 DataFrame 생성\n",
    "filtered_df = df.loc[:, [\"No\", \"VehType\"] + [col for col in df.columns[2:] if B in col]]\n",
    "\n",
    "# 결과를 새로운 csv 파일로 저장\n",
    "filtered_df.to_csv(rf\"C:\\Users\\user\\Desktop\\23.04.15\\0425Results\\{B}_avg_time.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108bf986",
   "metadata": {},
   "source": [
    "### (O) 3. 0~100 까지 Truck 비율을10단위로 증가시키면서 각각 파일 저장하는 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "15c82e10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\Ramp Data\\10,10\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,10\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:07.918174  에  10,10  완료\n",
      "D:\\Ramp Data\\10,20\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,20\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:08.308974  에  10,20  완료\n",
      "D:\\Ramp Data\\10,30\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\10,30\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:08.613953  에  10,30  완료\n",
      "D:\\Ramp Data\\20,10\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,10\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:09.030560  에  20,10  완료\n",
      "D:\\Ramp Data\\20,20\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,20\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:09.338592  에  20,20  완료\n",
      "D:\\Ramp Data\\20,30\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\20,30\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:09.743776  에  20,30  완료\n",
      "D:\\Ramp Data\\30,10\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,10\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:10.177842  에  30,10  완료\n",
      "D:\\Ramp Data\\30,20\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,20\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:10.570528  에  30,20  완료\n",
      "D:\\Ramp Data\\30,30\\all_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;0;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;10;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;20;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;30;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;40;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;50;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;60;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;70;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;80;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;90;_avg_time.csv\n",
      "new_data_path :  D:\\Ramp Data\\30,30\\;100;_avg_time.csv\n",
      "현재 :  2023-06-04 02:41:10.892451  에  30,30  완료\n"
     ]
    }
   ],
   "source": [
    "# 입력받은 경로를 지정합니다.\n",
    "base_folder = r'D:\\Ramp Data'\n",
    "\n",
    "# 폴더 생성을 위한 구간별 값들을 지정합니다.\n",
    "ranges = ['10', '20', '30']\n",
    "combinations = [f'{x},{y}' for x in ranges for y in ranges]\n",
    "\n",
    "\n",
    "# 모든 폴더에 대해 반복\n",
    "for combination in combinations:\n",
    "    directory = os.path.join(base_folder, combination)  # D:\\Ramp Data\\20,30 일걸?\n",
    "    data_path = os.path.join(directory, 'all_avg_time.csv') # r\"D:\\Ramp Data\\20,30\\all_avg_time.csv\" 여야 하는데\n",
    "    print(data_path) #############################################################################\n",
    "\n",
    "\n",
    "    # B값 0~100 까지 10씩 증가시키면서\n",
    "    for B in range(0, 101, 10):\n",
    "\n",
    "        B = str(B)\n",
    "        # 데이터 불러오기\n",
    "        df = pd.read_csv(data_path)\n",
    "\n",
    "        # B값에 해당하는 column들만 추출하여 새로운 DataFrame 생성\n",
    "        filtered_df = pd.DataFrame({\"index\": []})\n",
    "        filtered_df[\"index\"] = df[[\"No\", \"VehType\"]].apply(lambda x: '_'.join(map(str, [x[\"No\"], B + \"%\", x[\"VehType\"]])), axis=1)\n",
    "\n",
    "        for col in df.columns[2:]:\n",
    "            a, b, c = col.split(\";\")\n",
    "            if b == B:\n",
    "                filtered_df[col] = df[col]\n",
    "\n",
    "        # 결과를 새로운 csv 파일로 저장\n",
    "        new_name = ';' + B + ';_avg_time.csv'\n",
    "        new_data_path = os.path.join(directory, new_name) ###########################\n",
    "        print(\"new_data_path : \", new_data_path)\n",
    "        filtered_df.to_csv(new_data_path, index=False)\n",
    "\n",
    "\n",
    "    from datetime import datetime\n",
    "    now = datetime.now()\n",
    "    print(\"현재 : \", now, ' 에 ', combination,  ' 완료')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d253b8ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a9f84ad1",
   "metadata": {},
   "source": [
    "# 23.05.16(화) 세로로 Volume과 time 저장하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "82ff516f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "현재 :  2023-06-04 02:59:17.720860  에 완료\n",
      "현재 :  2023-06-04 02:59:21.681226  에 완료\n",
      "현재 :  2023-06-04 02:59:25.550820  에 완료\n",
      "현재 :  2023-06-04 02:59:29.680759  에 완료\n",
      "현재 :  2023-06-04 02:59:33.437767  에 완료\n",
      "현재 :  2023-06-04 02:59:37.265612  에 완료\n",
      "현재 :  2023-06-04 02:59:41.343703  에 완료\n",
      "현재 :  2023-06-04 02:59:45.294798  에 완료\n",
      "현재 :  2023-06-04 02:59:50.249169  에 완료\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# 입력받은 경로를 지정합니다.\n",
    "base_folder = r'D:\\Ramp Data'\n",
    "\n",
    "# 폴더 생성을 위한 구간별 값들을 지정합니다.\n",
    "ranges = ['10', '20', '30']\n",
    "combinations = [f'{x},{y}' for x in ranges for y in ranges]\n",
    "dash_combinations = [f'{x}-{y}' for x in ranges for y in ranges]\n",
    "\n",
    "# 모든 폴더에 대해 반복\n",
    "for combination, dash_combination in zip(combinations, dash_combinations):\n",
    "    directory = os.path.join(base_folder, combination)\n",
    "    result_folder = os.path.join(directory, 'result')\n",
    "\n",
    "    # result 폴더가 없는 경우 생성\n",
    "    if not os.path.exists(result_folder):\n",
    "        os.makedirs(result_folder)\n",
    "\n",
    "    data_path = os.path.join(directory, 'all_avg_time.csv')\n",
    "\n",
    "    # B값 0~100 까지 10씩 증가시키면서\n",
    "    for B in range(0, 101, 10):\n",
    "        B = str(B)\n",
    "\n",
    "        # 데이터 불러오기\n",
    "        df = pd.read_csv(data_path)\n",
    "\n",
    "        # B값에 해당하는 column들만 추출하여 새로운 DataFrame 생성\n",
    "        filtered_df = pd.DataFrame({\"index\": []})\n",
    "        filtered_df[\"index\"] = df[[\"No\", \"VehType\"]].apply(lambda x: '_'.join(map(str, [x[\"No\"], B + \"%\", x[\"VehType\"]])), axis=1)\n",
    "\n",
    "        # 원하는 truck %만 filtered_df에 포함시킴\n",
    "        for col in df.columns[2:]:\n",
    "            a, b, c = col.split(\";\")\n",
    "            if b == B:\n",
    "                filtered_df[col] = df[col]\n",
    "\n",
    "        # 결과를 새로운 csv 파일로 저장\n",
    "        for index, row in filtered_df.iterrows():\n",
    "            parts = row[\"index\"].split(\"_\")\n",
    "            X = (int)(parts[0])  # 20_100%_HOV 에서 20\n",
    "            Z = parts[2]\n",
    "            if Z == 'AVG':\n",
    "                continue\n",
    "\n",
    "            new_df = filtered_df.loc[[index]].copy() # dataframe 복사\n",
    "\n",
    "            for col in filtered_df.columns[1:]:\n",
    "                a, b, c = col.split(\";\")\n",
    "\n",
    "                # 첫번째 열 값 변경\n",
    "                if X // 10 == 2:\n",
    "                    new_col_name = int(a) % 19 * 400\n",
    "                elif X // 10 == 3:\n",
    "                    new_col_name = int(a) % 19 * 550\n",
    "                elif X // 10 == 4:\n",
    "                    new_col_name = int(a) % 19 * 700\n",
    "\n",
    "                new_df.rename(columns={col: new_col_name}, inplace=True)\n",
    "\n",
    "            new_name = str(X) + '_' + B + '_' + dash_combination + '_' + Z + '.csv'\n",
    "            file_name = os.path.join(result_folder, new_name)\n",
    "\n",
    "            transposed_df = new_df.transpose()\n",
    "            transposed_df.to_csv(file_name, index=True, header=False)\n",
    "\n",
    "    from datetime import datetime\n",
    "    now = datetime.now()\n",
    "    print(\"현재 : \", now, ' 에', combination ,'완료')\n",
    "    \n",
    "print('모두 완료')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab393ddf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47dd976b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f979a018",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2adf7012",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa2101aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4340ba2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ad665a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1eacabae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "778ceee3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78169afa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf1c989",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90b39596",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89cbf4d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a20b0af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa9fa677",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e3840cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96f0f49d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8331878",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f226c05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80b2bd8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53f55316",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a350596b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "323ccfda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ef3fd4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf476695",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12cf8aa6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31d01fad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b1ec228",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aac58f52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42fcf2ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04cff813",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fd10628",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7759f8d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dc6235f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "243dd4b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7058a750",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54d40c05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fcf848b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ce50958",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ba21aaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c1bd695",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dcd8d7f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "90240fe4",
   "metadata": {},
   "source": [
    "# 파일 지우기 세트"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e581299a",
   "metadata": {},
   "source": [
    "## 파일 복사 (하위 모든 폴더 탐색하여) : 에러뜨는거는 이미 옮겨놓고 그 안에 폴더에 또 들어감..;;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2400f69",
   "metadata": {},
   "outputs": [
    {
     "ename": "SameFileError",
     "evalue": "'D:\\\\Ramp Data\\\\HGV\\\\32\\\\10-10\\\\32_0_10-10_HGV.csv' and 'D:\\\\Ramp Data\\\\HGV\\\\32\\\\10-10\\\\32_0_10-10_HGV.csv' are the same file",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mSameFileError\u001b[0m                             Traceback (most recent call last)",
      "Input \u001b[1;32mIn [16]\u001b[0m, in \u001b[0;36m<cell line: 8>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     25\u001b[0m source_file_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(root, file)\n\u001b[0;32m     26\u001b[0m destination_file_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(destination_path, file)\n\u001b[1;32m---> 27\u001b[0m \u001b[43mshutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopyfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource_file_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdestination_file_path\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\shutil.py:244\u001b[0m, in \u001b[0;36mcopyfile\u001b[1;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[0;32m    241\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshutil.copyfile\u001b[39m\u001b[38;5;124m\"\u001b[39m, src, dst)\n\u001b[0;32m    243\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _samefile(src, dst):\n\u001b[1;32m--> 244\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m SameFileError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m are the same file\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(src, dst))\n\u001b[0;32m    246\u001b[0m file_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m    247\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, fn \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m([src, dst]):\n",
      "\u001b[1;31mSameFileError\u001b[0m: 'D:\\\\Ramp Data\\\\HGV\\\\32\\\\10-10\\\\32_0_10-10_HGV.csv' and 'D:\\\\Ramp Data\\\\HGV\\\\32\\\\10-10\\\\32_0_10-10_HGV.csv' are the same file"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "original_path = rf\"D:\\Ramp Data\\HGV\\32\"\n",
    "destination_path = rf\"D:\\Ramp Data\\HGV\\32\\10-10\"  # 목적지 경로를 지정하세요\n",
    "\n",
    "# original_path에서 모든 하위 디렉토리 탐색\n",
    "for root, dirs, files in os.walk(original_path):\n",
    "    for file in files:\n",
    "        if file.endswith(\".csv\"):\n",
    "            # 파일 이름 분리하기\n",
    "            file_name = os.path.splitext(file)[0]\n",
    "            components = file_name.split(\"_\")\n",
    "            \n",
    "            # A, B, C, D, XXX 추출\n",
    "            A = components[0]\n",
    "            B = components[1]\n",
    "            C = components[2].split(\"-\")[0]\n",
    "            D = components[2].split(\"-\")[1]\n",
    "            XXX = components[3]\n",
    "            \n",
    "            \n",
    "            # A가 21인 경우 해당 파일을 목적지로 복사\n",
    "            if A == '32' and XXX == 'HGV' and C == '10' and D == '10':\n",
    "                source_file_path = os.path.join(root, file)\n",
    "                destination_file_path = os.path.join(destination_path, file)\n",
    "                shutil.copyfile(source_file_path, destination_file_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed487f7",
   "metadata": {},
   "source": [
    "## 파일 복사 (original_path에 있는 csv 파일들만)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d6f64532",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "original_path = rf\"D:\\Ramp Data\\32\"\n",
    "destination_path = rf\"D:\\Ramp Data\\32\\10-30\"  # 목적지 경로를 지정하세요\n",
    "\n",
    "# original_path에서 파일 검색\n",
    "for file in os.listdir(original_path):\n",
    "    if file.endswith(\".csv\"):\n",
    "        # 파일 이름 분리하기\n",
    "        file_name = os.path.splitext(file)[0]\n",
    "        components = file_name.split(\"_\")\n",
    "        \n",
    "        # A, B, C, D, XXX 추출\n",
    "        A = components[0]\n",
    "        B = components[1]\n",
    "        C = components[2].split(\"-\")[0]\n",
    "        D = components[2].split(\"-\")[1]\n",
    "        XXX = components[3]\n",
    "        \n",
    "        # A가 21인 경우 해당 파일을 목적지로 복사\n",
    "        if C == '10' and D == '30':\n",
    "            source_file_path = os.path.join(original_path, file)\n",
    "            destination_file_path = os.path.join(destination_path, file)\n",
    "            shutil.copyfile(source_file_path, destination_file_path)\n",
    "print('완료')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b3157a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "00f2ec96",
   "metadata": {},
   "source": [
    "## csv 파일 다 지워버리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "837d9f60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\Ramp Data\\10,10 폴더의 제거 완료\n",
      "D:\\Ramp Data\\10,20 폴더의 제거 완료\n",
      "D:\\Ramp Data\\10,30 폴더의 제거 완료\n",
      "D:\\Ramp Data\\20,10 폴더의 제거 완료\n",
      "D:\\Ramp Data\\20,20 폴더의 제거 완료\n",
      "D:\\Ramp Data\\20,30 폴더의 제거 완료\n",
      "D:\\Ramp Data\\30,10 폴더의 제거 완료\n",
      "D:\\Ramp Data\\30,20 폴더의 제거 완료\n",
      "D:\\Ramp Data\\30,30 폴더의 제거 완료\n",
      "모든 작업 완료\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "# 제거할 파일 확장자를 지정합니다.\n",
    "extension = '.csv'\n",
    "\n",
    "# 입력받은 경로를 지정합니다.\n",
    "base_folder = r'D:\\Ramp Data'\n",
    "\n",
    "# 폴더 생성을 위한 구간별 값들을 지정합니다.\n",
    "ranges = ['10', '20', '30']\n",
    "combinations = [f'{x},{y}' for x in ranges for y in ranges]\n",
    "\n",
    "# 모든 폴더에 대해 반복하여 파일 제거 작업 수행\n",
    "for combination in combinations:\n",
    "    folder_path = os.path.join(base_folder, combination)\n",
    "    os.makedirs(folder_path, exist_ok=True)  # 폴더 생성, 이미 존재하는 경우 무시합니다.\n",
    "\n",
    "    # 하위 폴더까지 포함하여 모든 csv 파일의 경로를 찾습니다.\n",
    "    csv_files = glob.glob(os.path.join(folder_path, '**', f'*{extension}'), recursive=True)\n",
    "\n",
    "    # 모든 csv 파일을 제거합니다.\n",
    "    for file_path in csv_files:\n",
    "        os.remove(file_path)\n",
    "\n",
    "    print(f'{folder_path} 폴더의 제거 완료')\n",
    "\n",
    "print('모든 작업 완료')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d7553b",
   "metadata": {},
   "source": [
    "## 특정 폴더 아래의 모든 폴더, 파일 다 지우는 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87731ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "모든 파일, 폴더를 삭제했습니다. \n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def delete_files_in_folders(folder_path):\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            os.remove(os.path.join(root, file))\n",
    "\n",
    "# 사용 예시\n",
    "folder_path = r'D:\\0427Results(21,22,23,24,31,32,33,34,41,42,43,44)'\n",
    "delete_files_in_folders(folder_path)\n",
    "\n",
    "print('모든 파일, 폴더를 삭제했습니다. ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e0c43d6",
   "metadata": {},
   "source": [
    "## 얘는 걍 폴더 자체를 날려버리는구먼... 폴더 싹다 없애버림.. Scenario 폴더 하위에.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2cd9851",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "모든 파일, 폴더를 삭제했습니다. \n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def delete_folders_and_files(folder_path):\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            os.remove(os.path.join(root, file))\n",
    "        for dir in dirs:\n",
    "            dir_path = os.path.join(root, dir)\n",
    "            delete_folders_and_files(dir_path)\n",
    "            os.rmdir(dir_path)\n",
    "\n",
    "# 사용 예시\n",
    "folder_path = r'C:\\Users\\user\\Desktop\\23.04.15\\Scenarios'\n",
    "delete_folders_and_files(folder_path)\n",
    "\n",
    "print('모든 파일, 폴더를 삭제했습니다. ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b169089",
   "metadata": {},
   "source": [
    "## 특정 폴더 내 CSV 파일을 Xlsx 파일로 바꿔주는 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8854154c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20_0_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_0_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_0_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_0_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_0_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_0_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_0_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_0_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_0_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_100_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_100_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_100_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_100_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_100_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_100_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_100_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_100_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_100_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_10_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_10_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_10_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_10_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_10_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_10_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_10_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_10_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_10_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_20_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_20_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_20_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_20_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_20_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_20_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_20_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_20_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_20_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_30_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_30_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_30_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_30_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_30_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_30_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_30_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_30_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_30_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_40_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_40_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_40_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_40_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_40_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_40_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_40_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_40_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_40_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_50_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_50_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_50_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_50_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_50_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_50_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_50_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_50_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_50_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_60_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_60_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_60_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_60_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_60_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_60_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_60_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_60_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_60_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_70_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_70_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_70_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_70_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_70_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_70_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_70_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_70_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_70_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_80_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_80_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_80_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_80_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_80_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_80_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_80_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_80_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_80_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "20_90_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_90_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_90_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "20_90_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_90_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_90_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "20_90_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_90_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\20_90_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_0_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_0_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_0_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_0_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_0_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_0_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_0_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_0_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_0_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_100_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_100_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_100_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_100_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_100_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_100_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_100_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_100_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_100_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_10_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_10_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_10_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_10_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_10_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_10_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_10_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_10_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_10_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_20_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_20_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_20_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_20_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_20_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_20_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_20_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_20_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_20_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_30_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_30_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_30_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_30_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_30_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_30_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_30_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_30_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_30_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_40_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_40_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_40_0-0_AVG.csv을(를) 삭제했습니다.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30_40_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_40_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_40_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_40_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_40_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_40_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_50_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_50_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_50_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_50_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_50_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_50_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_50_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_50_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_50_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_60_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_60_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_60_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_60_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_60_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_60_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_60_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_60_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_60_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_70_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_70_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_70_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_70_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_70_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_70_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_70_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_70_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_70_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_80_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_80_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_80_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_80_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_80_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_80_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_80_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_80_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_80_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "30_90_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_90_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_90_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "30_90_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_90_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_90_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "30_90_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_90_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\30_90_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_0_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_0_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_0_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_0_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_0_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_0_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_0_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_0_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_0_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_100_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_100_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_100_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_100_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_100_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_100_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_100_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_100_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_100_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_10_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_10_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_10_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_10_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_10_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_10_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_10_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_10_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_10_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_20_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_20_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_20_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_20_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_20_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_20_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_20_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_20_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_20_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_30_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_30_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_30_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_30_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_30_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_30_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_30_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_30_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_30_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_40_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_40_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_40_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_40_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_40_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_40_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_40_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_40_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_40_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_50_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_50_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_50_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_50_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_50_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_50_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_50_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_50_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_50_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_60_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_60_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_60_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_60_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_60_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_60_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_60_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_60_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_60_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_70_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_70_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_70_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_70_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_70_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_70_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_70_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_70_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_70_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_80_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_80_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_80_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_80_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_80_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_80_0-0_HGV.csv을(를) 삭제했습니다.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40_80_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_80_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_80_0-0_LOV.csv을(를) 삭제했습니다.\n",
      "40_90_0-0_AVG.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_90_0-0_AVG.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_90_0-0_AVG.csv을(를) 삭제했습니다.\n",
      "40_90_0-0_HGV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_90_0-0_HGV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_90_0-0_HGV.csv을(를) 삭제했습니다.\n",
      "40_90_0-0_LOV.csv을(를) 변환하여 D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_90_0-0_LOV.xlsx로 저장했습니다.\n",
      "D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting\\40_90_0-0_LOV.csv을(를) 삭제했습니다.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# CSV 파일을 XLSX로 변환하는 함수\n",
    "def convert_csv_to_xlsx(csv_file_path, xlsx_file_path):\n",
    "    data = pd.read_csv(csv_file_path)\n",
    "    data.to_excel(xlsx_file_path, index=False)\n",
    "\n",
    "    # 변환 완료 후 CSV 파일 삭제\n",
    "    os.remove(csv_file_path)\n",
    "\n",
    "# 변환할 폴더 경로\n",
    "folder_path = rf'D:\\0522Results(20,30,40 with CC2,CC4)\\result\\fitting'\n",
    "\n",
    "# 폴더 내의 모든 파일에 대해 변환 및 삭제 수행\n",
    "for filename in os.listdir(folder_path):\n",
    "    if filename.endswith('.csv'):\n",
    "        csv_file_path = os.path.join(folder_path, filename)\n",
    "        xlsx_file_path = os.path.join(folder_path, filename[:-4] + '.xlsx')\n",
    "        convert_csv_to_xlsx(csv_file_path, xlsx_file_path)\n",
    "        print(f'{filename}을(를) 변환하여 {xlsx_file_path}로 저장했습니다.')\n",
    "        print(f'{csv_file_path}을(를) 삭제했습니다.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bbd3cc9",
   "metadata": {},
   "source": [
    "## 특정 폴더 내 rsr 파일 중 이름이 끝자리가 06-10으로 끝나는 애들을 01-05로 바꿔주는 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "927b92a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def rename_rsr_files(folder_path):\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            if file.endswith(\".rsr\"):\n",
    "                original_name = os.path.join(root, file)\n",
    "                file_name, file_ext = os.path.splitext(file)\n",
    "                last_two_chars = file_name[-2:]\n",
    "                new_last_two_chars = \"\"\n",
    "\n",
    "                if last_two_chars == \"06\":\n",
    "                    new_last_two_chars = \"01\"\n",
    "                elif last_two_chars == \"07\":\n",
    "                    new_last_two_chars = \"02\"\n",
    "                elif last_two_chars == \"08\":\n",
    "                    new_last_two_chars = \"03\"\n",
    "                elif last_two_chars == \"09\":\n",
    "                    new_last_two_chars = \"04\"\n",
    "                elif last_two_chars == \"10\":\n",
    "                    new_last_two_chars = \"05\"\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "                new_file_name = file_name[:-2] + new_last_two_chars\n",
    "                new_name = os.path.join(root, new_file_name + file_ext)\n",
    "                \n",
    "                os.rename(original_name, new_name)\n",
    "\n",
    "# 폴더 경로 설정\n",
    "folder_path = rf\"D:\\0522Results(20,30,40 with CC2,CC4)_2nd\\00,00\"\n",
    "\n",
    "# 함수 호출\n",
    "rename_rsr_files(folder_path)\n",
    "\n",
    "\n",
    "print('완료')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25c9b1e",
   "metadata": {},
   "source": [
    "### 1,2,3,4,5만 있는지 check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b75db59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def rename_rsr_files(folder_path):\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            if file.endswith(\".rsr\"):\n",
    "                original_name = os.path.join(root, file)\n",
    "                file_name, file_ext = os.path.splitext(file)\n",
    "                last_two_chars = file_name[-2:]\n",
    "                new_last_two_chars = \"\"\n",
    "\n",
    "                if not(last_two_chars == \"01\" or last_two_chars == \"02\" or last_two_chars == \"03\" or last_two_chars == \"04\" or last_two_chars == \"05\"):\n",
    "                    print(file_name, '이상함')\n",
    "                \n",
    "\n",
    "# 폴더 경로 설정\n",
    "folder_path = rf\"D:\\0522Results(20,30,40 with CC2,CC4)_2nd\\00,00\"\n",
    "\n",
    "# 함수 호출\n",
    "rename_rsr_files(folder_path)\n",
    "\n",
    "\n",
    "print('이상한거 없음')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f48425",
   "metadata": {},
   "source": [
    "## .results 폴더 다 지워주는 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "970f93ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "def delete_results_folders(folder_path):\n",
    "    for root, dirs, files in os.walk(folder_path, topdown=False):\n",
    "        for folder_name in dirs:\n",
    "            folder = os.path.join(root, folder_name)\n",
    "            if folder_name.endswith('.results'):\n",
    "                shutil.rmtree(folder)\n",
    "\n",
    "folder_path = r'C:\\Users\\user\\Desktop\\23.04.25(at 20, 30, 40 and truck ratio 0~100) - 복사본\\Scenarios'\n",
    "delete_results_folders(folder_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043c6de7",
   "metadata": {},
   "source": [
    "## AVG.csv 파일 다 지워주는 코드 (하위 폴더 전부)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d1a8d67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: D:\\Straight Data\\2040\\result\\20_0_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_100_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_10_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_20_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_30_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_40_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_50_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_60_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_70_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_80_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\20_90_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_0_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_100_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_10_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_20_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_30_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_40_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_50_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_60_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_70_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_80_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\30_90_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_0_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_100_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_10_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_20_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_30_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_40_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_50_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_60_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_70_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_80_0-0_AVG.csv\n",
      "Deleted file: D:\\Straight Data\\2040\\result\\40_90_0-0_AVG.csv\n",
      "모두 지우기 완료\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def delete_files_with_suffix(folder_path, suffix):\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            if file.endswith(suffix):\n",
    "                file_path = os.path.join(root, file)\n",
    "                os.remove(file_path)\n",
    "                print(f\"Deleted file: {file_path}\")\n",
    "    print('모두 지우기 완료')\n",
    "\n",
    "# 주어진 폴더 경로와 파일 이름 끝에 붙는 접미사를 지정합니다.\n",
    "folder_path = rf'D:\\Straight Data\\2040\\result'  # 본인의 폴더 경로로 바꿔주세요.\n",
    "suffix = '_AVG.csv'\n",
    "\n",
    "delete_files_with_suffix(folder_path, suffix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c581ddf",
   "metadata": {},
   "source": [
    "## 파일 옮기기 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "98df3224",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10,10 완료\n",
      "10,20 완료\n",
      "10,30 완료\n",
      "20,10 완료\n",
      "20,20 완료\n",
      "20,30 완료\n",
      "30,10 완료\n",
      "30,20 완료\n",
      "30,30 완료\n",
      "완료\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "\n",
    "\n",
    "def create_folders_and_copy_files(source_folder, target_folder):\n",
    "    # 폴더 생성\n",
    "    folders = ['2124', '3134', '4144']\n",
    "    subfolders = ['LOV', 'HGV']\n",
    "\n",
    "    for folder in folders:\n",
    "        folder_path = os.path.join(target_folder, folder)\n",
    "        if not os.path.exists(folder_path):  # 폴더가 이미 존재하는지 확인\n",
    "            os.makedirs(folder_path)\n",
    "            for subfolder in subfolders:\n",
    "                subfolder_path = os.path.join(folder_path, subfolder)\n",
    "                os.makedirs(subfolder_path)\n",
    "\n",
    "    # 파일 복사\n",
    "    files = os.listdir(source_folder)\n",
    "\n",
    "    for file in files:\n",
    "        target_path = None  # target_path 변수 초기화\n",
    "        if file.startswith(('21', '22', '23', '24')):\n",
    "            if file.endswith('_LOV.csv'):\n",
    "                target_path = os.path.join(target_folder, '2124', 'LOV', file)\n",
    "            elif file.endswith('_HGV.csv'):\n",
    "                target_path = os.path.join(target_folder, '2124', 'HGV', file)\n",
    "\n",
    "        elif file.startswith(('31', '32', '33', '34')):\n",
    "            if file.endswith('_LOV.csv'):\n",
    "                target_path = os.path.join(target_folder, '3134', 'LOV', file)\n",
    "            elif file.endswith('_HGV.csv'):\n",
    "                target_path = os.path.join(target_folder, '3134', 'HGV', file)\n",
    "\n",
    "        elif file.startswith(('41', '42', '43', '44')):\n",
    "            if file.endswith('_LOV.csv'):\n",
    "                target_path = os.path.join(target_folder, '4144', 'LOV', file)\n",
    "            elif file.endswith('_HGV.csv'):\n",
    "                target_path = os.path.join(target_folder, '4144', 'HGV', file)\n",
    "\n",
    "        if target_path:\n",
    "            shutil.copy2(os.path.join(source_folder, file), target_path)\n",
    "\n",
    "            \n",
    "############################################################################################\n",
    "# 입력받은 경로를 지정합니다.\n",
    "base_folder = r'D:\\Ramp Data'\n",
    "\n",
    "# 폴더 생성을 위한 구간별 값들을 지정합니다.\n",
    "ranges = ['10', '20', '30']\n",
    "combinations = [f'{x},{y}' for x in ranges for y in ranges]\n",
    "\n",
    "\n",
    "# 모든 폴더에 대해 반복\n",
    "for combination in combinations:\n",
    "    folder_path = os.path.join(base_folder, combination)\n",
    "    source_folder = os.path.join(folder_path, 'result')\n",
    "    target_folder = source_folder\n",
    "            \n",
    "\n",
    "    # 폴더 및 파일 복사 함수 호출\n",
    "    create_folders_and_copy_files(source_folder, target_folder)\n",
    "    print(combination, '완료')\n",
    "\n",
    "print('완료')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3275b91",
   "metadata": {},
   "source": [
    "## 이상하게 10-30 애들이 20,10 / 20,20/ 20,30/ 30,10/30,20/30,30에 들어가있다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c251c060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\Ramp Data\\30,30\\result\\ALL\\21_0_10-30_HGV.csv\n",
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      "D:\\Ramp Data\\30,30\\result\\ALL\\43_80_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\43_80_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\43_90_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\43_90_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_0_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_0_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_100_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_100_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_10_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_10_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_20_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_20_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_30_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_30_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_40_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_40_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_50_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_50_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_60_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_60_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_70_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_70_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_80_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_80_10-30_LOV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_90_10-30_HGV.csv\n",
      "D:\\Ramp Data\\30,30\\result\\ALL\\44_90_10-30_LOV.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "path = r\"D:\\Ramp Data\\30,30\\result\\ALL\"\n",
    "\n",
    "# 경로 내 파일들을 검색하여 조건에 맞는 파일 삭제\n",
    "for filename in os.listdir(path):\n",
    "    if filename.endswith(\".csv\"):\n",
    "        cc_part = filename[-13:-8]  # 파일 이름에서 \"C-C\" 부분을 뒤에서부터 인덱싱\n",
    "        if cc_part == \"10-30\":\n",
    "            file_path = os.path.join(path, filename)\n",
    "            print(file_path)\n",
    "            os.remove(file_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3b0b6608",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-10\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [97]\u001b[0m, in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remainder \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m     19\u001b[0m     \u001b[38;5;28mprint\u001b[39m(C_D)\n\u001b[1;32m---> 20\u001b[0m     filename[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m10\u001b[39m:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m8\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m remainder \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m     22\u001b[0m     filename[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m13\u001b[39m:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m11\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# 파일 경로 설정\n",
    "folder_path = r'D:\\Ramp Data\\10,10\\result\\2124\\LOV - 복사본'\n",
    "\n",
    "# 폴더 내의 모든 파일 확인\n",
    "for root, dirs, files in os.walk(folder_path):\n",
    "    for file in files:\n",
    "        if file.endswith('.csv'):\n",
    "            # 파일 이름 분리\n",
    "            filename, extension = os.path.splitext(file)\n",
    "            \n",
    "            # 파일 이름을 A, B, C, D로 분할\n",
    "            A, B, C_D, XXX = filename.split('_')\n",
    "            \n",
    "            # A를 10으로 나눈 나머지에 따라 D 또는 C를 0으로 변경\n",
    "            remainder = int(A) % 10\n",
    "            if remainder == 1:\n",
    "                print(C_D)\n",
    "                filename[-10:-8] = '0'\n",
    "            elif remainder == 2:\n",
    "                filename[-13:-11] = '0'\n",
    "            else:\n",
    "                continue\n",
    "            \n",
    "            \n",
    "            # 변경된 파일 이름 출력\n",
    "            print(filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8fc2ce79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21_0_10-10_LOV.csv\n",
      "21_100_10-10_LOV.csv\n",
      "21_10_10-10_LOV.csv\n",
      "21_20_10-10_LOV.csv\n",
      "21_30_10-10_LOV.csv\n",
      "21_40_10-10_LOV.csv\n",
      "21_50_10-10_LOV.csv\n",
      "21_60_10-10_LOV.csv\n",
      "21_70_10-10_LOV.csv\n",
      "21_80_10-10_LOV.csv\n",
      "21_90_10-10_LOV.csv\n",
      "22_0_10-10_LOV.csv\n",
      "22_100_10-10_LOV.csv\n",
      "22_10_10-10_LOV.csv\n",
      "22_20_10-10_LOV.csv\n",
      "22_30_10-10_LOV.csv\n",
      "22_40_10-10_LOV.csv\n",
      "22_50_10-10_LOV.csv\n",
      "22_60_10-10_LOV.csv\n",
      "22_70_10-10_LOV.csv\n",
      "22_80_10-10_LOV.csv\n",
      "22_90_10-10_LOV.csv\n",
      "23_0_10-10_LOV.csv\n",
      "23_100_10-10_LOV.csv\n",
      "23_10_10-10_LOV.csv\n",
      "23_20_10-10_LOV.csv\n",
      "23_30_10-10_LOV.csv\n",
      "23_40_10-10_LOV.csv\n",
      "23_50_10-10_LOV.csv\n",
      "23_60_10-10_LOV.csv\n",
      "23_70_10-10_LOV.csv\n",
      "23_80_10-10_LOV.csv\n",
      "23_90_10-10_LOV.csv\n",
      "24_0_10-10_LOV.csv\n",
      "24_100_10-10_LOV.csv\n",
      "24_10_10-10_LOV.csv\n",
      "24_20_10-10_LOV.csv\n",
      "24_30_10-10_LOV.csv\n",
      "24_40_10-10_LOV.csv\n",
      "24_50_10-10_LOV.csv\n",
      "24_60_10-10_LOV.csv\n",
      "24_70_10-10_LOV.csv\n",
      "24_80_10-10_LOV.csv\n",
      "24_90_10-10_LOV.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# 파일 경로 설정\n",
    "folder_path = r'D:\\Ramp Data\\10,10\\result\\2124\\LOV - 복사본'\n",
    "\n",
    "# 폴더 내의 모든 파일 확인\n",
    "for root, dirs, files in os.walk(folder_path):\n",
    "    for file in files:\n",
    "        if file.endswith('.csv'):\n",
    "            # 파일 이름 분리\n",
    "            filename, extension = os.path.splitext(file)\n",
    "            \n",
    "            # 파일 이름을 A, B, C, D로 분할\n",
    "            A, B, C_D, XXX = filename.split('_')\n",
    "            \n",
    "            # A를 10으로 나눈 나머지에 따라 D 또는 C를 0으로 변경\n",
    "            remainder = int(A) % 10\n",
    "            if remainder == 1:\n",
    "                C_D = C_D.replace('D', '0')\n",
    "            elif remainder == 2:\n",
    "                C_D = C_D.replace('C', '0')\n",
    "            \n",
    "            # 변경된 파일 이름 재구성\n",
    "            new_filename = '_'.join([A, B, C_D, XXX]) + extension\n",
    "            print(new_filename)\n",
    "            \n",
    "            # 변경된 파일 경로 생성\n",
    "            new_file_path = os.path.join(root, new_filename)\n",
    "            file_path = os.path.join(root, file)\n",
    "            \n",
    "            # 변경된 파일 이름으로 파일 이동\n",
    "            os.rename(file_path, new_file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d792a177",
   "metadata": {},
   "outputs": [],
   "source": []
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   "id": "d20871ce",
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   "execution_count": null,
   "id": "b9229c42",
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   "execution_count": null,
   "id": "2260b427",
   "metadata": {},
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   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b20eee09",
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