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- # -*- coding: utf-8 -*-
- """
- Created on Tue Jul 9 16:28:48 2024
- @author: Administrator
- """
- from datetime import datetime
- from os import *
- import chardet
- import pandas as pd
- # 获取文件编码
- def detect_file_encoding(filename):
- # 读取文件的前1000个字节(足够用于大多数编码检测)
- with open(filename, 'rb') as f:
- rawdata = f.read(1000)
- result = chardet.detect(rawdata)
- encoding = result['encoding']
- if encoding is None:
- encoding = 'gb18030'
- if encoding and encoding.lower() == 'gb2312' or encoding.lower().startswith("windows"):
- encoding = 'gb18030'
- return encoding
- # 读取数据到df
- def read_file_to_df(file_path, read_cols=list(), header=0):
- df = pd.DataFrame()
- if str(file_path).lower().endswith("csv") or str(file_path).lower().endswith("gz"):
- encoding = detect_file_encoding(file_path)
- end_with_gz = str(file_path).lower().endswith("gz")
- if read_cols:
- if end_with_gz:
- df = pd.read_csv(file_path, encoding=encoding, usecols=read_cols, compression='gzip', header=header)
- else:
- df = pd.read_csv(file_path, encoding=encoding, usecols=read_cols, header=header, on_bad_lines='warn')
- else:
- if end_with_gz:
- df = pd.read_csv(file_path, encoding=encoding, compression='gzip', header=header)
- else:
- df = pd.read_csv(file_path, encoding=encoding, header=header, on_bad_lines='warn')
- else:
- xls = pd.ExcelFile(file_path)
- # 获取所有的sheet名称
- sheet_names = xls.sheet_names
- for sheet in sheet_names:
- if read_cols:
- df = pd.concat([df, pd.read_excel(xls, sheet_name=sheet, header=header, usecols=read_cols)])
- else:
- df = pd.concat([df, pd.read_excel(xls, sheet_name=sheet, header=header)])
- return df
- def __build_directory_dict(directory_dict, path, filter_types=None):
- # 遍历目录下的所有项
- for item in listdir(path):
- item_path = path.join(path, item)
- if path.isdir(item_path):
- __build_directory_dict(directory_dict, item_path, filter_types=filter_types)
- elif path.isfile(item_path):
- if path not in directory_dict:
- directory_dict[path] = []
- if filter_types is None or len(filter_types) == 0:
- directory_dict[path].append(item_path)
- elif str(item_path).split(".")[-1] in filter_types:
- if str(item_path).count("~$") == 0:
- directory_dict[path].append(item_path)
- # 读取所有文件
- # 读取路径下所有的excel文件
- def read_excel_files(read_path):
- directory_dict = {}
- __build_directory_dict(directory_dict, read_path, filter_types=['xls', 'xlsx', 'csv', 'gz'])
- return [path for paths in directory_dict.values() for path in paths if path]
- # 创建路径
- def create_file_path(path, is_file_path=False):
- if is_file_path:
- path = path.dirname(path)
- if not path.exists(path):
- makedirs(path, exist_ok=True)
- def read_and_save_csv(file_path):
- begin = datetime.now()
- base_name = path.basename(file_path)
- print('开始', base_name)
- df1 = read_file_to_df(file_path + "箱变(1-8号逆变器)数据1.xls")
- del df1['Unnamed: 0']
- df1['时间'] = pd.to_datetime(df1['时间'])
- df1.set_index(keys='时间', inplace=True)
- df2 = read_file_to_df(file_path + "箱变(9-16号逆变器)数据1.xls")
- del df2['Unnamed: 0']
- df2['时间'] = pd.to_datetime(df2['时间'])
- df2.set_index(keys='时间', inplace=True)
- df3 = read_file_to_df(file_path + "箱变(1-8号逆变器)数据2.xls")
- del df3['Unnamed: 0']
- df3['时间'] = pd.to_datetime(df3['时间'])
- df3.set_index(keys='时间', inplace=True)
- df4 = read_file_to_df(file_path + "箱变(9-16号逆变器)数据2.xls")
- del df4['Unnamed: 0']
- df4['时间'] = pd.to_datetime(df4['时间'])
- df4.set_index(keys='时间', inplace=True)
- df = pd.concat([df1, df2, df3, df4], axis=1)
- df.reset_index(inplace=True)
- columns = list(df.columns)
- columns.sort()
- print(df.columns)
- df = df[columns]
- df.sort_values(by='时间', inplace=True)
- df.to_csv(path.join(r'D:\trans_data\大唐玉湖性能分析离线分析', '05整理数据', base_name + '_箱变.csv'), encoding='utf-8',
- index=False)
- print('结束', base_name, '耗时:' + str(datetime.now() - begin))
- if __name__ == '__main__':
- path = r'D:\trans_data\大唐玉湖性能分析离线分析\test'
- all_files = read_excel_files(path)
- all_paths = set()
- for file in all_files:
- base_name = path.basename(file).split("箱变")[0]
- base_path = path.dirname(file)
- if base_name not in all_paths:
- all_paths.add(path.join(base_path, base_name))
- all_datas = list(all_paths)
- all_datas.sort()
- print(all_datas)
- # with Pool(1) as pool:
- # pool.starmap(read_and_save_csv, [(i,) for i in all_datas])
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