Files
Quant_Code/2.数据下载与处理/数据转换最终版/merged_tickdata__BIT_20240522.py
T
Win_home f925dff46b Enhance trading workflow with new order flow management
- Added dingdanliu_nb_mflow for improved order processing
- Updated related scripts and configurations to support new functionality
2025-03-15 22:45:08 +08:00

69 lines
2.6 KiB
Python

import pandas as pd
import os
from datetime import time as s_time
from datetime import datetime
import chardet
import numpy as np
def split_alpha_numeric(string):
alpha_chars = ""
numeric_chars = ""
for char in string:
if char.isalpha():
alpha_chars += char
elif char.isdigit():
numeric_chars += char
return alpha_chars, numeric_chars
def merged_old_unprocessed_tickdata(all_csv_files, sp_char):
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
print("csv_files:", csv_files)
merged_up_df = pd.DataFrame()
dir = os.getcwd()
fileNum_errors = 0
# 循环遍历每个csv文件
for file in csv_files:
try:
df = pd.read_csv(file,
header=0,
encoding='gbk',
low_memory= False,
# skiprows=0,
# parse_dates=['时间'] # 注意此处增加的排序,为了后面按时间排序
)
except:
file_path = os.path.join(dir, file)
fileNum_errors += 1
with open(file_path, 'rb') as file:
data = file.read()
# 使用chardet检测编码
detected_encoding = chardet.detect(data)['encoding']
# print("%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(file,detected_encoding,fileNum_errors))
print("%s:%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(datetime.now().strftime('%Y-%m-%d %H:%M:%S'),file_path,detected_encoding,fileNum_errors))
with open('output_error.txt', 'a') as f:
print("%s:%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(datetime.now().strftime('%Y-%m-%d %H:%M:%S'),file_path,detected_encoding,fileNum_errors), file = f)
# 删除重复行
df.drop_duplicates(inplace=True)
# 将数据合并到新的DataFrame中
merged_up_df = pd.concat([merged_up_df, df], ignore_index=True)
# 删除重复列
merged_up_df.drop_duplicates(subset=merged_up_df.columns.tolist(), inplace=True)
# 重置行索引
merged_up_df.reset_index(inplace=True, drop=True)
# merged_up_df,alpha_chars,code_value = insert_main_contract(merged_up_df)
# 打印提示信息
# print("按年份未处理的CSV文件合并成功!")
return merged_up_df #,alpha_chars,code_value