本文主要是介绍Python中实现SQL中窗口函数lag,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
Python的 shift
代码示例
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import os.path
import pandas as pd
from datetime import datetime
if __name__ == "__main__":
top_file_dir = r"C:\Users\filter"
according_dir = [
datetime.strptime(file_name.split("_")[-1], '%Y%m%d%H%M') for file_name in os.listdir(top_file_dir)
if os.path.isdir(os.path.join(top_file_dir, file_name)) and file_name.startswith("test")]
calculate_dir = sorted(according_dir, reverse=False)
dat_time = sorted(according_dir)
all_daytime_df = pd.DataFrame({'day_time': sorted(dat_time)})
# 窗口函数 .LEAD(col,n,DEFAULT) 用于统计窗口内往下第n行值
# daytime_df['lag_day'] = daytime_df.shift(-1).fillna(0).astype('')
all_daytime_df['lag_day'] = all_daytime_df.shift(-1)
# pandas计算时间差
all_daytime_df['interval_day'] = (all_daytime_df['lag_day'] - all_daytime_df['day_time']).dt.seconds/60
file_start_time = all_daytime_df.loc[:, "day_time"][[0]]
# 数据筛选
file_filter_time = all_daytime_df['lag_day'][all_daytime_df['interval_day'] > 20]
# Series 时间转字符串
res = pd.concat([file_start_time, file_filter_time]).apply(lambda x: "test"+x.strftime("%Y%m%d%H%M")+".json")
out_file = os.path.join(top_file_dir, "J_DAT_json_name.txt")
res.to_csv(out_file, header=False, index=False)
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