Python 滚动两个数据框并比较一列数据

我有以下数据帧：

      import pandas as pd
      import numpy as np

      df_Sensor = pd.DataFrame({'ID_System_Embed': ['1000', '1000', '1000', '1003', '1004'], 
                      'Date_Time': ['2020-10-18 12:58:05', '2020-10-18 12:58:15',
                                    '2020-10-19 20:10:10', '2018-12-18 12:58:00', 
                                    '2015-10-25 11:00:00']})



     df_Period = pd.DataFrame({'ID_System_Embed': ['1000', '1000', '1001', '1002', '1003', '1004'],
                      'ID_Sensor': ['1', '2', '3', '4', '5', '6'], 
                      'Date_Init': ['2020-10-18 12:58:00', '2020-10-18 19:58:00',
                                    '2019-11-18 19:58:00', '2018-12-29 12:58:00',
                                    '2019-11-20 12:58:00', '2015-10-25 10:00:00'],

                      'Date_End': ['2020-10-18 16:58:00', '2020-10-19 20:58:00',
                                   '2019-11-25 12:58:00', '2018-12-18 12:58:00',
                                   '2019-11-25 12:58:00', '2015-10-25 12:00:00']})

我需要检测数据帧“df_传感器”的日期是否包含在同一ID_System_Embed（嵌入式系统标识符）的第二个数据帧（df_周期）的日期范围内

我尝试实现以下代码：

      df_Period['New_Column'] = 0

     for j in range(0, len(df_Period)):
          for i in range(0, len(df_Sensor)):


              if((df_Sensor['ID_System_Embed'].iloc[i] == df_Period['ID_System_Embed'].iloc[j]) &
                 (df_Sensor['Date_Time'].iloc[i] >= df_Period['Date_Init'].iloc[j]) &
                 (df_Sensor['Date_Time'].iloc[i] <= df_Period['Date_End'].iloc[j])):

                   df_Period['New_Column'].iloc[j] += 1       

---

将测向周期和测向传感器按['ID\u System\u Embed'，'ID\u Sensor']作为唯一键分组
然后使用appnd函数将其他日期列的值聚合为一个列表

def appnd(col):
    return [d for d in col]

df_p = df_Period.copy().groupby(['ID_System_Embed', 'ID_Sensor']).agg(appnd)
df_s = df_Sensor.copy().groupby(['ID_System_Embed']).agg(appnd)

然后连接两个数据帧（可以用0填充NaN）

将此函数应用于将结果映射到新_列的日期列

def inInterval(row):
    ctr = 0
    for d in row[2]:
        for start, end in zip(row[0], row[1]):
            if  start <= d <= end: ctr +=1
    return ctr

df['New_Column'] = df[ ['Date_Init', 'Date_End', 'Date_Time'] ].copy()\
                    .apply(lambda x: inInterval(x)  if type(x[2]) == list else 0, axis = 1)
df

defininterval（行）：
ctr=0
对于第[2]行中的d：
对于开始，以zip结尾（第[0]行，第[1]行）：
如果开始
df = df_p.join(df_s).fillna(value = 0)
df['New_Column'] = 0
df

def inInterval(row):
    ctr = 0
    for d in row[2]:
        for start, end in zip(row[0], row[1]):
            if  start <= d <= end: ctr +=1
    return ctr

df['New_Column'] = df[ ['Date_Init', 'Date_End', 'Date_Time'] ].copy()\
                    .apply(lambda x: inInterval(x)  if type(x[2]) == list else 0, axis = 1)
df