示例csv数据（实际数据有大量类似数据[大约150＆＃000; 000 - 270＆＃39; 000行]和不同的日期和时间，但是样本数据是df_filter2的条件可以满足的条件）
ID,v1,c1,v2,c2,p1,p2,p3,p4,f1,r1,r2,Time_Stamp
8301,418,13.2,34.4,136,4673,1,-1,5524.5,0,49,0,22/6/2017 05:11:00
8301,419.3,2.3,0.7,-0.9,-0.6,1,-1,946.2,0,50,0,22/6/2017 05:11:01
8301,417.7,15.2,30.3,196.5,5962,1,-1,6355,0,49,0,22/6/2017 05:11:02
8301,418.7,2.3,0.7,-0.9,-0.6,1,-1,944.7,0,50,0,22/6/2017 05:11:03
8301,419.3,3.4,53.6,10.8,580.2,1,-1,1432.8,0,49,0,22/6/2017 05:11:04
8301,417.7,13.6,30.1,170.4,5122.7,1,-1,5681.8,0,50,0,22/6/2017 05:11:05
8301,418,11.5,41.2,105,4328.2,1,-1,4796.9,0,49,0,22/6/2017 05:11:07
8301,419.7,2.3,0.8,-0.9,-0.7,1,-1,946.9,0,51,0,22/6/2017 05:11:08
8301,419.7,2.3,40.6,-0.7,-27.9,1,-1,974,0,49,0,22/6/2017 05:11:09
8301,417.4,14.9,30.4,194.4,5903.8,1,-1,6215.4,0,51,0,22/6/2017 05:11:10
8301,417.7,14.7,30.5,186.2,5682.9,1,-1,6139.5,0,49,0,22/6/2017 05:11:11
8301,418,12,31.5,141.5,4456.9,1,-1,5012.5,0,51,0,22/6/2017 05:11:12
8301,419,2.3,0.7,-1.4,-0.9,1,-1,945.4,0,49,0,22/6/2017 05:11:13
8301,419,2.3,0.7,-0.9,-0.6,1,-1,945.4,0,50,0,22/6/2017 05:11:14
8301,419.7,2.3,0.8,-0.9,-0.7,1,-1,946.9,0,50,0,22/6/2017 05:11:15
8301,419,2.3,0.7,-0.9,-0.6,1,-1,945.4,0,49,0,22/6/2017 05:11:16
8301,419,2.3,32.9,-0.2,-5.7,1,-1,972.4,0,51,0,22/6/2017 05:11:17
8301,419.3,2.3,50.3,0.3,17.3,1,-1,973.2,0,49,0,22/6/2017 05:11:18
8301,417.4,15.2,30.5,197.4,6010.5,1,-1,6350,0,50,0,22/6/2017 05:11:19
8301,418.7,2.3,0.9,-0.9,-0.7,1,-1,944.7,0,49,0,22/6/2017 05:11:20
8301,419,2.3,42.9,-0.2,-7.4,1,-1,972.4,0,50,0,22/6/2017 05:11:21
8301,417.4,13.9,30.4,180,5477.6,1,-1,5811.8,0,49,0,22/6/2017 05:11:22
8301,419.7,2.3,0.9,-0.9,-0.8,1,-1,946.9,0,50,0,22/6/2017 05:11:23
8301,418.7,2.3,0.7,-0.9,-0.6,1,-1,944.7,0,50,0,22/6/2017 05:11:24
8301,418.3,2.3,0.6,-0.9,-0.5,1,-1,943.9,0,49,0,22/6/2017 05:11:25
Python代码： 注意：我必须从.csv文件中读取而不是将数据设置为变量中的字符串
import numpy as np
from datetime import date,time,datetime
import pandas as pd
import csv
# ------ Reading from .csv file - initial df with old values ------
df = pd.read_csv('Data.csv')
df["Time_Stamp"] = pd.to_datetime(df["Time_Stamp"]) # convert to Datetime
def getMask(start,end):
mask = (df['Time_Stamp'] > start) & (df['Time_Stamp'] <= end)
return mask;
start = '2017-06-22 05:00:00'
end = '2017-06-22 05:20:00'
timerange = df.loc[getMask(start, end)]
df_filter = timerange[timerange["AC_Input_Current"].le(3.0)] # new df with less or equal to 0.5
where = (df_filter[df_filter["Time_Stamp"].diff().dt.total_seconds() > 1] ["Time_Stamp"] - pd.Timedelta("1s")).astype(str).tolist()
# df_filter2 below
df_filter2 = timerange[timerange["Time_Stamp"].isin(where)] # Create new df with those
#print(df_filter2)
df_filter2["AC_Input_Current"] = 0.0 # Set c1 to 0.0
for index, row in df_filter2.iterrows():
values = row.astype(str).tolist()
print(','.join(values))
以下是df_filter2的输出，c1的值被编辑为0.0。
我想将其与df - 合并在一起，其中所有数据都在，但是将df的行替换为来自df_filter2的行Time_Stamp是一样的。我该怎么做呢？
输出：
8301,418.0,0.0,34.4,136.0,4673.0,1,-1,5524.5,0,49,0,2017-06-22 05:11:00
8301,417.7,0.0,30.3,196.5,5962.0,1,-1,6355.0,0,49,0,2017-06-22 05:11:02
8301,418.0,0.0,41.2,105.0,4328.2,1,-1,4796.9,0,49,0,2017-06-22 05:11:07
8301,418.0,0.0,31.5,141.5,4456.9,1,-1,5012.5,0,51,0,2017-06-22 05:11:12
8301,417.4,0.0,30.5,197.4,6010.5,1,-1,6350.0,0,50,0,2017-06-22 05:11:19
8301,417.4,0.0,30.4,180.0,5477.6,1,-1,5811.8,0,49,0,2017-06-22 05:11:22
编辑 - 通缉结果 :( ＆lt;已取代 - df_filter2行
ID,v1,c1,v2,c2,p1,p2,p3,p4,f1,r1,r2,Time_Stamp
8301,418.0,0.0,34.4,136.0,4673.0,1,-1,5524.5,0,49,0,2017-06-22 05:11:00 < replaced
8301,419.3,2.3,0.7,-0.9,-0.6,1,-1,946.2,0,50,0,22/6/2017 05:11:01
8301,417.7,0.0,30.3,196.5,5962.0,1,-1,6355.0,0,49,0,2017-06-22 05:11:02 < replaced
8301,418.7,2.3,0.7,-0.9,-0.6,1,-1,944.7,0,50,0,22/6/2017 05:11:03
8301,419.3,3.4,53.6,10.8,580.2,1,-1,1432.8,0,49,0,22/6/2017 05:11:04
8301,417.7,13.6,30.1,170.4,5122.7,1,-1,5681.8,0,50,0,22/6/2017 05:11:05
8301,418.0,0.0,41.2,105.0,4328.2,1,-1,4796.9,0,49,0,2017-06-22 05:11:07 < replaced
8301,419.7,2.3,0.8,-0.9,-0.7,1,-1,946.9,0,51,0,22/6/2017 05:11:08
8301,419.7,2.3,40.6,-0.7,-27.9,1,-1,974,0,49,0,22/6/2017 05:11:09
8301,417.4,14.9,30.4,194.4,5903.8,1,-1,6215.4,0,51,0,22/6/2017 05:11:10
8301,417.7,14.7,30.5,186.2,5682.9,1,-1,6139.5,0,49,0,22/6/2017 05:11:11
8301,418.0,0.0,31.5,141.5,4456.9,1,-1,5012.5,0,51,0,2017-06-22 05:11:12 < replaced
8301,419,2.3,0.7,-1.4,-0.9,1,-1,945.4,0,49,0,22/6/2017 05:11:13
8301,419,2.3,0.7,-0.9,-0.6,1,-1,945.4,0,50,0,22/6/2017 05:11:14
8301,419.7,2.3,0.8,-0.9,-0.7,1,-1,946.9,0,50,0,22/6/2017 05:11:15
8301,419,2.3,0.7,-0.9,-0.6,1,-1,945.4,0,49,0,22/6/2017 05:11:16
8301,419,2.3,32.9,-0.2,-5.7,1,-1,972.4,0,51,0,22/6/2017 05:11:17
8301,419.3,2.3,50.3,0.3,17.3,1,-1,973.2,0,49,0,22/6/2017 05:11:18
8301,417.4,0.0,30.5,197.4,6010.5,1,-1,6350.0,0,50,0,2017-06-22 05:11:19 < replaced
8301,418.7,2.3,0.9,-0.9,-0.7,1,-1,944.7,0,49,0,22/6/2017 05:11:20
8301,419,2.3,42.9,-0.2,-7.4,1,-1,972.4,0,50,0,22/6/2017 05:11:21
8301,417.4,0.0,30.4,180.0,5477.6,1,-1,5811.8,0,49,0,2017-06-22 05:11:22 < replaced
8301,419.7,2.3,0.9,-0.9,-0.8,1,-1,946.9,0,50,0,22/6/2017 05:11:23
8301,418.7,2.3,0.7,-0.9,-0.6,1,-1,944.7,0,50,0,22/6/2017 05:11:24
8301,418.3,2.3,0.6,-0.9,-0.5,1,-1,943.9,0,49,0,22/6/2017 05:11:25