pd.Timedelta 在计算日期之间的差异时增加了一天

Question

I have the following pandas data frame df :

Actual                  Scheduled
2017-01-01 04:03:00.000 2017-01-01 04:25:00.000
2017-01-01 04:56:00.000 2017-01-01 04:55:00.000
2017-01-01 04:36:00.000 2017-01-01 05:05:00.000
2017-01-01 06:46:00.000 2017-01-01 06:55:00.000
2017-01-01 06:46:00.000 2017-01-01 07:00:00.000

I need to create an additional column DIFF_MINUTES that contains the difference (in minutes) between Actual and Scheduled ( Actual - Scheduled ).

This is how I tried to solve this task:

import pandas as pd
import datetime

df["Actual"] = df.apply(lambda row: datetime.datetime.strptime(str(row["Actual"]),"%Y-%m-%d %H:%M:%S.%f"), axis=1)
df["Scheduled"] = df.apply(lambda row: datetime.datetime.strptime(str(row["Scheduled"]),"%Y-%m-%d %H:%M:%S.%f"), axis=1)
df["DIFF_MINUTES"] = df.apply(lambda row: (pd.Timedelta(row["Actual"]-row["Scheduled"]).seconds)/60, axis=1)

However, I got wrong results for a negative difference cases (eg 04:03:00-04:25:00 should give 22 minutes instead of 1418 minutes):

Actual                      Scheduled              DIFF_MINUTES
2017-01-01 04:03:00         2017-01-01 04:25:00    1418.0
2017-01-01 04:56:00         2017-01-01 04:55:00    1.0
2017-01-01 04:36:00         2017-01-01 05:05:00    1411.0
2017-01-01 06:46:00         2017-01-01 06:55:00    1431.0
2017-01-01 06:46:00         2017-01-01 07:00:00    1426.0

How to fix it?

Expected result:

Actual                      Scheduled              DIFF_MINUTES
2017-01-01 04:03:00         2017-01-01 04:25:00    -22.0
2017-01-01 04:56:00         2017-01-01 04:55:00    1.0
2017-01-01 04:36:00         2017-01-01 05:05:00    -29
2017-01-01 06:46:00         2017-01-01 06:55:00    -9.0
2017-01-01 06:46:00         2017-01-01 07:00:00    -14.0

Answer 1

Use dt.total_seconds() as (also check whether date is coming first or month in your columns):

df['Actual']  = pd.to_datetime(df['Actual'], dayfirst=True)
df['Scheduled']  = pd.to_datetime(df['Scheduled'], dayfirst=True)
df['DIFF_MINUTES'] = (df['Actual']-df['Scheduled']).dt.total_seconds()/60

print(df)
               Actual           Scheduled  DIFF_MINUTES
0 2017-01-01 04:03:00 2017-01-01 04:25:00         -22.0
1 2017-01-01 04:56:00 2017-01-01 04:55:00           1.0
2 2017-01-01 04:36:00 2017-01-01 05:05:00         -29.0
3 2017-01-01 06:46:00 2017-01-01 06:55:00          -9.0
4 2017-01-01 06:46:00 2017-01-01 07:00:00         -14.0

Answer 2

Assuming that both column are DateTime , run just:

df['DIFF_MINUTES'] = (df.Actual - df.Scheduled).dt.total_seconds() / 60

(a one-liner).

If you read this DataFrame eg from Excel or CSV file, add parse_dates=[0, 1] parameter to have these columns converted into dates, so that there will be no need to cast them by your code.

And if for some reason you have these column as text , then to convert them run:

df.Actual = pd.to_datetime(df.Actual)
df.Scheduled = pd.to_datetime(df.Scheduled)

(another quicker solution than "plain Python" functions).