如何在python / pandas的毫秒部分转换带逗号（，）的datetime-String？

Question

I got the following problem:

The date-time columns from my data got the following time-format (the columns are "Date" dd.mm.yyyy and "Time" hh:mm:ss.fff,f ):

01.03.2019  12:29:15.732,7

I looked around but I couldn't find a formatting-option which deals with the part behind the comma (after the milliseconds). A source which didn't help me: https://docs.python.org/2/library/datetime.html

I am reading the csv-file in with python3 and pd.read_csv() .

I got the following work-around which truncates the comma and the cipher behind it.

It is terribly slow because of the truncation of over 50000 strings in my dataset:

data = pd.read_csv('xyz.csv', sep=';', low_memory = False, parse_dates = [['Date', 'Time']], 
                   date_parser = lambda x, y : pd.to_datetime((x + ' ' + y)[:23], format='%d.%m.%Y %H:%M:%S.%f'))

What I want is to use a string-formatting which deals with the comma, either by discarding the whole milliseconds part or by converting it correctly to microseconds.

Sidenote: With RI simply used "%d.%m.%Y %H:%M:%S" which discarded the milliseconds without throwing an error.

Answer 1

ResidentSleeper is correct you can use pd.to_datetime() and drop the comma.

import pandas as pd

data1 = {'Date': ['01.03.2019  12:29:15.732,7',
                  '01.03.2019  12:29:15.732,7',
                  '01.03.2019  12:29:15.732,7',
                  '01.03.2019  12:29:15.732,7'], 
        'Value': [1, 2, 3, 4]}

df1 = pd.DataFrame(data1)

df1['Date'] = pd.to_datetime(df1['Date'].str.replace(',', ''))

print(df1)

                        Date  Value
0 2019-01-03 12:29:15.732700      1
1 2019-01-03 12:29:15.732700      2
2 2019-01-03 12:29:15.732700      3
3 2019-01-03 12:29:15.732700      4