在一种格式的熊猫中转换两种日期格式

Question

What would be the easiest way to transform two date formats in pandas dataframe into one? Below is my input

import pandas as pd
df = pd.DataFrame({'DOB': {0: '26/1/16 ', 1: '26/1/2016 '}})

Expected output:

       DOB
0   26/1/2016
1   26/1/2016

I need to maintain the same dateformat.

Followed the comments and used pd.to_datetime

pd.to_datetime(df.DOB)

Output:

0   2016-01-26
1   2016-01-26

Which is fine but I want to retain the original format of %d/%m/%Y

I have tried using the format argument after executing pd.to_datetime

df.DOB=pd.to_datetime(df.DOB,format="%d/%m/%Y")

It seems it does not changes the format. Output:

0   2016-01-26
1   2016-01-26

If I directly execute below to provide the format it gives error which makes sense as input is not of same format

df = pd.DataFrame({'DOB': {0: '26/1/2016 ', 1: '26/1/2016 '}})
df.DOB=pd.to_datetime(df.DOB,format="%d/%m/%Y")

ERROR:

ValueError: unconverted data remains:  

Long story short how can I retain the original date format

Answer 1

You have whitespace in your date strings. Use pd.Series.str.strip before calling pd.to_datetime to guarantee you will avoid errors.

Option 1: don't specify format

Here you don't even need to strip whitespace:

df = pd.DataFrame({'DOB': {0: '26/1/16 ', 1: '26/1/2016 '}})

df.DOB = pd.to_datetime(df.DOB)

print(df)

         DOB
0 2016-01-26
1 2016-01-26

Option 2: strip before specifying format

Supply format only if you know beforehand the specific format of your dates. If you do this, you will need to use pd.Series.str.strip beforehand:

df = pd.DataFrame({'DOB': {0: '26/1/2016 ', 1: '26/1/2016 '}})

df.DOB = pd.to_datetime(df.DOB.str.strip(), format='%d/%m/%Y')

print(df)

         DOB
0 2016-01-26
1 2016-01-26