I have a dataframe with dates in the following manner:
'Jan 2019', 'Feb 2019', 'Mär 2019', 'Apr 2019', 'Mai 2019', 'Jun 2019', 'Jul 2019', 'Aug 2019', 'Sep 2019', 'Okt 2019', 'Nov 2019', 'Dez 2019'
I am trying to convert the column to datetime using
pd.to_datetime(df.month, format='%b%Y', errors='ignore')
Unfortunately, to_datetime retuns objects instead of datetimes. I believe it's because of the German spelling of the date (e.g. 'Mär 2019' instead of 'Mar 2019' or 'Dez 2019' instead of 'Dec 2019').
What would be a good general solution to this problem?
I think one possible solution is use Series.replace before converting to datetimes:
a = ['Jan 2019', 'Feb 2019', 'Mär 2019', 'Apr 2019', 'Mai 2019',
'Jun 2019', 'Jul 2019', 'Aug 2019', 'Sep 2019', 'Okt 2019', 'Nov 2019', 'Dez 2019']
df = pd.DataFrame({'month':a})
d = {'Mär':'Mar', 'Mai':'May','Okt':'Oct','Dez':'Dec'}
df['month']=pd.to_datetime(df['month'].replace(d, regex=True), format='%b %Y', errors='coerce')
print (df)
month
0 2019-01-01
1 2019-02-01
2 2019-03-01
3 2019-04-01
4 2019-05-01
5 2019-06-01
6 2019-07-01
7 2019-08-01
8 2019-09-01
9 2019-10-01
10 2019-11-01
11 2019-12-01