Pandas column - converting string into specific date format

Question

One of the columns in my pandas dataframe looks like this:

14.3.2019
15.3.2019
16.3.2019

So this is European/German date that I have to convert to USA format:

2019-3-14
2019-3-15
2019-3-16

What is the fastest way to do it, possibly inplace, if I have a large dataset?

pd.to_datetime(df['date'], format='%d.%m.%Y').strftime('%Y-%m-%d')? — Quang Hoang
– Quang Hoang, Commented Nov 15, 2019 at 15:58
@QuangHoang : AttributeError: 'Series' object has no attribute 'strftime' — Hrvoje
– Hrvoje, Commented Nov 15, 2019 at 16:01
add .dt: pd.to_datetime(...).dt.strftime? But, as suggested by @anky_91, don't use string, just use datetime format, i.e., use pd.to_datetime only. — Quang Hoang
– Quang Hoang, Commented Nov 15, 2019 at 16:02
@Harvey much faster than what? The difference between my suggestion and anky's is that I pass the format so pandas doesn't need to guess. If you have consistent format, then that's the way to go. — Quang Hoang
– Quang Hoang, Commented Nov 15, 2019 at 16:10

Hrvoje · Accepted Answer · 2019-11-17 01:34:11Z

2

Correct answer given by both commenters, posting here faster solution from @QuangHoang.

Casting string column in date type in desired format:

df['date'] = pd.to_datetime(df['date'], format='%d.%m.%Y').dt.strftime('%Y-%m-%d')

answered Nov 16, 2019 at 20:49

Hrvoje

15.4k11 gold badges104 silver badges121 bronze badges

Sign up to request clarification or add additional context in comments.

1 Answer 1