How to specify date format when using pandas.to_csv?

The default output format of to_csv() is:

12/14/2012  12:00:00 AM

I cannot figure out how to output only the date part with specific format:


or date and time in two separate columns in the csv file:

20121214,  084530

The documentation is too brief to give me any clue as to how to do these. Can anyone help?

Here is Solutions:

We have many solutions to this problem, But we recommend you to use the first solution because it is tested & true solution that will 100% work for you.

Solution 1

Since version v0.13.0 (January 3, 2014) of Pandas you can use the date_format parameter of the to_csv method:

df.to_csv(filename, date_format='%Y%m%d')

Solution 2

You could use strftime to save these as separate columns:

df['date'] = df['datetime'].apply(lambda x: x.strftime('%d%m%Y'))
df['time'] = df['datetime'].apply(lambda x: x.strftime('%H%M%S'))

and then be specific about which columns to export to csv:

df[['date', 'time', ... ]].to_csv('df.csv')

Solution 3

To export as a timestamp, do this:

df.to_csv(filename, date_format='%s')

The %s format is not documented in python/pandas but works in this case.

I found the %s from the dates formats of ruby. Strftime doc for C here

Note that the timestamp miliseconds format %Q does not work with pandas (you’ll have a litteral %Q in the field instead of the date). I caried my sets with python 3.6 and pandas 0.24.1

Note: Use and implement solution 1 because this method fully tested our system.
Thank you 🙂

All methods was sourced from or, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0

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