pandas.DataFrame.to_csv: selectively apply date_format by column

Question

I have a dataframe with multiple columns

df = pd.DataFrame({'date': ['2011-01-01 10:15:20', '2016-11-17 08:22:10'],
'text': ['red', 'purple'],
'datetime': ['2011-01-01 10:15:20', '2016-11-17 08:22:10']})

I want to export this df to a csv file using pandas' .to_csv(...) and save the column date in the format %Y-%m-%d but the column datetime in the format %Y-%m-%d %H:%M:%S and can't find a way to modify the code accordingly.

I have tried

df.to_csv('output.csv', date_format = '%Y-%m-%d'), which transforms all columns' date format, including the column datetime, where I want to keep the time.
df.to_csv('output.csv') (without using date_format = ...) displays all columns' date format as datetime - yet I want the column date to not carry the time-component.

Does anyone have an idea of how to do this? Thanks a ton in advance.

jezrael · Accepted Answer · 2019-08-28 09:00:39Z

1

I think selective is not possible, docs:

date_format: Format string for datetime objects

Solution is convert column to dates before to_csv:

df['date'] = df['date'].dt.date
print (df.to_csv())

Or:

print (df.assign(date=df['date'].dt.date).to_csv())

,date,text,datetime
0,2011-01-01,red,2011-01-01 10:15:20
1,2016-11-17,purple,2016-11-17 08:22:10

edited Aug 28, 2019 at 9:00

answered Aug 28, 2019 at 8:54

jezrael

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