One can initialize the data for the days using strings, then convert the strings to datetimes. A print can then deliver the objects in the needed format.
I will use an other format (with dots as separators), so that the conversion is clear between the steps.
Sample code first:
import pandas as pd
data = {'day': ['3-20-2019', None, '2-25-2019'] }
df = pd.DataFrame( data )
df['day'] = pd.to_datetime(df['day'])
df['day'] = df['day'].dt.strftime('%d.%m.%Y')
df[ df == 'NaT' ] = ''
Comments on the above.
The first instance of df
is in the ipython interpreter:
In [56]: df['day']
Out[56]:
0 3-20-2019
1 None
2 2-25-2019
Name: day, dtype: object
After the conversion to datetime:
In [58]: df['day']
Out[58]:
0 2019-03-20
1 NaT
2 2019-02-25
Name: day, dtype: datetime64[ns]
so that we have
In [59]: df['day'].dt.strftime('%d.%m.%Y')
Out[59]:
0 20.03.2019
1 NaT
2 25.02.2019
Name: day, dtype: object
That NaT
makes problems. So we replace all its occurrences with the empty string.
In [73]: df[ df=='NaT' ] = ''
In [74]: df
Out[74]:
day
0 20.03.2019
1
2 25.02.2019