Change Date Format Of Pandas Column (month-day-year To Day-month-year)

Got the following issue. I have an column in my pandas with some dates and some empty values. Example: 1 - 3-20-2019 2 - 3 - 2-25-2019 etc I want to convert the for

Solution 1:

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.

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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' ] = ''

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Comments on the above. The first instance of df is in the ipython interpreter:

In[56]: df['day']Out[56]: 
03-20-20191None22-25-2019Name: day, dtype: object

After the conversion to datetime:

In[58]: df['day']Out[58]: 
02019-03-201NaT22019-02-25Name: day, dtype: datetime64[ns]

so that we have

In[59]: df['day'].dt.strftime('%d.%m.%Y')
Out[59]: 
020.03.20191NaT225.02.2019Name: 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

Solution 2:

Not sure if this is the fastest way to get it done. Anyway,

df = pd.DataFrame({'Date': {0: '3-20-2019', 1:"", 2:"2-25-2019"}}) #your dataframedf['Date'] = pd.to_datetime(df.Date) #convert to datetime formatdf['Date'] = [d.strftime('%d-%m-%Y') if not pd.isnull(d) else''for d indf['Date']]

Output:

Date020-03-20191225-02-2019