Updating the dataframe in python with modified values

Question

I have written a code to do a date modification to reduce 1 day on 2/29/2008. The code successfully executes and shows me the output but it does not update the dataframe.

Name    Maint Start Date
Yasin   02/29/08
Susy    01/04/15
James   04/14/06

I wrote the below code it gave me the output but it did not update the dataframe

spend_sw[spend_sw['Maint Start Date'] == '02/29/2008'].apply(lambda x: pd.datetime(x['Maint Start Date'].year , x['Maint Start Date'].month, x['Maint Start Date'].day - 1), axis=1)

Then I modified it to but all the fields which did not have the leap year dat became blank.

spend_sw['Maint Start Date'] = spend_sw[spend_sw['Maint Start Date'] == '02/29/2008'].apply(lambda x: pd.datetime(x['Maint Start Date'].year , x['Maint Start Date'].month, x['Maint Start Date'].day - 1), axis=1)

Can you please tell me how can I modify the values to the existing dataframe.

jezrael · Accepted Answer · 2016-11-02 14:56:17Z

2

I think you can first convert all values in column Maint Start Date to_datetime, then filter with ix and subtract Timedelta one day created by to_timedelta:

print (spend_sw)
    Name Maint Start Date
0  Yasin         02/29/08
1   Susy         01/04/15
2  James         04/14/06

spend_sw['Maint Start Date'] = pd.to_datetime(spend_sw['Maint Start Date'])

print (spend_sw.ix[spend_sw['Maint Start Date'] == '02/29/08', 'Maint Start Date'])
0   2008-02-29
Name: Maint Start Date, dtype: datetime64[ns]

spend_sw.ix[spend_sw['Maint Start Date'] == '02/29/08', 'Maint Start Date'] = \
spend_sw.ix[spend_sw['Maint Start Date'] == '02/29/08', 'Maint Start Date'] - 
pd.to_timedelta(1, unit='d') 
print (spend_sw)
    Name Maint Start Date
0  Yasin       2008-02-28
1   Susy       2015-01-04
2  James       2006-04-14

Another solution with mask and offset:

spend_sw['Maint Start Date'] = pd.to_datetime(spend_sw['Maint Start Date'])

date = pd.to_datetime('02/29/08')
date1 = date - pd.offsets.Day(1)
#mask by condition
ma = spend_sw['Maint Start Date'] == date
spend_sw['Maint Start Date'] = spend_sw['Maint Start Date'].mask(ma, date1)
print (spend_sw)
    Name Maint Start Date
0  Yasin       2008-02-28
1   Susy       2015-01-04
2  James       2006-04-14

edited Nov 2, 2016 at 14:56

answered Nov 2, 2016 at 14:36

jezrael

868k103 gold badges1.4k silver badges1.3k bronze badges

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

1 Comment

yasin mohammed Over a year ago

Hi...I tried the solution and it worked perfectly fine. Also I had to do some more date manipulations of adding years referring to an Name and it worked exactly how I needed it.

Collectives™ on Stack Overflow

Updating the dataframe in python with modified values

1 Answer 1

1 Comment

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

1 Comment

Your Answer

Sign up or log in

Post as a guest

Related