CODEWITHSUNDEEP
pythonpandasnumpy
Sophia
Sophia

Reputation: 43

Unsupported type for timedelta

I'm trying to increase dates in the pandas dataframe by values contained in the other column of the same dataframe like this

loans['est_close_date'] = loans['dealdate'] + loans['tenor_weeks'].apply(lambda x: dt.timedelta(weeks =x))

but I keep getting error as:

"unsupported type for timedelta weeks component: numpy.int64" error.

On the other hand, construction like this

loans['est_close_date'] = loans['dealdate'] + loans['tenor_weeks'].apply(lambda x: dt.timedelta(weeks =1)*x)

works just fine. I can't understand what's wrong with the first approach. There is no missing values in tenor_weeks column.

Thank you in advance!

Upvotes: 4

Views: 5239

Answers (1)

jezrael
jezrael

Reputation: 862511

For me you solutions work perfectly, maybe necessary upgrade pandas/python.

I add pure pandas solution with to_timedelta:

rng = pd.date_range('2017-04-03', periods=10)
loans = pd.DataFrame({'dealdate': rng, 'tenor_weeks': range(1,11)})  
print (loans)
    dealdate  tenor_weeks
0 2017-04-03            1
1 2017-04-04            2
2 2017-04-05            3
3 2017-04-06            4
4 2017-04-07            5
5 2017-04-08            6
6 2017-04-09            7
7 2017-04-10            8
8 2017-04-11            9
9 2017-04-12           10

loans['est_close_date'] = loans['dealdate'] + loans['tenor_weeks'].apply(lambda x: dt.timedelta(weeks =x))
loans['est_close_date1'] = loans['dealdate'] + loans['tenor_weeks'].apply(lambda x: dt.timedelta(weeks =1)*x)

loans['est_close_date2'] = loans['dealdate'] + pd.to_timedelta(loans['tenor_weeks'],unit='w')
print (loans)
    dealdate  tenor_weeks est_close_date est_close_date1 est_close_date2
0 2017-04-03            1     2017-04-10      2017-04-10      2017-04-10
1 2017-04-04            2     2017-04-18      2017-04-18      2017-04-18
2 2017-04-05            3     2017-04-26      2017-04-26      2017-04-26
3 2017-04-06            4     2017-05-04      2017-05-04      2017-05-04
4 2017-04-07            5     2017-05-12      2017-05-12      2017-05-12
5 2017-04-08            6     2017-05-20      2017-05-20      2017-05-20
6 2017-04-09            7     2017-05-28      2017-05-28      2017-05-28
7 2017-04-10            8     2017-06-05      2017-06-05      2017-06-05
8 2017-04-11            9     2017-06-13      2017-06-13      2017-06-13
9 2017-04-12           10     2017-06-21      2017-06-21      2017-06-21

Upvotes: 4

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