Python & Pandas: series to timedelta -

m col in dataframe df indicates number of month.

m 1 0 15 

i trying find number of days between 2015-01-01 , 2015-01-01 + df.m. following col want get.

daynum 31 0 456 

i know how using loop , list:

int((datetime.strptime("2015-01-01", "%y-%m-%d") + relativedelta(months=df.m[i])                      - datetime.strptime("2015-01-01", "%y-%m-%d")).days) 

is there build-in function in pandas can solve problem easily?

you can use same approach in question, using automatic vectorized operations instead of looping.
first convert series of integers relativedelta's:

in [76]: m = pd.series([1, 0, 15])  in [77]: m2 = m.apply(lambda x: dateutil.relativedelta.relativedelta(months=x))  in [78]: m2 out[78]: 0              relativedelta(months=+1) 1                       relativedelta() 2    relativedelta(years=+1, months=+3) dtype: object 

then can same calculation:

in [80]: (pd.timestamp('2015-01-01') + m2) - pd.timestamp('2015-01-01') out[80]: 0    31 days 1     0 days 2   456 days dtype: timedelta64[ns] 

if want have integer values instead of timedelta above, can .dt.days:

in [81]: days = (pd.timestamp('2015-01-01') + m2) - pd.timestamp('2015-01-01')  in [82]: days.dt.days out[82]: 0     31 1      0 2    456 dtype: int64 

---

reason not use timedelta

in case, cannot work timedelta, not shift date amount of months, appears give kind of mean month length:

in [83]: pd.to_timedelta(1, unit='m') out[83]: timedelta('30 days 10:29:06')  in [84]: (pd.timestamp('2015-01-01') + pd.to_timedelta(m, unit='m')) - pd.timestamp('2015-01-01') out[84]: 0    30 days 10:29:06 1     0 days 00:00:00 2   456 days 13:16:30 dtype: timedelta64[ns] 

so give different answers. example in case, gives 30 days instead of 31 first element.

the pandas equivalent relativedelta use dateoffset. in case eg pd.dateoffset(months=1)