Date Calculation (typeerror: Unsupported Operand Type(s) For -: 'str' And 'str')

I have a data set as below: date_time srch_co srch_ci 0 2014-11-03 16:02:28 2014-12-19 2014-12-15 1 2013-03-13 19:25:01 2013-03-14 2013-03-13 2 2014-10-13

Solution 1:

You need convert columns srch_co and srch_cito_datetime first and then use mask for replace values less as 0 to NaN (default value of mask function):

sample["srch_co"]=pd.to_datetime(sample["srch_co"])sample["srch_ci"]=pd.to_datetime(sample["srch_ci"])sample["duration"]=(sample["srch_co"]-sample["srch_ci"])/np.timedelta64(1,'D')sample["days_in_advance"]=(sample["srch_co"]-sample["date_time"])/np.timedelta64(1,'D')cols= ['duration','days_in_advance']
sample[cols]=sample[cols].mask(sample[cols]<0)#first value of srch_ci column was changed for NaN outputprint(sample)date_timesrch_cosrch_cidurationdays_in_advance02014-11-03 16:02:28 2014-12-19 2015-12-15       NaN45.33162012013-03-13 19:25:01 2013-03-14 2013-03-13       1.00.19096122014-10-13 13:20:25 2015-04-10 2015-04-03       7.0178.44415532013-11-05 10:40:34 2013-11-08 2013-11-07       1.02.55516242014-06-10 13:34:56 2014-08-08 2014-08-03       5.058.43407452014-12-16 14:34:39 2014-12-17 2014-12-16       1.00.392604

Solution 2:

Seems like you're subtracting a string from a string. Make sure to convert the column to type 'date' using pd.to_datetime, and then you'll be able to subtract one day from another.

Another recommendation is to avoid for loops and to use vectorized operations, such as pd.DataFrame.subtract(series, axis=0), as that is one of the biggest advantages of using pandas over any simple list.

After you've calculated the difference, then you can turn all negatives into nan by saying

dataframe[dataframe['duration'] < 0] = np.nan