Finding the mean and standard deviation of a timedelta object in pandas df

Question

I would like to calculate the mean and standard deviation of a timedelta by bank from a dataframe with two columns shown

Answer 1

You need to convert timedelta to some numeric value, e.g. int64 by values what is most accurate, because convert to ns is what is the numeric representation of timedelta:

dropped['new'] = dropped['diff'].values.astype(np.int64)

means = dropped.groupby('bank').mean()
means['new'] = pd.to_timedelta(means['new'])

std = dropped.groupby('bank').std()
std['new'] = pd.to_timedelta(std['new'])

Another solution is to convert values to seconds by total_seconds, but that is less accurate:

dropped['new'] = dropped['diff'].dt.total_seconds()

means = dropped.groupby('bank').mean()