How do I calculate summary statistics (mean and standard deviation) for python datetime objects in the form YYYY-MM-DD? I want to do this for different groups of datetime obejcts which have different IDs.
Here's what the data look like:
import datetime as dt
df = pd.DataFrame({
'Date': [dt.date(2017,9,1),dt.date(2017,9,21),dt.date(2017,9,14),
dt.date(2017,11,7),dt.date(2017,8,1),dt.date(2017,12,21),
dt.date(2017,12,14),dt.date(2017,10,1),dt.date(2017,10,1)],
'ID': [1,2,3,3,2,1,2,3,2],
})
Date ID
2017-09-01 1
2017-11-01 2
2017-09-01 3
2017-11-07 3
2017-08-01 2
2017-11-01 1
2017-12-01 2
2017-10-01 3
2017-10-01 2
And I want a result that looks like:
ID_1_mean ID_1_sd ID_2_mean ID_2_sd ...
YYYY-MM-DD int YYYY-MM-DD int ...
where YYYY-MM-DD is the mean from the dates in group 1 and int is the mean in group 1, repeated for all the groups.
Here's a somewhat clunky workaround:
datetime.date
to pandas.Timestamp
with pd.to_datetime()
pandas.Timestamp
to integer with .astype(int)
pandas.Timestamp
pandas.Timedelta
Setup:
df = pd.DataFrame({
'Date': [dt.date(2017,9,1),dt.date(2017,9,21),dt.date(2017,9,14),
dt.date(2017,11,7),dt.date(2017,8,1),dt.date(2017,12,21),
dt.date(2017,12,14),dt.date(2017,10,1),dt.date(2017,10,1)],
'ID': [1,2,3,3,2,1,2,3,2],
})
Solution:
df['Date_int'] = pd.to_datetime(df['Date']).astype(int)
res = df.groupby('ID').agg(['mean', 'std'])
res.columns = ['_'.join(c) for c in res.columns.values]
res['Date_mean'] = pd.to_datetime(res['Date_int_mean'])
res['Date_std'] = pd.to_timedelta(res['Date_int_std'])
res = res[['Date_mean', 'Date_std']]
res
Date_mean Date_std
ID
1 2017-10-26 12:00:00 78 days 11:43:56.874291
2 2017-10-01 18:00:00 55 days 15:53:10.401720
3 2017-10-07 16:00:00 27 days 14:38:57.222514
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