user2915556 - 1 year ago 101
Python Question

# Average time for datetime list

Looking for fastest solution of time averaging problem.

I've got a list of datetime objects. Need to find average value of time (excluding year, month, day).
Here is what I got so far:

``````import datetime as dtm
def avg_time(times):
avg = 0
for elem in times:
avg += elem.second + 60*elem.minute + 3600*elem.hour
avg /= len(times)
rez = str(avg/3600) + ' ' + str((avg%3600)/60) + ' ' + str(avg%60)
return dtm.datetime.strptime(rez, "%H %M %S")
``````

Here's a better way to approach this problem

Generate a sample of datetimes

``````In [28]: i = date_range('20130101',periods=20000000,freq='s')

In [29]: i
Out[29]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00, ..., 2013-08-20 11:33:19]
Length: 20000000, Freq: S, Timezone: None
``````

avg 20m times

``````In [30]: %timeit pd.to_timedelta(int((i.hour*3600+i.minute*60+i.second).mean()),unit='s')
1 loops, best of 3: 2.87 s per loop
``````

The result as a timedelta (note that this requires numpy 1.7 and pandas 0.13 for the `to_timedelta` part, coming very soon)

``````In [31]: pd.to_timedelta(int((i.hour*3600+i.minute*60+i.second).mean()),unit='s')
Out[31]:
0   11:59:12
dtype: timedelta64[ns]
``````

In seconds (this will work for pandas 0.12, numpy >= 1.6).

``````In [32]: int((i.hour*3600+i.minute*60+i.second).mean())
Out[32]: 43152
``````
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