Reputation: 6263
If I have a dataframe such as:
index = pd.date_range(start='2014 01 01 00:00', end='2014 01 05 00:00', freq='12H')
df = pd.DataFrame(pd.np.random.randn(9),index=index,columns=['A'])
df
Out[5]:
A
2014-01-01 00:00:00 2.120577
2014-01-01 12:00:00 0.968724
2014-01-02 00:00:00 1.232688
2014-01-02 12:00:00 0.328104
2014-01-03 00:00:00 -0.836761
2014-01-03 12:00:00 -0.061087
2014-01-04 00:00:00 -1.239613
2014-01-04 12:00:00 0.513896
2014-01-05 00:00:00 0.089544
And I want to resample to daily frequency, it is quite easy:
df.resample(rule='1D',how='mean')
Out[6]:
A
2014-01-01 1.544650
2014-01-02 0.780396
2014-01-03 -0.448924
2014-01-04 -0.362858
2014-01-05 0.089544
However, I need to track how many instances are going into each day. Is there a good pythonic way of using resample to both perform the specified "how" operation AND track number of data points going into each mean value, e.g. yielding
Out[6]:
A Instances
2014-01-01 1.544650 2
2014-01-02 0.780396 2
2014-01-03 -0.448924 2
2014-01-04 -0.362858 2
2014-01-05 0.089544 2
Upvotes: 2
Views: 2844
Reputation: 35245
Conveniently, how
accepts a list:
df1 = df.resample(rule='1D', how=['mean', 'count'])
This will return a DataFrame with a MultiIndex column: one level for 'A' and another level for 'mean' and 'count'. To get a simple DataFrame like the desired output in your question, you can drop the extra level like df1.columns = df1.columns.droplevel(0)
or, better, you can do your resampling on df['A']
instead of df
.
Upvotes: 4