Create multiple columns in pandas aggregation function

pandas, python, time-series

Solution

You can pass a dictionary of functions to the `resample` method:

In [35]: ts
Out[35]:
2013-01-01 00:00:00     0
2013-01-01 00:15:00     1
2013-01-01 00:30:00     2
2013-01-01 00:45:00     3
2013-01-01 01:00:00     4
2013-01-01 01:15:00     5
...
2013-01-01 23:00:00    92
2013-01-01 23:15:00    93
2013-01-01 23:30:00    94
2013-01-01 23:45:00    95
2013-01-02 00:00:00    96
Freq: 15T, Length: 97

Create a dictionary of functions:

mhl = {'m':np.mean, 'h':np.max, 'l':np.min}

Pass the dictionary to the `how` parameter of `resample`:

In [36]: ts.resample("30Min", how=mhl)
Out[36]:
                      h     m   l
2013-01-01 00:00:00   1   0.5   0
2013-01-01 00:30:00   3   2.5   2
2013-01-01 01:00:00   5   4.5   4
2013-01-01 01:30:00   7   6.5   6
2013-01-01 02:00:00   9   8.5   8
2013-01-01 02:30:00  11  10.5  10
2013-01-01 03:00:00  13  12.5  12
2013-01-01 03:30:00  15  14.5  14

Problem

I'd like to create multiple columns while resampling a pandas DataFrame like the built-in ohlc method. ``` def mhl(data): return pandas.Series([np.mean(data),np.max(data),np.min(data)],index = ['mean','high','low']) ts.resample('30Min',how=mhl) ``` Dies with ``` Exception: Must produce aggregated value ``` Any suggestions? Thanks!

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