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!
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
"mean" than to use np.mean. Same goes for 'min' and 'max'
sum instead of mean) and then override the method for a single column?
mhl = {'data_column_1': {'resultA': np.mean, 'resultB': max}, 'data_column_2': {'resultC': min, 'resultD': sum}}