python - Pandas resample to return just one column after an apply as been made - 


def wk(args):     return args['open'].mean() - args['close'].mean()  df = pd.dataframe()  df = data.resample("2b").apply(wk)

i run following code on below dataframe:

             open   high    low  close    volume date                                             2016-01-04  860.0  868.0  849.0  856.0  314041.0 2016-01-05  867.5  870.0  844.0  853.5  292475.0 2016-01-06  863.0  863.0  844.0  861.0  312689.0 2016-01-07  872.0  901.0  871.5  899.5  870578.0

which returns:

             open   high    low  close  volume date                                           2016-01-04   9.00   9.00   9.00   9.00    9.00 2016-01-06 -12.75 -12.75 -12.75 -12.75  -12.75

it's dubious have fives columns same data. how can make resample , apply return 1 column?

so may write

df['one column'] = data.resample("2b").apply(wk)

instead of 

df = data.resample("2b").apply(wk)

row-wise apply , resampler dispatch

use row-wise .apply(func, axis=1) , turn resampled object (returned pandas.tseries.resample.datetimeindexresampler) dataframe dispatch method (sum, mean, first, last...):

import pandas pd  data = pd.read_csv(stringio('''date,open,high,low,close,volume 2016-01-04,860.0,868.0,849.0,856.0,314041.0 2016-01-05,867.5,870.0,844.0,853.5,292475.0 2016-01-06,863.0,863.0,844.0,861.0,312689.0 2016-01-07,872.0,901.0,871.5,899.5,870578.0'''), index_col=0, parse_dates=true)  def wk(args):     return args['open'].mean() - args['close'].mean()  df = data.resample('2b').mean().apply(wk, axis=1)  print df 
date 2016-01-04     9.00 2016-01-06   -12.75 freq: 2b, dtype: float64

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