python - selecting secondary index by tuple of labels -


i have sales data indexed on ('dt', 'product_id') this:

in [43]: sub.head() out[43]:                            income dt          product_id 2015-01-15  10016          23 2015-01-15  10017          188 2015-01-15  10018          nan 2015-01-16  10016          188 2015-01-17  10025         1000 # goes on , on...

how can view income of product 10016 , 10025 in between 2015-01-15 , 2015-01-16? tried learn pandas slicers here couldn't right:

in [44]: sub.loc[idx[start:end,[10016,10018]]]  keyerror: 'none of [[10055, 10158]] in [columns]'

raw data

import pandas pd  product_order = pd.dataframe.from_csv('order.csv') odr = product_order.set_index(['dt','product_id']) 
dt,product_id,subsidy 2015-03-03 00:39:08+08:00,10029,50.00 2015-03-09 00:47:00+08:00,10016,55.00 2015-03-13 01:00:12+08:00,10029,23.00 2015-03-15 01:03:40+08:00,10016,21.00 2015-03-16 02:18:45+08:00,10016,52.00

assuming here gp groupby object can slice following:

in [146]: idx = pd.indexslice gp.loc[idx['2015-01-15':'2015-01-16'], idx[10016:10025]]  out[146]: dt          product_id 2015-01-15  10016          23             10017         188             10018         nan 2015-01-16  10016         188 name: income, dtype: float64

so need define indexslice each level want perform row selection criteria on


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