python - apply pandas qcut function to subgroups -


let assume created dataframe df using code below. have created bin frequency count based on 'value' column in df. how frequency count of these label=1 samples frequency count based on previous created bin? obviously, should not use qcut label = 1 samples count, since bin positions not same before.

import numpy np import pandas pd mu, sigma = 0, 0.1 theta = 0.3 s = np.random.normal(mu, sigma, 100) group = np.random.binomial(1, theta, 100) df = pd.dataframe(np.vstack([s,group]).transpose()) df.columns = ['value','label'] factor = pd.qcut(df['value'], 5) factor_bin_count = pd.value_counts(factor)

update: took solution jeff

df.groupby(['label',factor]).value.count()

if understand question. want take grouping factor (e.g. created using qcut bin continuous values), , grouper (e.g. 'label'), perform operation. count in case.

in [36]: df.groupby(['label',factor]).value.count() out[36]:  label  value              0      [-0.248, -0.0864]     14        (-0.0864, -0.0227]    15        (-0.0227, 0.0208]     15        (0.0208, 0.0718]      17        (0.0718, 0.24]        13 1      [-0.248, -0.0864]      6        (-0.0864, -0.0227]     5        (-0.0227, 0.0208]      5        (0.0208, 0.0718]       3        (0.0718, 0.24]         7 name: value, dtype: int64

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