# binning data in python with scipy/numpy

## binning data in python with scipy/numpy

Its probably faster and easier to use `numpy.digitize()`:

``````import numpy
data = numpy.random.random(100)
bins = numpy.linspace(0, 1, 10)
digitized = numpy.digitize(data, bins)
bin_means = [data[digitized == i].mean() for i in range(1, len(bins))]
``````

An alternative to this is to use `numpy.histogram()`:

``````bin_means = (numpy.histogram(data, bins, weights=data)[0] /
numpy.histogram(data, bins)[0])
``````

Try for yourself which one is faster… ðŸ™‚

The Scipy (>=0.11) function scipy.stats.binned_statistic specifically addresses the above question.

For the same example as in the previous answers, the Scipy solution would be

``````import numpy as np
from scipy.stats import binned_statistic

data = np.random.rand(100)
bin_means = binned_statistic(data, data, bins=10, range=(0, 1))[0]
``````

#### binning data in python with scipy/numpy

Not sure why this thread got necroed; but here is a 2014 approved answer, which should be far faster:

``````import numpy as np

data = np.random.rand(100)
bins = 10
slices = np.linspace(0, 100, bins+1, True).astype(np.int)
counts = np.diff(slices)

mean = np.add.reduceat(data, slices[:-1]) / counts
print mean
``````