daign - 1 year ago 129
Python Question

# Replacing entries in a numpy array with their quantile index with python

I have a one-dimensional numpy array with numbers, and I want each number replaced with the index of the quantile it belongs to.

This is my code for quintile indices:

``````import numpy as np

def get_quintile_indices( a ):

result = np.ones( a.shape[ 0 ] ) * 4

quintiles = [
np.percentile( a, 20 ),
np.percentile( a, 40 ),
np.percentile( a, 60 ),
np.percentile( a, 80 )
]

for q in quintiles:
result -= np.less_equal( a, q ) * 1

return result

a = np.array( [ 58, 54, 98, 76, 35, 13, 62, 18, 62, 97, 44, 43 ] )
print get_quintile_indices( a )
``````

Output:

``````[ 2.  2.  4.  4.  0.  0.  3.  0.  3.  4.  1.  1.]
``````

You see I start with an array initialized with the highest possible index and for every quintile cutpoint substract 1 from each entry that is less or equal than the quintile cutpoint. Is there a better way to do this? A build-in function that can be used to map numbers against a list of cutpoints?

First off, we can generate those `quintiles` in one go -

``````quintiles = np.percentile( a, [20,40,60,80] )
``````

For the final step to get the offsets, we can simply use `np.searchsorted` and this might be the built-in you were looking for, like so -

``````out = quintiles.searchsorted(a)
``````

Alternatively, a direct translation of your loopy code to a vectorized version would be with `broadcasting`, like so -

``````# Use broadcasting to perform those comparisons in one go.
# Then, simply sum along the first axis and subtract from 4.
out = 4 - (quintiles[:,None] >=  a).sum(0)
``````
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