CODEWITHSUNDEEP
pythonnumpy
xnx
xnx

Reputation: 25518

using numpy percentile on binned data

Suppose house sale figures are presented for a town in ranges:

< $100,000              204
$100,000 - $199,999    1651
$200,000 - $299,999    2405
$300,000 - $399,999    1972
$400,000 - $500,000     872
> $500,000             1455

I want to know which house-price bin a given percentile falls. Is there a way of using numpy's percentile function to do this? I can do it by hand:

import numpy as np
a = np.array([204., 1651., 2405., 1972., 872., 1455.])
b = np.cumsum(a)/np.sum(a) * 100
q = 75
len(b[b <= q])
4       # ie bin $300,000 - $399,999

But is there a way to use np.percentile instead?

Upvotes: 5

Views: 5114

Answers (1)

perimosocordiae
perimosocordiae

Reputation: 17797

You were almost there:

cs = np.cumsum(a)
bin_idx = np.searchsorted(cs, np.percentile(cs, 75))

At least for this case (and a couple others with larger a arrays), it's not any faster, though:

In [9]: %%timeit
   ...: b = np.cumsum(a)/np.sum(a) * 100
   ...: len(b[b <= 75])
   ...:
10000 loops, best of 3: 38.6 µs per loop

In [10]: %%timeit
   ....: cs = np.cumsum(a)
   ....: np.searchsorted(cs, np.percentile(cs, 75))
   ....:
10000 loops, best of 3: 125 µs per loop

So unless you want to check for multiple percentiles, I'd stick with what you have.

Upvotes: 2

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