Quantile/Median/2D binning in Python

Question

do you know a quick/elegant Python/Scipy/Numpy solution for the following problem: You have a set of x, y coordinates with associated values w (all 1D arrays). Now bin x and y onto a 2D grid (size BINSxBINS) and calculate quantiles (like the median) of the w values for each bin, which should at the end result in a BINSxBINS 2D array with the required quantiles. This is easy to do with some nested loop,but I am sure there is a more elegant solution. Thanks, Mark

Accepted Answer

This is what I came up with, I hope it's useful. It's not necessarily cleaner or better than using a loop, but maybe it'll get you started toward something better.

```
import numpy as np
bins_x, bins_y = 1., 1.
x = np.array([1,1,2,2,3,3,3])
y = np.array([1,1,2,2,3,3,3])
w = np.array([1,2,3,4,5,6,7], 'float')

# You can get a bin number for each point like this
x = (x // bins_x).astype('int')
y = (y // bins_y).astype('int')
shape = [x.max()+1, y.max()+1]
bin = np.ravel_multi_index([x, y], shape)

# You could get the mean by doing something like:
mean = np.bincount(bin, w) / np.bincount(bin)

# Median is a bit harder
order = bin.argsort()
bin = bin[order]
w = w[order]
edges = (bin[1:] != bin[:-1]).nonzero()[0] + 1
med_index = (np.r_[0, edges] + np.r_[edges, len(w)]) // 2
median = w[med_index]

# But that's not quite right, so maybe
median2 = [np.median(i) for i in np.split(w, edges)]
```

Also take a look at numpy.histogram2d

Quantile/Median/2D binning in Python

Solution

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