Question

I have a 2-d array containing pairs of values and I’d like to make a boxplot of the y-values by different bins of the x-values. I.e. if the array is:

my_array = array([[1, 40.5], [4.5, 60], ...]])

then I’d like to bin my_array[:, 0] and then for each of the bins, produce a boxplot of the corresponding my_array[:, 1] values that fall into each box. So in the end I want the plot to contain number of bins-many box plots.

I tried the following:

min_x = min(my_array[:, 0])
max_x = max(my_array[:, 1])

num_bins = 3
bins = linspace(min_x, max_x, num_bins)
elts_to_bins = digitize(my_array[:, 0], bins)

However, this gives me values in elts_to_bins that range from 1 to 3. I thought I should get 0-based indices for the bins, and I only wanted 3 bins. I’m assuming this is due to some trickyness with how bins are represented in linspace vs. digitize.

What is the easiest way to achieve this? I want num_bins-many equally spaced bins, with the first bin containing the lower half of the data and the upper bin containing the upper half… i.e., I want each data point to fall into some bin, so that I can make a boxplot.

thanks.

Answer 1

Numpy has a dedicated function for creating histograms the way you need to:

histogram(a, bins=10, range=None, normed=False, weights=None, new=None)

which you can use like:

(hist_data, bin_edges) = histogram(my_array[:,0], weights=my_array[:,1])

The key point here is to use the weights argument: each value a[i] will contribute weights[i] to the histogram. Example:

a = [0, 1]
weights = [10, 2]

describes 10 points at x = 0 and 2 points at x = 1.

You can set the number of bins, or the bin limits, with the bins argument (see the official documentation for more details).

The histogram can then be plotted with something like:

bar(bin_edges[:-1], hist_data)

If you only need to do a histogram plot, the similar hist() function can directly plot the histogram:

hist(my_array[:,0], weights=my_array[:,1])