find mean bin values using histogram2d python [duplicate]

Answer 1

This is doable at least with the following method

# xedges, yedges as returned by 'histogram2d'

# create an array for the output quantities
avgarr = np.zeros((nbins, nbins))

# determine the X and Y bins each sample coordinate belongs to
xbins = np.digitize(x, xedges[1:-1])
ybins = np.digitize(y, yedges[1:-1])

# calculate the bin sums (note, if you have very many samples, this is more
# effective by using 'bincount', but it requires some index arithmetics
for xb, yb, v in zip(xbins, ybins, verification):
    avgarr[yb, xb] += v

# replace 0s in H by NaNs (remove divide-by-zero complaints)
# if you do not have any further use for H after plotting, the
# copy operation is unnecessary, and this will the also take care
# of the masking (NaNs are plotted transparent)
divisor = H.copy()
divisor[divisor==0.0] = np.nan

# calculate the average
avgarr /= divisor

# now 'avgarr' contains the averages (NaNs for no-sample bins)

If you know the bin edges beforehand, you can do the histogram part in the same just by adding one row.

Answer 2

There is an elegant and fast way to do this! Use weights parameter to sum values:

denominator, xedges, yedges = np.histogram2d(x,y,bins=nbins)
nominator, _, _ = np.histogram2d(x,y,bins=[xedges, yedges], weights=verification)

So all you need is to divide in each bin the sum of values by the number of events:

result = nominator / denominator

Voila!