Standard deviation from numpy 2d histogram

Is there a method to determine the standard deviation in each bin using the numpy histrogram2d method, given a series of 2d coordinates along with weighted values for each coordinate?

It's not directly possible with numpy's histrogram2d but with scipy.stats.binned_statistic_2d it can be done quite easily.

from scipy import stats

x = np.random.rand(10000)
y = np.random.rand(10000)
z = np.random.rand(10000)
binx = np.linspace(0,x.max(), 100)
biny = np.linspace(0,y.max(), 100)
hist = stats.binned_statistic_2d(x, y, z, statistic='std', bins=[binx, biny])
plot_x, plot_y = np.meshgrid(binx, biny)

fig, ax = plt.subplots(figsize=(5,5))
pc = ax.pcolormesh(plot_x, plot_y, hist[0].T, cmap="inferno")
ax.set_aspect('equal')
cbar=fig.colorbar(pc,shrink=0.725)
fig.tight_layout()

The statistic option can also take different things like mean, median or a user-defined function, see the documentation for more details.