https://github.com/JelleAalbers/multihist
Thin wrapper around numpy's histogram and histogramdd.
Numpy has great histogram functions, which return (histogram, bin_edges) tuples. This package wraps these in a class with methods for adding new data to existing histograms, take averages, projecting, etc.
For 1-dimensional histograms you can access cumulative and density information, as well as basic statistics (mean and std). For d-dimensional histograms you can name the axes, and refer to them by their names when projecting / summing / averaging.
Synopsis:
# Create histograms just like from numpy...
m = Hist1d([0, 3, 1, 6, 2, 9], bins=3)
# ...or add data incrementally:
m = Hist1d(bins=100, range=(-3, 4))
m.add(np.random.normal(0, 0.5, 10**4))
m.add(np.random.normal(2, 0.2, 10**3))
# Get the data back out:
print(m.histogram, m.bin_edges)
# Access derived quantities like bin_centers, normalized_histogram, density, cumulative_density, mean, std
plt.plot(m.bin_centers, m.normalized_histogram, label="Normalized histogram", linestyle='steps')
plt.plot(m.bin_centers, m.density, label="Empirical PDF", linestyle='steps')
plt.plot(m.bin_centers, m.cumulative_density, label="Empirical CDF", linestyle='steps')
plt.title("Estimated mean %0.2f, estimated std %0.2f" % (m.mean, m.std))
plt.legend(loc='best')
plt.show()
# Slicing and arithmetic behave just like ordinary ndarrays
print("The fourth bin has %d entries" % m[3])
m[1:4] += 4 + 2 * m[-27:-24]
print("Now it has %d entries" % m[3])
# Of course I couldn't resist adding a canned plotting function:
m.plot()
plt.show()
# Create and show a 2d histogram. Axis names are optional.
m2 = Histdd(bins=100, range=[[-5, 3], [-3, 5]], axis_names=['x', 'y'])
m2.add(np.random.normal(1, 1, 10**6), np.random.normal(1, 1, 10**6))
m2.add(np.random.normal(-2, 1, 10**6), np.random.normal(2, 1, 10**6))
m2.plot()
plt.show()
# x and y projections return Hist1d objects
m2.projection('x').plot(label='x projection')
m2.projection(1).plot(label='y projection')
plt.legend()
plt.show()
Of course, the easiest alternative is just to use np.histogram without any wrappers.
If you're looking for a more fancy histogram class, and don't mind installing a big framework, you might like the particle physics workhorse ROOT (https://root.cern.ch/root/html/TH1.html) and one of its python bindings (pyROOT, rootpy).
If you do come from a ROOT background, you might appreciate pyhistogram (https://github.com/cbourjau/pyhistogram) instead, which has a much more ROOT-like interface.
Another python histogram package oriented towards physics is http://docs.danse.us/histogram/0.2.1/intro.html. This has support for physical units in histograms and error propagation, but the interface is further removed from numpy.