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Convenience wrappers around numpy histograms

Project Description

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.


# 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))

# 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:

# 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))

# x and y projections return Hist1d objects
m2.projection('x').plot(label='x projection')
m2.projection(1).plot(label='y projection')


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 ( and one of its python bindings (pyROOT, rootpy).

If you do come from a ROOT background, you might appreciate pyhistogram ( instead, which has a much more ROOT-like interface.

Another python histogram package oriented towards physics is This has support for physical units in histograms and error propagation, but the interface is further removed from numpy.


0.5.4 (2017-09-20)

  • Fix issue with input from dask

0.5.3 (2017-09-18)

  • Fix python 2 support

0.5.2 (2017-08-08)

  • Fix colorbar arguments to Histdd.plot (#4)
  • percentile for Hist1d
  • rebin method for Histdd (experimental)

0.5.1 (2017-03-22)

  • get_random for Histdd no longer just returns bin centers (Hist1d does stil…)
  • lookup for Hist1d. When will I finally merge the classes…

0.5.0 (2016-10-07)

  • pandas.DataFrame and dask.dataframe support
  • dimensions option to Histdd to init axis_names and bin_centers at once

0.4.3 (2016-10-03)

  • Remove matplotlib requirement (still required for plotting features)

0.4.2 (2016-08-10)

  • Fix small bug for >=3 d histograms

0.4.1 (2016-17-14)

  • get_random and lookup for Histdd. Not really tested yet.

0.4.0 (2016-02-05)

  • .std function for Histdd
  • Fix off-by-one errors

0.3.0 (2015-09-28)

  • Several new histdd functions: cumulate, normalize, percentile…
  • Python 2 compatibility

0.2.1 (2015-08-18)

  • Histdd functions sum, slice, average now also work

0.2 (2015-08-06)

  • Multidimensional histograms
  • Axes naming

0.1.1-4 (2015-08-04)

Correct various rookie mistakes in packaging… Hey, it’s my first pypi package!

0.1 (2015-08-04)

Initial release

  • Hist1d, Hist2d
  • Basic test suite
  • Basic readme

Release History

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