P(i/y)thon h(i/y)stograms. Inspired (and based on) numpy.histogram, but designed for humans(TM) on steroids(TM).

The goal is to unify different concepts of histograms as occurring in numpy, pandas, matplotlib, ROOT, etc. and to create one representation that is easily manipulated with from the data point of view and at the same time provides nice integration into IPython notebook and various plotting options. In short, whatever you want to do with histograms, physt aims to be on your side.

from physt import histogram

# Create the sample
heights = [160, 155, 156, 198, 177, 168, 191, 183, 184, 179, 178, 172, 173, 175,
           172, 177, 176, 175, 174, 173, 174, 175, 177, 169, 168, 164, 175, 188,
           178, 174, 173, 181, 185, 166, 162, 163, 171, 165, 180, 189, 166, 163,
           172, 173, 174, 183, 184, 161, 162, 168, 169, 174, 176, 170, 169, 165]

hist = histogram(heights, 10)    # <--- get the histogram data
hist.plot()                      # <--- and plot it

from physt import h2
import seaborn as sns

iris = sns.load_dataset('iris')
iris_hist = h2(iris["sepal_length"], iris["sepal_width"], "human", (12, 7), name="Iris")
iris_hist.plot(show_zero=False, cmap=cm.gray_r, show_values=True);

import numpy as np
from physt import special

# Generate some sample data
data = np.empty((n, 3))
data[:,0] = np.random.normal(0, 1, n)
data[:,1] = np.random.normal(0, 1.3, n)
data[:,2] = np.random.normal(1, .6, n)

# Get histogram data (in spherical coordinates)
h = special.spherical_histogram(data)                 

# And plot its projection on a globe
h.projection("theta", "phi").plot.globe_map(density=True, figsize=(7, 7), cmap="rainbow")   

See more in docstring's and notebooks:

• Basic tutorial: http://nbviewer.jupyter.org/github/janpipek/physt/blob/master/doc/tutorial.ipynb
• Binning: http://nbviewer.jupyter.org/github/janpipek/physt/blob/master/doc/binning.ipynb
• Bokeh plots: http://nbviewer.jupyter.org/github/janpipek/physt/blob/master/doc/bokeh_examples.ipynb
• 2D histograms: http://nbviewer.jupyter.org/github/janpipek/physt/blob/master/doc/2d_histograms.ipynb
• Special histograms (polar, spherical, cylindrical - beta): http://nbviewer.jupyter.org/github/janpipek/physt/blob/master/doc/special_histograms.ipynb
• Adaptive histograms: http://nbviewer.jupyter.org/github/janpipek/physt/blob/master/doc/adaptive_histogram.ipynb
• Use dask for large (not "big") data - alpha: http://nbviewer.jupyter.org/github/janpipek/physt/blob/master/doc/dask.ipynb
• Geographical bins . alpha: http://nbviewer.jupyter.org/github/janpipek/physt/blob/master/doc/geospatial.ipynb

Using pip:

pip install physt

Using conda (not always up-to-date):

conda install -c janpipek physt

• 1D histograms
• 2D histograms
• ND histograms
• Some special histograms
  • 2D polar coordinates (with plotting)
  • 3D spherical / cylindrical coordinates (beta)
• Adaptive rebinning for on-line filling of unknown data (beta)
• Non-consecutive bins
• Memory-effective histogramming of dask arrays (beta)
• Understands any numpy-array-like object
• Keep underflow / overflow / missed bins
• Basic numeric operations (* / + -)
• Items / slice selection (including mask arrays)
• Add new values (fill, fill_n)
• Cumulative values, densities
• Simple statistics for original data (mean, std, sem)
• Simple plotting (matplotlib, bokeh, folium)
• Algorithms for optimized binning
  • human-friendly
  • mathematical
• IO, conversions
  • I/O xarray.DataSet
  • I/O JSON
  • O pandas.DataFrame

• Rebinning
  • using reference to original data?
  • merging bins
• Statistics (based on original data)?
• Stacked histograms (with names)
• More plotting backends

• Kernel density estimates - use your favourite statistics package (like seaborn)
• Rebinning using interpolation - it should be trivial to use rebin (https://github.com/jhykes/rebin) with physt

Rationale (for both): physt is dumb, but precise.

• Python 3.5+ targeted, 2.7 passes unit tests (hopefully)
• numpy
• (optional) matplotlib - simple output
• (optional) bokeh - simple output
• (optional) xarray - I/O
• (optional) astropy - additional binning algorithms
• (optional) folium - map plotting
• (testing) py.test, pandas
• (docs) sphinx, sphinx_rtd_theme, ipython

I am looking for anyone interested in using / developing physt. You can contribute by reporting errors, implementing missing features and suggest new one.