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https://github.com/cbourjau/pyhistogram

Convinient and intuitive histograms with minimal dependencies
https://github.com/cbourjau/pyhistogram

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Convinient and intuitive histograms with minimal dependencies

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README 

        
===========

pyhistogram

===========



**This is an early release which is not yet battle-hardened. Please file an issue over at github if you encounter problems**



What is pyhistogram

===================



pyhistogram is a pure python package for easy handling of histogram data. It offers much more functionality than pythons build-in 'collections' feature. 



pyhistogram interface is heavily inspired by the excellent rootpy package which, however, depends on the gigantic particle physics ROOT framework - an dependency hardly justifiable for small projects. At the moment pyhistogram has no dependencies at all. Matplotlib is optional if one wants to use the built in plotting features. Than also means that this packages performance is nowhere near to that of the rootpy/ROOT solution, but should be sufficient for most use cases. In any case, it is quite possible that numpy might be added as an dependency in the future to use some of its features and to give this package a performance boost. 



Pyhistogram supports histograms of one, two or three dimensions and each axis can be either defined by datetimes, numerical values or even regexes! How cool it that?



Taking it for a spin:

=====================



The following shows some, but by far not all features and focuses on the one dimensional histogram to keep it simple. A proper documentation is on the todo list but for now I can recommend tacking a look at the unittests in the /tests folder over at github.



Installing pyhistogram:

-----------------------

::



   $ pip install pyhistogram



Creating one dimensional histograms:

------------------------------------

::



  from pyhistogram import Hist



  # 1D histogram with fixed-width bins

  # 5 bins from -2 to 4

  h1d = Hist(5, -2, 4)

  # variable-width bins by specifying the bin edges

  h1d = Hist([-10, -3.2, 5.2, 35.])



Histograms can be filled in loops:

----------------------------------

::



  >>> import random

  >>> h = Hist(10, -4, 12)

  >>> for i in xrange(1000):

  >>>     h.fill(random.gauss(4, 3))



And one can easily iterate through all the bins:

------------------------------------------------



::



  >>> h = Hist(4, 0, 4)

  >>> h.fill(1)

  >>> [b.value for b in h.bins()]

  [1, 0, 0, 0]

  

  



A weight can be associated to each value in a 2-tuple:

------------------------------------------------------

::



  >>> h = Hist(4, 0, 4)

  >>> h.fill((1, weight=0.5)

  >>> [b.value for b in h.bins()]

  [0.5, 0, 0, 0]

  



datetime support is also no-brainer:

------------------------------------

::



  from datetime import datetim

  h = Hist(4, datetime(2014, 1, 1, 12, 0), datetime(2014, 1, 1, 16, 0))

  h.fill(datetime(2014, 1, 1, 13, 0))



And even word frequencies (based on regex) are all there for your convenience:

------------------------------------------------------------------------------

::



   >>> hist = Hist(['My', 'name', 'is', 'Bond'])

   >>> [hist.fill(s) for s in ['James', 'Bond']]

   >>> [(b.x.regex, b.value) for b in self.hist.bins()]

   [('My', 0), ('name', 0), ('is', 0), ('Bond', 1)]

   



If matplotlib is available, 1D histograms can also be plotted conveniently:

---------------------------------------------------------------------------

::



  from pyhistogram import Hist

  import numpy as np

  import matplotlib.pyplot as plt

  

  h = Hist(20, -5, 5)

  sample = np.random.normal(size=500)

  for v in sample:

   h.fill(v)

   h.plot()

   plt.show()



Running the included unit tests (for (pyhistogram) developers):

::



   $ nosetests pyhistogram