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:warning:root_numpy is deprecated and unmaintained:warning:

root_numpy has not been actively maintained or developed in several years. This is mostly due to the emergence of new alternatives which are both faster and more flexible.

• uproot provides support for reading and writing ROOT files without the need for an installation of ROOT. See here for details. <https://github.com/scikit-hep/uproot4>_
• ROOT now natively supports converting objects into numpy arrays using either directly using TTree <https://root.cern/doc/master/pyroot002__TTreeAsMatrix_8py.html>_ or with the newer RDataFrame <https://root.cern.ch/doc/master/df026__AsNumpyArrays_8py.html>_

root_numpy: The interface between ROOT and NumPy

.. image:: https://img.shields.io/pypi/v/root_numpy.svg :target: https://pypi.python.org/pypi/root_numpy .. image:: https://api.travis-ci.org/scikit-hep/root_numpy.png :target: https://travis-ci.org/scikit-hep/root_numpy .. image:: https://coveralls.io/repos/github/scikit-hep/root_numpy/badge.svg?branch=master :target: https://coveralls.io/github/scikit-hep/root_numpy?branch=master .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.592881.svg :target: https://doi.org/10.5281/zenodo.592881

[see the full documentation] <http://scikit-hep.org/root_numpy>_

root_numpy is a Python extension module that provides an efficient interface between ROOT <http://root.cern.ch/>_ and NumPy <http://www.numpy.org/>_. root_numpy's internals are compiled C++ and can therefore handle large amounts of data much faster than equivalent pure Python implementations.

With your ROOT data in NumPy form, make use of NumPy's broad library <http://docs.scipy.org/doc/numpy/reference/>, including fancy indexing, slicing, broadcasting, random sampling, sorting, shape transformations, linear algebra operations, and more. See this tutorial <https://docs.scipy.org/doc/numpy/user/quickstart.html> to get started. NumPy is the fundamental library of the scientific Python ecosystem. Using NumPy arrays opens up many new possibilities beyond what ROOT offers. Convert your TTrees into NumPy arrays and use SciPy <http://www.scipy.org/>_ for numerical integration and optimization, matplotlib <http://matplotlib.org/>_ for plotting, pandas <http://pandas.pydata.org/>_ for data analysis, statsmodels <http://statsmodels.sourceforge.net/>_ for statistical modelling, scikit-learn <http://scikit-learn.org/>_ for machine learning, and perform quick exploratory analysis in a Jupyter notebook <https://jupyter.org/>_.

At the core of root_numpy are powerful and flexible functions for converting ROOT TTrees <https://root.cern.ch/doc/master/classTTree.html>_ into structured NumPy arrays <http://docs.scipy.org/doc/numpy/user/basics.rec.html>_ as well as converting NumPy arrays back into ROOT TTrees. root_numpy can convert branches of strings and basic types such as bool, int, float, double, etc. as well as variable-length and fixed-length multidimensional arrays and 1D or 2D vectors of basic types and strings. root_numpy can also create columns in the output array that are expressions involving the TTree branches similar to TTree::Draw().

Did we mention that root_numpy is fast?

.. image:: benchmarks/bench_tree2array.png