Metadata-Version: 1.2
Name: awkward
Version: 0.12.20
Summary: Manipulate arrays of complex data structures as easily as Numpy.
Home-page: https://github.com/scikit-hep/awkward-array
Author: Jim Pivarski (IRIS-HEP)
Author-email: pivarski@princeton.edu
Maintainer: Jim Pivarski (IRIS-HEP)
Maintainer-email: pivarski@princeton.edu
License: BSD 3-clause
Download-URL: https://github.com/scikit-hep/awkward-array/releases
Description: .. image:: https://raw.githubusercontent.com/scikit-hep/awkward-array/master/docs/source/logo-300px.png
           :alt: awkward-array
           :target: https://github.com/scikit-hep/awkward-array
        
        |
        
        .. inclusion-marker-1-5-do-not-remove
        
        Calculations with rectangular, numerical data are simpler and faster in Numpy than traditional for loops. Consider, for instance,
        
        .. code-block:: python
        
            all_r = []
            for x, y in zip(all_x, all_y):
                all_r.append(sqrt(x**2 + y**2))
        
        versus
        
        .. code-block:: python
        
            all_r = sqrt(all_x**2 + all_y**2)
        
        Not only is the latter easier to read, it's hundreds of times faster than the for loop (and provides opportunities for hidden vectorization and parallelization). However, the Numpy abstraction stops at rectangular arrays of numbers or character strings. While it's possible to put arbitrary Python data in a Numpy array, Numpy's ``dtype=object`` is essentially a fixed-length list: data are not contiguous in memory and operations are not vectorized.
        
        Awkward-array is a pure Python+Numpy library for manipulating complex data structures as you would Numpy arrays. Even if your data structures
        
        * contain variable-length lists (jagged/ragged),
        * are deeply nested (record structure),
        * have different data types in the same list (heterogeneous),
        * are masked, bit-masked, or index-mapped (nullable),
        * contain cross-references or even cyclic references,
        * need to be Python class instances on demand,
        * are not defined at every point (sparse),
        * are not contiguous in memory,
        * should not be loaded into memory all at once (lazy),
        
        this library can access them as `columnar data structures <https://towardsdatascience.com/the-beauty-of-column-oriented-data-2945c0c9f560>`__, with the efficiency of Numpy arrays. They may be converted from JSON or Python data, loaded from "awkd" files, `HDF5 <https://www.hdfgroup.org>`__, `Parquet <https://parquet.apache.org>`__, or `ROOT <https://root.cern>`__ files, or they may be views into memory buffers like `Arrow <https://arrow.apache.org>`__.
        
        **Note:** feedback on this project informs the development of `awkward-1.0 <https://github.com/jpivarski/awkward-1.0>`__, a reimplementation in C++ with a simpler user interface, coming in 2020. Leave comments about the future of awkward-array there (as GitHub issues or in the Google Docs).
        
        .. inclusion-marker-2-do-not-remove
        
        Installation
        ============
        
        Install awkward like any other Python package:
        
        .. code-block:: bash
        
            pip install awkward                       # maybe with sudo or --user, or in virtualenv
        
        or install with `conda <https://conda.io/en/latest/miniconda.html>`__:
        
        .. code-block:: bash
        
            conda config --add channels conda-forge   # if you haven't added conda-forge already
            conda install awkward
        
        The base ``awkward`` package requires only `Numpy <https://scipy.org/install.html>`__  (1.13.1+).
        
        Recommended packages:
        ---------------------
        
        - `pyarrow <https://arrow.apache.org/docs/python/install.html>`__ to view Arrow and Parquet data as awkward-arrays
        - `h5py <https://www.h5py.org>`__ to read and write awkward-arrays in HDF5 files
        - `Pandas <https://pandas.pydata.org>`__ as an alternative view
Platform: Any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
