Manipulate JSON-like data with NumPy-like idioms.

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  • License: BSD License (BSD-3-Clause)
  • Author: Jim Pivarski
  • Maintainer: Scikit-HEP
  • Requires: Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, >=2.7
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Project description

PyPI version Conda-Forge Python 2.7,3.5‒3.9 BSD-3 Clause License Continuous integration tests

Scikit-HEP NSF-1836650 DOI Documentation Gitter

Awkward Array is a library for nested, variable-sized data, including arbitrary-length lists, records, mixed types, and missing data, using NumPy-like idioms.

Arrays are dynamically typed, but operations on them are compiled and fast. Their behavior coincides with NumPy when array dimensions are regular and generalizes when they're not.

Motivating example

Given an array of objects with x, y fields and variable-length nested lists like

array = ak.Array([
    [{"x": 1.1, "y": [1]}, {"x": 2.2, "y": [1, 2]}, {"x": 3.3, "y": [1, 2, 3]}],
    [],
    [{"x": 4.4, "y": {1, 2, 3, 4]}, {"x": 5.5, "y": [1, 2, 3, 4, 5]}]
])

the following slices out the y values, drops the first element from each inner list, and runs NumPy's np.square function on everything that is left:

output = np.square(array["y", ..., 1:])

The result is

[
    [[], [4], [4, 9]],
    [],
    [[4, 9, 16], [4, 9, 16, 25]]
]

The equivalent using only Python is

output = []
for sublist in array:
    tmp1 = []
    for record in sublist:
        tmp2 = []
        for number in record["y"][1:]:
            tmp2.append(np.square(number))
        tmp1.append(tmp2)
    output.append(tmp1)

Not only is the expression using Awkward Arrays more concise, using idioms familiar from NumPy, but it's much faster and uses less memory.

For a similar problem 10 million times larger than the one above (on a single-threaded 2.2 GHz processor),

  • the Awkward Array one-liner takes 4.6 seconds to run and uses 2.1 GB of memory,
  • the equivalent using Python lists and dicts takes 138 seconds to run and uses 22 GB of memory.

Speed and memory factors in the double digits are common because we're replacing Python's dynamically typed, pointer-chasing virtual machine with type-specialized, precompiled routines on contiguous data. (In other words, for the same reasons as NumPy.) Even higher speedups are possible when Awkward Array is paired with Numba.

Our presentation at SciPy 2020 provides a good introduction, showing how to use these arrays in a real analysis.

Installation

Awkward Array can be installed from PyPI using pip:

pip install awkward

You will likely get a precompiled binary (wheel), depending on your operating system and Python version. If not, pip attempts to compile from source (which requires a C++ compiler, make, and CMake).

Awkward Array is also available using conda, which always installs a binary:

conda install -c conda-forge awkward

If you have already added conda-forge as a channel, the -c conda-forge is unnecessary. Adding the channel is recommended because it ensures that all of your packages use compatible versions:

conda config --add channels conda-forge
conda update --all

Getting help

How-to tutorials

Python API reference

C++ API reference

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  • License: BSD License (BSD-3-Clause)
  • Author: Jim Pivarski
  • Maintainer: Scikit-HEP
  • Requires: Python !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, >=2.7
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  • Uploaded via: twine/1.15.0 pkginfo/1.7.0 requests/2.25.1 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/2.7.18

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