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Author: Jim Pivarski

Requires Python: >=3.9

Classifiers

Development Status

5 - Production/Stable

Intended Audience

Developers
Information Technology
Science/Research

License

OSI Approved :: BSD License

Operating System

MacOS :: MacOS X
Microsoft :: Windows
POSIX :: Linux
Unix

Programming Language

Python
Python :: 3
Python :: 3 :: Only
Python :: 3.9
Python :: 3.10
Python :: 3.11
Python :: 3.12
Python :: 3.13

Topic

Scientific/Engineering
Scientific/Engineering :: Information Analysis
Scientific/Engineering :: Mathematics
Scientific/Engineering :: Physics
Software Development
Utilities

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 lists of objects with x, y fields (with nested lists in the y field),

import awkward as ak

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)

The expression using Awkward Arrays is more concise, using idioms familiar from NumPy, and it also has NumPy-like performance. For a similar problem 10 million times larger than the one above (single-threaded on a 2.2 GHz processor),

the Awkward Array one-liner takes 1.5 seconds to run and uses 2.1 GB of memory,
the equivalent using Python lists and dicts takes 140 seconds to run and uses 22 GB of memory.

Awkward Array is even faster when used in Numba's JIT-compiled functions.

See the Getting started documentation on awkward-array.org for an introduction, including a no-install demo you can try in your web browser.

Getting help

View the documentation on awkward-array.org.
Report bugs, request features, and ask for additional documentation on GitHub Issues.
If you have a "How do I...?" question, start a GitHub Discussion with category "Q&A".
Alternatively, ask about it on StackOverflow with the [awkward-array] tag. Be sure to include tags for any other libraries that you use, such as Pandas or PyTorch.
To ask questions in real time, try the Gitter Scikit-HEP/awkward-array chat room.

Installation

Awkward Array can be installed from PyPI using pip:

pip install awkward

The awkward package is pure Python, and it will download the awkward-cpp compiled components as a dependency. If there is no awkward-cpp binary package (wheel) for your platform and Python version, pip will attempt to compile it from source (which has additional dependencies, such as a C++ compiler).

Awkward Array is also available on conda-forge: