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.

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.

• 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.

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:

conda install -c conda-forge awkward

Because of the two packages (awkward-cpp may be updated in GitHub but not on PyPI), pip install through git (pip install git+https://...) will not work. Instead, use the Installation for developers section below.

Clone this repository recursively to get the header-only C++ dependencies, then generate sources with nox, compile and install awkward-cpp, and finally install awkward as an editable installation:

git clone --recursive https://github.com/scikit-hep/awkward.git
cd awkward

nox -s prepare
python -m pip install -v ./awkward-cpp
python -m pip install -e .

Tests can be run in parallel with pytest:

python -m pytest -n auto tests

For more details, see CONTRIBUTING.md, or one of the links below.

• Continuous integration and continuous deployment are hosted by GitHub Actions.
• Code of conduct for how we work together.
• The LICENSE is BSD-3.

The documentation is on awkward-array.org, including

• Getting started
• User guide
• API reference
• Tutorials (with videos)
• Papers and talks about Awkward Array

The Release notes for each version are in the GitHub Releases tab.

The Roadmap, Plans, and Deprecation Schedule are in the GitHub Wiki.

To cite Awkward Array in a paper, see the "Cite this repository" drop-down menu on the top-right of the GitHub front page. The BibTeX is

@software{Pivarski_Awkward_Array_2018,
author = {Pivarski, Jim and Osborne, Ianna and Ifrim, Ioana and Schreiner, Henry and Hollands, Angus and Biswas, Anish and Das, Pratyush and Roy Choudhury, Santam and Smith, Nicholas and Goyal, Manasvi},
doi = {10.5281/zenodo.4341376},
month = {10},
title = {{Awkward Array}},
year = {2018}
}

Support for this work was provided by NSF cooperative agreement OAC-1836650 (IRIS-HEP), grant OAC-1450377 (DIANA/HEP), PHY-2121686 (US-CMS LHC Ops), and OAC-2103945 (Awkward Array).

We also thank Erez Shinan and the developers of the Lark standalone parser, which is used to parse type strings as type objects.

Thanks especially to the gracious help of Awkward Array contributors (including the original repository).

Jim Pivarski 💻 📖 🚇 🚧	Ianna Osborne 💻	Pratyush Das 💻	Anish Biswas 💻	glass-ships 💻 ⚠️	Henry Schreiner 💻 🚇	Nicholas Smith 💻 ⚠️
Lindsey Gray 💻 ⚠️	Ellipse0934 ⚠️	Dmitry Kalinkin 🚇	Charles Escott 💻	Mason Proffitt 💻	Michael Hedges 💻	Jonas Rembser 💻
Jaydeep Nandi 💻	benkrikler 💻	bfis 💻	Doug Davis 💻	Joosep Pata 🤔	Martin Durant 🤔	Gordon Watts 🤔
Nikolai Hartmann 💻	Simon Perkins 💻	.hard 💻 ⚠️	HenryDayHall 💻	Angus Hollands ⚠️ 💻	ioanaif 💻 ⚠️	Bernhard M. Wiedemann 🚧
Matthew Feickert 🚧	Santam Roy Choudhury ⚠️	Jeroen Van Goey 📖	Ahmad-AlSubaie 💻	Manasvi Goyal 💻	Aryan Roy 💻	Saransh 💻
Laurits Tani 📖	Daniel Savoiu 💻	Ray Bell 📖

💻: code, 📖: documentation, 🚇: infrastructure, 🚧: maintenance, ⚠: tests and feedback, 🤔: foundational ideas.