Development of awkward 1.0, to replace scikit-hep/awkward-array in 2020.

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Project description

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.

How-to documentation
for data analysts

How-it-works tutorials
for developers

Python
API reference

C++
API reference

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.

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Release history Release notifications | RSS feed

1.0.0

Dec 5, 2020

1.0.0rc1 pre-release

Dec 1, 2020

0.4.5

Nov 24, 2020

0.4.4

Nov 17, 2020

0.4.3

Nov 5, 2020

0.4.2

Nov 4, 2020

0.4.1

Nov 3, 2020

0.4.0

Oct 29, 2020

0.3.1

Sep 21, 2020

0.3.0

Sep 21, 2020

0.2.38

Sep 18, 2020

0.2.37

Sep 12, 2020

0.2.36

Sep 8, 2020

0.2.35

Aug 26, 2020

0.2.34

Aug 25, 2020

0.2.33

Aug 22, 2020

0.2.32

Aug 19, 2020

0.2.31

Aug 15, 2020

0.2.30

Aug 11, 2020

0.2.29

Aug 10, 2020

0.2.28

Aug 7, 2020

This version

0.2.27

Jul 23, 2020

0.2.26

Jul 23, 2020

0.2.25

Jul 11, 2020

0.2.24

Jun 12, 2020

0.2.23

Jun 6, 2020

0.2.22

May 29, 2020

0.2.21

May 28, 2020

0.2.20

May 14, 2020

0.2.19

May 8, 2020

0.2.10

Apr 7, 2020

0.2.0

Mar 6, 2020

0.1.128

Feb 29, 2020

0.1.92

Jan 28, 2020

0.1.38

Dec 28, 2019

0.1.25

Nov 27, 2019

0.1.17

Oct 22, 2019

0.1.7

Sep 26, 2019

0.1.0

Aug 27, 2019

0.0.14

Aug 17, 2019

0.0.10

Aug 16, 2019

0.0.6

Aug 16, 2019

0.0.1

Aug 16, 2019

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