Manipulate JSON-like data with NumPy-like idioms.
Reason this release was yanked:
The `flatten_records` argument shouldn't have been removed, just the no-longer-implemented value for that argument (`True`). Fixed in 2.0.4.
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.
Motivating example
Given an array of lists of objects with x, y fields (with nested lists in the y field),
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:
conda install -c conda-forge awkward
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