Manipulate JSON-like data with NumPy-like idioms.
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 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
This will usually download and install a pure-Python wheel for the core awkward package, and a compiled binary wheel for the awkward-cpp C++ components, depending on your operating system and Python version. If not, pip attempts to compile from source (which requires a C++ compiler).
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
- View the documentation on ReadTheDocs.
- 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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file awkward-2.0.0rc6.tar.gz.
File metadata
- Download URL: awkward-2.0.0rc6.tar.gz
- Upload date:
- Size: 4.9 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 172395f65c047479d2f545eb799ce1b6568d4b79fdbfcb790aecc9c777a91ae7 |
|
| MD5 | 4dadc63074acd00b129e4e5c40c2cb6e |
|
| BLAKE2b-256 | 70d64954d4d0b485cdb264c13303771a2dcb8ff0f1c25d5bb8e28e8729b2c4af |
File details
Details for the file awkward-2.0.0rc6-py3-none-any.whl.
File metadata
- Download URL: awkward-2.0.0rc6-py3-none-any.whl
- Upload date:
- Size: 534.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b7fd0beb649a7cff922f9ccdc9bfd7a3f38488c47e53e669351f00958fa83171 |
|
| MD5 | 224cfcf64b21d2d0c0df9840ec0cb71d |
|
| BLAKE2b-256 | a6dae0c155a5204089ffca5dc534ccb384091d76cd9bd503311ec3c87879c37c |
