Manipulate arrays of complex data structures as easily as Numpy.
Calculations with rectangular, numerical data are simpler and faster in Numpy than traditional for loops. Consider, for instance,
all_r =  for x, y in zip(all_x, all_y): all_r.append(sqrt(x**2 + y**2))
all_r = sqrt(all_x**2 + all_y**2)
Not only is the latter easier to read, it’s hundreds of times faster than the for loop (and provides opportunities for hidden vectorization and parallelization). However, the Numpy abstraction stops at rectangular arrays of numbers or character strings. While it’s possible to put arbitrary Python data in a Numpy array, Numpy’s dtype=object is essentially a fixed-length list: data are not contiguous in memory and operations are not vectorized.
Awkward-array is a pure Python+Numpy library for manipulating complex data structures as you would Numpy arrays. Even if your data structures
- contain variable-length lists (jagged/ragged),
- are deeply nested (record structure),
- have different data types in the same list (heterogeneous),
- are masked, bit-masked, or index-mapped (nullable),
- contain cross-references or even cyclic references,
- need to be Python class instances on demand,
- are not defined at every point (sparse),
- are not contiguous in memory,
- should not be loaded into memory all at once (lazy),
this library can access them as columnar data structures, with the efficiency of Numpy arrays. They may be converted from JSON or Python data, loaded from “awkd” files, HDF5, Parquet, or ROOT files, or they may be views into memory buffers like Arrow.
Install awkward like any other Python package:
pip install awkward # maybe with sudo or --user, or in virtualenv pip install awkward-numba # optional: integration with and optimization by Numba
or install with conda:
conda config --add channels conda-forge # if you haven't added conda-forge already conda install awkward conda install awkward-numba # optional: integration with and optimization by Numba
Release history Release notifications
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|awkward-0.12.3-py2.py3-none-any.whl (86.0 kB) Copy SHA256 hash SHA256||Wheel||py2.py3|
|awkward-0.12.3.tar.gz (663.2 kB) Copy SHA256 hash SHA256||Source||None|