Metadata-Version: 2.1
Name: awkward
Version: 2.4.5
Summary: Manipulate JSON-like data with NumPy-like idioms.
Project-URL: Bug Tracker, https://github.com/scikit-hep/awkward-1.0/issues
Project-URL: Chat, https://gitter.im/Scikit-HEP/awkward-array
Project-URL: Discussions, https://github.com/scikit-hep/awkward-1.0/discussions
Project-URL: Documentation, https://awkward-array.org
Project-URL: Homepage, https://github.com/scikit-hep/awkward-1.0
Project-URL: Releases, https://github.com/scikit-hep/awkward-1.0/releases
Project-URL: Source Code, https://github.com/scikit-hep/awkward-1.0
Author-email: Jim Pivarski <pivarski@princeton.edu>
License: BSD-3-Clause
License-File: LICENSE
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Requires-Python: >=3.8
Requires-Dist: awkward-cpp==24
Requires-Dist: importlib-metadata>=4.13.0; python_version < '3.12'
Requires-Dist: numpy>=1.18.0
Requires-Dist: packaging
Requires-Dist: typing-extensions>=4.1.0; python_version < '3.11'
Description-Content-Type: text/markdown

<a href="https://github.com/scikit-hep/awkward-1.0">
    <img src="https://github.com/scikit-hep/awkward-1.0/raw/main/docs-img/logo/logo-300px.png">
</a>


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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),

```python
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:

```python
output = np.square(array["y", ..., 1:])
```

The result is

```python
[
    [[], [4], [4, 9]],
    [],
    [[4, 9, 16], [4, 9, 16, 25]]
]
```

The equivalent using only Python is

```python
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](https://numba.pydata.org/)'s JIT-compiled functions.

See the [Getting started](https://awkward-array.org/doc/main/getting-started/index.html) documentation on [awkward-array.org](https://awkward-array.org) for an introduction, including a [no-install demo](https://awkward-array.org/doc/main/getting-started/try-awkward-array.html) you can try in your web browser.

# Getting help

   * View the documentation on [awkward-array.org](https://awkward-array.org/).
   * Report bugs, request features, and ask for additional documentation on [GitHub Issues](https://github.com/scikit-hep/awkward/issues).
   * If you have a "How do I...?" question, start a [GitHub Discussion](https://github.com/scikit-hep/awkward/discussions) with category "Q&A".
   * Alternatively, ask about it on [StackOverflow with the [awkward-array] tag](https://stackoverflow.com/questions/tagged/awkward-array). 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](https://gitter.im/Scikit-HEP/awkward-array) chat room.

# Installation

Awkward Array can be installed from [PyPI](https://pypi.org/project/awkward) using pip:

```bash
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](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge):

```bash
conda install -c conda-forge awkward
```

