Quickstart
What is an Awkward Array?
Converting arrays
NumPy
Python objects
JSON
ROOT via Uproot
Arrow and Parquet
Pandas
Generic buffers
Creating new arrays
ArrayBuilder (easy & general)
LayoutBuilder (medium) [todo]
Direct constructors (faster) [todo]
Unflattening and grouping [todo]
Arrays of records [todo]
Arrays of strings [todo]
Arrays of categories [todo]
Lazy arrays [todo]
Partitioned arrays [todo]
Examining arrays
Data type [todo]
Single item detail [todo]
Listing fields/keys/columns [todo]
Simple slicing [todo]
Checking validity [todo]
Numerical math
NumPy functions [todo]
Awkward broadcasting [todo]
Reducing (sum/min/any/all) [todo]
Statistics (mean/var/std) [todo]
Using argmin/argmax [todo]
On GPUs [todo]
Filtering data
By number of items [todo]
Cuts vs. masks [todo]
Slicing lists within arrays [todo]
Slices with missing values [todo]
Restructuring data
Zip/unzip and project [todo]
Adding fields to records [todo]
Renaming records [todo]
Flattening for plots [todo]
Padding/clipping for machine learning [todo]
Concatenating and interleaving [todo]
Sorting [todo]
Combinatorics
Cartesian and "n choose k" [todo]
Best match between collections [todo]
Using arrays in Numba
Supported features [todo]
Building array output [todo]
Specialized behavior
Subclassing Array/Record [todo]
Overriding NumPy functions [todo]
In Numba [todo]
For physics: Lorentz vectors [todo]
Differentiation using JAX
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Using arrays in Numba
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Supported features
Building array output
How to find the best match between two collections using Cartesian (cross) product
Awkward Array features that are supported in Numba-compiled functions
