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  • 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)
    • Layout (faster)
    • Unflattening and grouping
    • Arrays of records
    • Arrays of strings
    • Arrays of categories
    • Lazy arrays
    • Partitioned arrays
  • Examining arrays
    • Data type
    • Single item detail
    • Listing fields/keys/columns
    • Simple slicing
    • Checking validity
  • Numerical math
    • NumPy functions
    • Awkward broadcasting
    • Reducing (sum/min/any/all)
    • Statistics (mean/var/std)
    • Using argmin/argmax
    • On GPUs
  • Filtering data
    • By number of items
    • Cuts vs. masks
    • Slicing lists within arrays
    • Slices with missing values
  • Restructuring data
    • Zip/unzip and project
    • Adding fields to records
    • Renaming records
    • Flattening for plots
    • Padding/clipping for machine learning
    • Concatenating and interleaving
    • Sorting
  • Combinatorics
    • Cartesian product and "n choose k"
    • Best match between collections
  • Using arrays in Numba
    • Supported features
    • Building array output
  • Specialized behavior
    • Subclassing Array/Record
    • Overriding NumPy functions
    • In Numba
    • For physics: Lorentz vectors

Numerical mathΒΆ

  • NumPy functions

  • Awkward broadcasting

  • Reducing (sum/min/any/all)

  • Statistics (mean/var/std)

  • Using argmin/argmax

  • On GPUs

How to ensure that an array is valid How to perform computations with NumPy

By Jim Pivarski
© Copyright 2020 Jim Pivarski.