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NumPy v2.2 Manual - Home NumPy v2.2 Manual - Home
  • User Guide
  • API reference
  • Building from source
  • Development
  • Release notes
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    • NEPs
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  • User Guide
  • API reference
  • Building from source
  • Development
  • Release notes
  • Learn
  • NEPs
  • GitHub

Section Navigation

  • NumPy’s module structure
  • Array objects
  • Universal functions (ufunc)
  • Routines and objects by topic
    • Constants
    • Array creation routines
    • Array manipulation routines
    • Bit-wise operations
    • String functionality
    • Datetime support functions
    • Data type routines
    • Mathematical functions with automatic domain
    • Floating point error handling
    • Exceptions and Warnings (numpy.exceptions)
    • Discrete Fourier Transform (numpy.fft)
    • Functional programming
    • Input and output
    • Indexing routines
    • Linear algebra (numpy.linalg)
    • Logic functions
    • Masked array operations
    • Mathematical functions
    • Miscellaneous routines
    • Polynomials
    • Random sampling (numpy.random)
      • Random Generator
        • numpy.random.Generator.bit_generator
        • numpy.random.Generator.spawn
        • numpy.random.Generator.integers
        • numpy.random.Generator.random
        • numpy.random.Generator.choice
        • numpy.random.Generator.bytes
        • numpy.random.Generator.shuffle
        • numpy.random.Generator.permutation
        • numpy.random.Generator.permuted
        • numpy.random.Generator.beta
        • numpy.random.Generator.binomial
        • numpy.random.Generator.chisquare
        • numpy.random.Generator.dirichlet
        • numpy.random.Generator.exponential
        • numpy.random.Generator.f
        • numpy.random.Generator.gamma
        • numpy.random.Generator.geometric
        • numpy.random.Generator.gumbel
        • numpy.random.Generator.hypergeometric
        • numpy.random.Generator.laplace
        • numpy.random.Generator.logistic
        • numpy.random.Generator.lognormal
        • numpy.random.Generator.logseries
        • numpy.random.Generator.multinomial
        • numpy.random.Generator.multivariate_hypergeometric
        • numpy.random.Generator.multivariate_normal
        • numpy.random.Generator.negative_binomial
        • numpy.random.Generator.noncentral_chisquare
        • numpy.random.Generator.noncentral_f
        • numpy.random.Generator.normal
        • numpy.random.Generator.pareto
        • numpy.random.Generator.poisson
        • numpy.random.Generator.power
        • numpy.random.Generator.rayleigh
        • numpy.random.Generator.standard_cauchy
        • numpy.random.Generator.standard_exponential
        • numpy.random.Generator.standard_gamma
        • numpy.random.Generator.standard_normal
        • numpy.random.Generator.standard_t
        • numpy.random.Generator.triangular
        • numpy.random.Generator.uniform
        • numpy.random.Generator.vonmises
        • numpy.random.Generator.wald
        • numpy.random.Generator.weibull
        • numpy.random.Generator.zipf
      • Legacy Generator (RandomState)
      • Bit generators
      • Upgrading PCG64 with PCG64DXSM
      • Compatibility policy
      • Parallel Applications
      • Multithreaded Generation
      • What’s new or different
      • Comparing Performance
      • C API for random
      • Examples of using Numba, Cython, CFFI
    • Set routines
    • Sorting, searching, and counting
    • Statistics
    • Test support (numpy.testing)
    • Window functions
  • Typing (numpy.typing)
  • Packaging (numpy.distutils)
  • NumPy C-API
  • Array API standard compatibility
  • CPU/SIMD optimizations
  • Thread Safety