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- Bit generators
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Bit generators...
- Array API
...lace it in the ndarray, arr, at the place pointed to by itemptr. Return -1 if an error occurs or 0 on success. Note In general, prefer the use of PyArray_Pack when handling arbitrary Python objects. Setitem is for example not able to hand...
- Array creation
...Array creation See also Array creation routines Introduction There are 6 general mechanisms for creating arrays: Conversion from other Python structures (i.e. lists and tuples) Intrinsic NumPy...
- Bit generators
...NumPy reference NumPy’s module structure Random sampling (numpy.random) Bit generators...
- Data type API
...d types enum NPY_TYPES There is a list of enumerated types defined providing the basic 25 data types plus some useful generic names. Whenever the code requires a type number, one of these enumerated types is requested. The types are all c...
- Data type classes (
numpy.dtypes) - Data type promotion in NumPy
- Legacy random generation
- Mersenne Twister (MT19937)
- NumPy 1.12.0 Release Notes
- NumPy 1.15.0 Release Notes
- NumPy 1.16.0 Release Notes
- NumPy 1.17.0 Release Notes
- NumPy 1.18.0 Release Notes
- NumPy 1.19.0 Release Notes
- NumPy 1.22.0 Release Notes
- NumPy 1.24 Release Notes
- NumPy 1.25.0 Release Notes
- NumPy 1.3.0 Release Notes
- NumPy 1.6.0 Release Notes
- NumPy 1.8.0 Release Notes
- NumPy 1.9.0 Release Notes
- NumPy 2.0.0 Release Notes
- NumPy for MATLAB users
- numpy.random.Generator.bit_generator
- numpy.random.Generator.spawn
- NumPy: the absolute basics for beginners
- Parallel random number generation
- Performance
- Permuted congruential generator (64-bit, PCG64 DXSM)
- Permuted congruential generator (64-bit, PCG64)
- Philox counter-based RNG
- Random
Generator - Random sampling (
numpy.random) - Releasing a version
- ufunc API
- Universal functions (
ufunc) - Using Python as glue
- Beyond the basics
- Building from source
- Compatibility policy
- CPU build options
- Data type objects (
dtype) - Data types
- Datetimes and timedeltas
- Development workflow
- Extending
- F2PY examples
- How to extend NumPy
- Indexing on
ndarrays - Internal organization of NumPy arrays
- Iterating over arrays
- NumPy 1.10.0 Release Notes
- NumPy 1.10.1 Release Notes
- NumPy 1.14.0 Release Notes
- NumPy 1.15.1 Release Notes
- NumPy 1.17.4 Release Notes
- NumPy 1.17.5 Release Notes
- NumPy 1.21.3 Release Notes
- NumPy 1.26.0 Release Notes
- NumPy C-API
- NumPy core math library
- NumPy quickstart
- NumPy reference
- numpy.binary_repr
- numpy.dtype
- numpy.i: a SWIG interface file for NumPy
- numpy.random.BitGenerator
- numpy.random.BitGenerator.cffi
- numpy.random.BitGenerator.ctypes
- numpy.random.BitGenerator.random_raw
- numpy.random.BitGenerator.seed_seq
- numpy.random.BitGenerator.spawn
- numpy.random.get_state
- numpy.random.MT19937.cffi
- numpy.random.MT19937.ctypes
- numpy.random.MT19937.jumped
- numpy.random.PCG64.advance
- numpy.random.PCG64.cffi
- numpy.random.PCG64.ctypes
- numpy.random.PCG64.jumped
- numpy.random.PCG64DXSM.advance
- numpy.random.PCG64DXSM.cffi
- numpy.random.PCG64DXSM.ctypes
- numpy.random.PCG64DXSM.jumped
- numpy.random.Philox.advance
- numpy.random.Philox.cffi
- numpy.random.Philox.ctypes
- numpy.random.Philox.jumped
- numpy.random.RandomState.get_state
- numpy.random.RandomState.set_state
- numpy.random.SeedSequence.spawn
- numpy.random.set_state
- numpy.random.SFC64.cffi
- numpy.random.SFC64.ctypes
- numpy.set_printoptions
- Python types and C-structures
- Reading and writing files
- Scalars
- Setting up and using your development environment
- SFC64 Small Fast Chaotic PRNG
- Status of
numpy.distutilsand migration advice - Structured arrays