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What’s New or Different

Warning

The Box-Muller method used to produce NumPy’s normals is no longer available in Generator. It is not possible to reproduce the exact random values using Generator for the normal distribution or any other distribution that relies on the normal such as the gamma or standard_t. If you require bitwise backward compatible streams, use RandomState.

Quick comparison of legacy mtrand to the new Generator

Feature Older Equivalent Notes
Generator RandomState Generator requires a stream source, called a BitGenerator A number of these are provided. RandomState uses the Mersenne Twister MT19937 by default, but can also be instantiated with any BitGenerator.
random random_sample, rand

Access the values in a BitGenerator, convert them to float64 in the interval [0.0., `` 1.0)``. In addition to the size kwarg, now supports dtype='d' or dtype='f', and an out kwarg to fill a user- supplied array.

Many other distributions are also supported.
integers randint, random_integers Use the endpoint kwarg to adjust the inclusion or exclution of the high interval endpoint

And in more detail:

  • random_entropy provides access to the system source of randomness that is used in cryptographic applications (e.g., /dev/urandom on Unix).
  • Simulate from the complex normal distribution (complex_normal)
  • The normal, exponential and gamma generators use 256-step Ziggurat methods which are 2-10 times faster than NumPy’s default implementation in standard_normal, standard_exponential or standard_gamma.
  • integers is now the canonical way to generate integer random numbers from a discrete uniform distribution. The rand and randn methods are only available through the legacy RandomState. This replaces both randint and the deprecated random_integers.
  • The Box-Muller method used to produce NumPy’s normals is no longer available.
  • All bit generators can produce doubles, uint64s and uint32s via CTypes (ctypes) and CFFI (cffi). This allows these bit generators to be used in numba.
  • The bit generators can be used in downstream projects via Cython.
In [1]: from  numpy.random import Generator, PCG64

In [2]: import numpy.random

In [3]: rg = Generator(PCG64())

In [4]: %timeit rg.standard_normal(100000)
   ...: %timeit numpy.random.standard_normal(100000)
   ...: 
792 us +- 4.07 us per loop (mean +- std. dev. of 7 runs, 1000 loops each)
1.87 ms +- 20.3 us per loop (mean +- std. dev. of 7 runs, 1000 loops each)

In [5]: %timeit rg.standard_exponential(100000)
   ...: %timeit numpy.random.standard_exponential(100000)
   ...: 
372 us +- 2.73 us per loop (mean +- std. dev. of 7 runs, 1000 loops each)
1.35 ms +- 7.16 us per loop (mean +- std. dev. of 7 runs, 1000 loops each)

In [6]: %timeit rg.standard_gamma(3.0, 100000)
   ...: %timeit numpy.random.standard_gamma(3.0, 100000)
   ...: 
1.84 ms +- 6.24 us per loop (mean +- std. dev. of 7 runs, 1000 loops each)
3.92 ms +- 39.3 us per loop (mean +- std. dev. of 7 runs, 100 loops each)

In [7]: rg = Generator(PCG64(0))

In [8]: rg.random(3, dtype='d')
Out[8]: array([0.63696169, 0.26978671, 0.04097352])

In [9]: rg.random(3, dtype='f')

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https://docs.scipy.org/doc/numpy-1.17.0/reference/random/new-or-different.html