# numpy.random.poisson

random.poisson(lam=1.0, size=None)

  Draw samples from a Poisson distribution.

  The Poisson distribution is the limit of the binomial distribution for large N.

  Note

  New code should use the `poisson` method of a `default_rng()` instance instead; please see the Quick Start.

  Parameters
    lam float or array_like of floats

      Expected number of events occurring in a fixed-time interval, must be >= 0. A sequence must be broadcastable over the requested size.

    size int or tuple of ints, optional

      Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k` samples are drawn. If size is `None` (default), a single value is returned if `lam` is a scalar. Otherwise, `np.array(lam).size` samples are drawn.

  Returns
    out ndarray or scalar

      Drawn samples from the parameterized Poisson distribution.

  See also

  `Generator.poisson`

    which should be used for new code.

  Notes

  The Poisson distribution

  \[f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}\]

  For events with an expected separation \(\lambda\) the Poisson distribution \(f(k; \lambda)\) describes the probability of \(k\) events occurring within the observed interval \(\lambda\).

  Because the output is limited to the range of the C int64 type, a ValueError is raised when lam is within 10 sigma of the maximum representable value.

  References

  1

    Weisstein, Eric W. “Poisson Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html

  2

    Wikipedia, “Poisson distribution”, https://en.wikipedia.org/wiki/Poisson_distribution

  Examples

  Draw samples from the distribution:

      >>> import numpy as np
      >>> s = np.random.poisson(5, 10000)

  Display histogram of the sample:

      >>> import matplotlib.pyplot as plt
      >>> count, bins, ignored = plt.hist(s, 14, density=True)
      >>> plt.show()

  Draw each 100 values for lambda 100 and 500:

      >>> s = np.random.poisson(lam=(100., 500.), size=(100, 2))

previous

numpy.random.permutation

next

numpy.random.power