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# numpy.random.RandomState.poisson

`RandomState.``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.

  Parameters: | lam : float or array_like of floats Expectation of interval, should be >= 0. A sequence of expectation intervals 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.

  Notes

  The Poisson distribution

  For events with an expected separation the Poisson distribution describes the probability of events occurring within the observed interval .

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

  References

  [R189] | Weisstein, Eric W. “Poisson Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html

  [R190] | Wikipedia, “Poisson distribution”, http://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, normed=True)
      >>> plt.show()

  (Source code, png, pdf)

  Draw each 100 values for lambda 100 and 500:

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

  ()