poisson(self, lam=1.0, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.poisson.
Some inconsistencies with the Dask version may exist.
The Poisson distribution is the limit of the binomial distribution for large N.
New code should use the poisson
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
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 :None:None:`lam` is within 10 sigma of the maximum representable value.
Expected number of events occurring in a fixed-time interval, must be >= 0. A sequence must be broadcastable over the requested size.
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.
Drawn samples from the parameterized Poisson distribution.
Draw samples from a Poisson distribution.
Generator.poisson
which should be used for new code.
Draw samples from the distribution:
This example is valid syntax, but we were not able to check execution>>> import numpy as np # doctest: +SKIP
... s = np.random.poisson(5, 10000) # doctest: +SKIP
Display histogram of the sample:
This example is valid syntax, but we were not able to check execution>>> import matplotlib.pyplot as plt # doctest: +SKIP
... count, bins, ignored = plt.hist(s, 14, density=True) # doctest: +SKIP
... plt.show() # doctest: +SKIP
Draw each 100 values for lambda 100 and 500:
This example is valid syntax, but we were not able to check execution>>> s = np.random.poisson(lam=(100., 500.), size=(100, 2)) # doctest: +SKIPSee :
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