binomial(self, n, p, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.binomial.
Some inconsistencies with the Dask version may exist.
Samples are drawn from a binomial distribution with specified parameters, n trials and p probability of success where n an integer >= 0 and p is in the interval [0,1]. (n may be input as a float, but it is truncated to an integer in use)
New code should use the binomial
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
The probability density for the binomial distribution is
$$P(N) = \binom{n}{N}p^N(1-p)^{n-N},$$where $n$ is the number of trials, $p$ is the probability of success, and $N$ is the number of successes.
When estimating the standard error of a proportion in a population by using a random sample, the normal distribution works well unless the product p*n <=5, where p = population proportion estimate, and n = number of samples, in which case the binomial distribution is used instead. For example, a sample of 15 people shows 4 who are left handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4, so the binomial distribution should be used in this case.
Parameter of the distribution, >= 0. Floats are also accepted, but they will be truncated to integers.
Parameter of the distribution, >= 0 and <=1.
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 n
and p
are both scalars. Otherwise, np.broadcast(n, p).size
samples are drawn.
Drawn samples from the parameterized binomial distribution, where each sample is equal to the number of successes over the n trials.
Draw samples from a binomial distribution.
Generator.binomial
which should be used for new code.
scipy.stats.binom
probability density function, distribution or cumulative density function, etc.
Draw samples from the distribution:
This example is valid syntax, but we were not able to check execution>>> n, p = 10, .5 # number of trials, probability of each trial # doctest: +SKIP
... s = np.random.binomial(n, p, 1000) # doctest: +SKIP # result of flipping a coin 10 times, tested 1000 times.
A real world example. A company drills 9 wild-cat oil exploration wells, each with an estimated probability of success of 0.1. All nine wells fail. What is the probability of that happening?
Let's do 20,000 trials of the model, and count the number that generate zero positive results.
This example is valid syntax, but we were not able to check execution>>> sum(np.random.binomial(9, 0.1, 20000) == 0)/20000. # doctest: +SKIP # answer = 0.38885, or 38%.See :
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