# Scipy Python 1. SciPy (pronounced / ˈsaɪpaɪ / 'sigh pie') is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
2. The SciPy library of Python is built to work with NumPy arrays and provides many user-friendly and efficient numerical practices such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install and are free of charge.

A Poisson discrete random variable.

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As an instance of the `rv_discrete` class, `poisson` object inherits from ita collection of generic methods (see below for the full list),and completes them with details specific for this particular distribution.

Notes

The probability mass function for `poisson` is: for (k ge 0).

`poisson` takes (mu) as shape parameter.When mu = 0 then at quantile k = 0, `pmf` methodreturns 1.0.

The probability mass function above is defined in the “standardized” form.To shift distribution use the `loc` parameter.Specifically, `poisson.pmf(k,mu,loc)` is identicallyequivalent to `poisson.pmf(k-loc,mu)`.

Examples

Calculate a few first moments:

Display the probability mass function (`pmf`):

Alternatively, the distribution object can be called (as a function)to fix the shape and location. This returns a “frozen” RV object holdingthe given parameters fixed.

Freeze the distribution and display the frozen `pmf`:

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Check accuracy of `cdf` and `ppf`:

Generate random numbers:

Methods

 rvs(mu, loc=0, size=1, random_state=None) Random variates. pmf(k, mu, loc=0) Probability mass function. logpmf(k, mu, loc=0) Log of the probability mass function. cdf(k, mu, loc=0) Cumulative distribution function. logcdf(k, mu, loc=0) Log of the cumulative distribution function. sf(k, mu, loc=0) Survival function (also defined as `1-cdf`, but sf is sometimes more accurate). logsf(k, mu, loc=0) Log of the survival function. ppf(q, mu, loc=0) Percent point function (inverse of `cdf` — percentiles). isf(q, mu, loc=0) Inverse survival function (inverse of `sf`). stats(mu, loc=0, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(mu, loc=0) (Differential) entropy of the RV. expect(func, args=(mu,), loc=0, lb=None, ub=None, conditional=False) Expected value of a function (of one argument) with respect to the distribution. median(mu, loc=0) Median of the distribution. mean(mu, loc=0) Mean of the distribution. var(mu, loc=0) Variance of the distribution. std(mu, loc=0) Standard deviation of the distribution. interval(alpha, mu, loc=0) Endpoints of the range that contains alpha percent of the distribution

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Based on how often these packages appear together in public `requirements.txt` files on GitHub.

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