![scipy stats norm scipy stats norm](https://docs.scipy.org/doc/scipy/_images/scipy-stats-multivariate_normal-1_01.png)
Parameters : array : Input array or object having the elements. This function tests the null hypothesis of the population that the sample was drawn from.
#SCIPY STATS NORM CODE#
This makes it easier to write code that takes a general distribution object as an argument. (array, axis0) function test whether the sample is different from the normal distribution. But once a distribution object is created, its PDF, for example, can be called with a single argument. For example, some distributions take more parameters than others and so their object constructors require more arguments. SciPy also provides constructors for objects representing random variables.Įxample: x = (3, 1) x.cdf(2.7) returns the same value as (2.7, 3, 1).Ĭonstructing objects representing random variables encapsulates the differences between distributions in the constructors. Writing (x, scale=7) would have given the expected result because the default location value is 0. Instead, the location was set to 7 and the scale was left at its default value 1. I assumed there would be no location parameter and that the second argument, 7, would be the mean (scale).
#SCIPY STATS NORM PDF#
This means, for example, that (x, 7) evaluates at x the PDF of an exponential distribution with location 7. Somewhat surprisingly, the exponential distribution has a location parameter.
![scipy stats norm scipy stats norm](http://www.gentosha-academy.com/wp-content/uploads/2019/10/3-2.png)
This means, for example, that the exponential distribution is parameterized in terms of its mean, not its rate. Most distributions are parameterized in terms of location and scale. The command line help() facility does not document the distribution parameterizations, but the external documentation does. The function call (2, 3, size = 10) returns an array of 10 samples from the same distribution. The size is set to 1 by default.Įxample: (2, 3) generates a random sample from a normal (Gaussian) random variable with mean 2 and standard deviation 3. Random values are generated using rvs which takes an optional size argument. (Discrete distributions use pmf rather than pdf.) One surprise here is that the inverse CDF function is called ppf for “percentage point function.” I’d never heard that terminology and would have expected something like “quantile.”Įxample: (0.1, 2, 3) evaluates the CDF of a beta(2, 3) random variable at 0.1. The density and cumulative distribution functions are pdf and cdf respectively. Other distribution names are less obvious: expon for the exponential, chi2 for chi-squared distribution, etc.Įach distribution supports several functions. The only possible surprise is that all distributions begin with a lower-case letter, even those corresponding to a proper name (e.g. Some distributions have obvious names: gamma, cauchy, t, f, etc. There are 81 supported continuous distribution families and 12 discrete distribution families.
#SCIPY STATS NORM HOW TO#
Here are some notes on how to work with probability distributions using the SciPy numerical library for Python.įunctions related to probability distributions are located in scipy.stats.