Basic of Statistic and Machine Learning used for Analyze Data (2)

Non-Parametric Test:

Non-parametric covers techniques that do not rely on data belonging to any particular distribution.
Non-parametric covers techniques that do not assume that the structure of a model is fixed.

Mann-Whitney U Test:

Is a non parametric test of the null hypothesis that two populations are the same against an alternative hypothesis, especially that a particular population tends to have larger values than the other.

scipy.stats.mannwhitneyu

scipy.stats.mannwhitneyu(x, y, use_continuity=True)]

Computes the Mann-Whitney rank test on samples x and y.

Parameters:	x, y : array_like Array of samples, should be one-dimensional. use_continuity : bool, optional Whether a continuity correction (1/2.) should be taken into account. Default is True.
Returns:	u : float The Mann-Whitney statistics. prob : float One-sided p-value assuming a asymptotic normal distribution.

If you need test the data is Normal distribution or not you can use:

scipy.stats.shapiro

scipy.stats.shapiro(x, a=None, reta=False)

Perform the Shapiro-Wilk test for normality.

The Shapiro-Wilk test tests the null hypothesis that the data was drawn from a normal distribution.

Parameters:	x : array_like Array of sample data. a : array_like, optional Array of internal parameters used in the calculation. If these are not given, they will be computed internally. If x has length n, then a must have length n/2. reta : bool, optional Whether or not to return the internally computed a values. The default is False.
Returns:	W : float The test statistic. p-value : float The p-value for the hypothesis test. a : array_like, optional If reta is True, then these are the internally computed “a” values that may be passed into this function on future calls.