scipy tutorial - Baustatik-Info-Server

Examples
An array containing the calculated quantiles.
SciPy Reference Guide, Release 0.8.dev
>>> from scipy.stats.mstats import mquantiles
>>> a = np.array([6., 47., 49., 15., 42., 41., 7., 39., 43., 40., 36.])
>>> mquantiles(a)
array([ 19.2, 40. , 42.8])
Using a 2D array, specifying axis and limit.
>>> data = np.array([[ 6., 7., 1.],
[ 47., 15., 2.],
[ 49., 36., 3.],
[ 15., 39., 4.],
[ 42., 40., -999.],
[ 41., 41., -999.],
[ 7., -999., -999.],
[ 39., -999., -999.],
[ 43., -999., -999.],
[ 40., -999., -999.],
[ 36., -999., -999.]])
>>> mquantiles(data, axis=0, limit=(0, 50))
array([[ 19.2 , 14.6 , 1.45],
[ 40. , 37.5 , 2.5 ],
[ 42.8 , 40.05, 3.55]])
>>> data[:, 2] = -999.
>>> mquantiles(data, axis=0, limit=(0, 50))
masked_array(data =
[[19.2 14.6 --]
[40.0 37.5 --]
[42.8 40.05 --]],
mask =
[[False False True]
[False False True]
[False False True]],
fill_value = 1e+20)
msign(x)
Returns the sign of x, or 0 if x is masked.
normaltest(a, axis=0)
Tests whether a sample differs from a normal distribution
This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D’Agostino
and Pearson’s [R28], [R29] test that combines skew and kurtosis to produce an omnibus test of normality.
Parameters
a : array
axis : int or None
Returns
p-value : float
A 2-sided chi squared probability for the hypothesis test
3.18. Statistical functions (scipy.stats) 681