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Module « scipy.stats »

Fonction describe - module scipy.stats

Signature de la fonction describe

def describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate') 

Description

help(scipy.stats.describe)

Compute several descriptive statistics of the passed array.

Parameters
----------
a : array_like
    Input data.
axis : int or None, optional
    Axis along which statistics are calculated. Default is 0.
    If None, compute over the whole array `a`.
ddof : int, optional
    Delta degrees of freedom (only for variance).  Default is 1.
bias : bool, optional
    If False, then the skewness and kurtosis calculations are corrected
    for statistical bias.
nan_policy : {'propagate', 'raise', 'omit'}, optional
    Defines how to handle when input contains nan.
    The following options are available (default is 'propagate'):

    * 'propagate': returns nan
    * 'raise': throws an error
    * 'omit': performs the calculations ignoring nan values

Returns
-------
nobs : int or ndarray of ints
    Number of observations (length of data along `axis`).
    When 'omit' is chosen as nan_policy, the length along each axis
    slice is counted separately.
minmax: tuple of ndarrays or floats
    Minimum and maximum value of `a` along the given axis.
mean : ndarray or float
    Arithmetic mean of `a` along the given axis.
variance : ndarray or float
    Unbiased variance of `a` along the given axis; denominator is number
    of observations minus one.
skewness : ndarray or float
    Skewness of `a` along the given axis, based on moment calculations
    with denominator equal to the number of observations, i.e. no degrees
    of freedom correction.
kurtosis : ndarray or float
    Kurtosis (Fisher) of `a` along the given axis.  The kurtosis is
    normalized so that it is zero for the normal distribution.  No
    degrees of freedom are used.

Raises
------
ValueError
    If size of `a` is 0.

See Also
--------
skew, kurtosis

Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> a = np.arange(10)
>>> stats.describe(a)
DescribeResult(nobs=10, minmax=(0, 9), mean=4.5,
               variance=9.166666666666666, skewness=0.0,
               kurtosis=-1.2242424242424244)
>>> b = [[1, 2], [3, 4]]
>>> stats.describe(b)
DescribeResult(nobs=2, minmax=(array([1, 2]), array([3, 4])),
               mean=array([2., 3.]), variance=array([2., 2.]),
               skewness=array([0., 0.]), kurtosis=array([-2., -2.]))

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