`scipy.stats._stats_py:describe`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_stats_py.py:1454

Signature

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

Summary

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.

Examples

import numpy as np
from scipy import stats
a = np.arange(10)

✓

stats.describe(a)

✗

DescribeResult(nobs=np.int64(10), minmax=(np.int64(0), np.int64(9)), mean=np.float64(4.5), variance=np.float64(9.166666666666668), skewness=np.float64(0.0), kurtosis=np.float64(-1.2242424242424244))

b = [[1, 2], [3, 4]]

✓

stats.describe(b)

✗

DescribeResult(nobs=np.int64(2), minmax=(array([1, 2]), array([3, 4])), mean=array([2., 3.]), variance=array([2., 2.]), skewness=array([0., 0.]), kurtosis=array([-2., -2.]))

Aliases

scipy.stats.describe