scipy.stats.describe

scipy.stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')

Compute several descriptive statistics of the passed array.

Parameters

aarray_like

Input data.

axisint or None, optional

Axis along which statistics are calculated. Default is 0. If None, compute over the whole array a.

ddofint, optional

Delta degrees of freedom (only for variance). Default is 1.

biasbool, 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

nobsint or ndarray of ints

Number of observations (length of data along axis). When ‘omit’ is chosen as nan_policy, each column is counted separately.

minmax: tuple of ndarrays or floats

Minimum and maximum value of data array.

meanndarray or float

Arithmetic mean of data along axis.

variancendarray or float

Unbiased variance of the data along axis, denominator is number of observations minus one.

skewnessndarray or float

Skewness, based on moment calculations with denominator equal to the number of observations, i.e. no degrees of freedom correction.

kurtosisndarray or float

Kurtosis (Fisher). The kurtosis is normalized so that it is zero for the normal distribution. No degrees of freedom are used.

See also

skew, kurtosis

Examples

>>> 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.]))