scipy.stats.describe#
- scipy.stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')[source]#
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, 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.
- meanndarray or float
Arithmetic mean of a along the given axis.
- variancendarray or float
Unbiased variance of a along the given axis; denominator is number of observations minus one.
- skewnessndarray 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.
- kurtosisndarray 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.
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.]))