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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, 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.

  Raises:
    ValueError

      If size of a is 0.

  See also

  `skew`, `kurtosis`

  Notes

  `describe` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ✅
  PyTorch | ✅ | ✅
  JAX | ⚠️ no JIT | ⚠️ no JIT
  Dask | ⚠️ computes graph | n/a

  See Support for the array API standard for more information.

  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.]))