# scipy.stats.mode

scipy.stats.mode(a, axis=0, nan_policy='propagate')

  Return an array of the modal (most common) value in the passed array.

  If there is more than one such value, only the smallest is returned. The bin-count for the modal bins is also returned.

  Parameters
    a array_like

      n-dimensional array of which to find mode(s).

    axis int or None, optional

      Axis along which to operate. Default is 0. If None, compute over the whole array a.

    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
    mode ndarray

      Array of modal values.

    count ndarray

      Array of counts for each mode.

  Examples

      >>> a = np.array([[6, 8, 3, 0],
      ...               [3, 2, 1, 7],
      ...               [8, 1, 8, 4],
      ...               [5, 3, 0, 5],
      ...               [4, 7, 5, 9]])
      >>> from scipy import stats
      >>> stats.mode(a)
      ModeResult(mode=array([[3, 1, 0, 0]]), count=array([[1, 1, 1, 1]]))

  To get mode of whole array, specify `axis=None`:

      >>> stats.mode(a, axis=None)
      ModeResult(mode=array([3]), count=array([3]))

scipy.stats.kurtosis scipy.stats.moment