scipy.stats.mode — SciPy v0.19.1 Reference Guide (original) (raw)

scipy.stats. mode(a, axis=0, nan_policy='propagate')[source]¶

Returns 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. ‘propagate’ returns nan, ‘raise’ throws an error, ‘omit’ performs the calculations ignoring nan values. Default is ‘propagate’.
Returns:	mode : ndarray Array of modal values. count : ndarray Array of counts for each mode.

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. ‘propagate’ returns nan, ‘raise’ throws an error, ‘omit’ performs the calculations ignoring nan values. Default is ‘propagate’.

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) (array([[3, 1, 0, 0]]), array([[1, 1, 1, 1]]))

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

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