numpy.ma.count_masked#

ma.count_masked(arr, axis=None)[source]#

Count the number of masked elements along the given axis.

Parameters:

arrarray_like: An array with (possibly) masked elements.
axisint, optional: Axis along which to count. If None (default), a flattened version of the array is used.

Returns:

countint, ndarray: The total number of masked elements (axis=None) or the number of masked elements along each slice of the given axis.

See also

MaskedArray.count: Count non-masked elements.

Examples

>>> import numpy as np
>>> a = np.arange(9).reshape((3,3))
>>> a = np.ma.array(a)
>>> a[1, 0] = np.ma.masked
>>> a[1, 2] = np.ma.masked
>>> a[2, 1] = np.ma.masked
>>> a
masked_array(
  data=[[0, 1, 2],
        [--, 4, --],
        [6, --, 8]],
  mask=[[False, False, False],
        [ True, False,  True],
        [False,  True, False]],
  fill_value=999999)
>>> np.ma.count_masked(a)
3

When the axis keyword is used an array is returned.

>>> np.ma.count_masked(a, axis=0)
array([1, 1, 1])
>>> np.ma.count_masked(a, axis=1)
array([0, 2, 1])