SciPy, axis=None, keepdims=<class numpy._globals._NoValue at 0x40b6a26c>) = < instance at 0x52d13d4c>

Count the non-masked elements of the array along the given axis.


axis : None or int or tuple of ints, optional

Axis or axes along which the count is performed. The default (axis = None) performs the count over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis.

New in version 1.10.0.

If this is a tuple of ints, the count is performed on multiple axes, instead of a single axis or all the axes as before.

keepdims : bool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array.


result : ndarray or scalar

An array with the same shape as the input array, with the specified axis removed. If the array is a 0-d array, or if axis is None, a scalar is returned.

See also

Count masked elements in array or along a given axis.


>>> import as ma
>>> a = ma.arange(6).reshape((2, 3))
>>> a[1, :] = ma.masked
>>> a
masked_array(data =
 [[0 1 2]
 [-- -- --]],
             mask =
 [[False False False]
 [ True  True  True]],
       fill_value = 999999)
>>> a.count()

When the axis keyword is specified an array of appropriate size is returned.

>>> a.count(axis=0)
array([1, 1, 1])
>>> a.count(axis=1)
array([3, 0])

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