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# Count the non-masked elements of the masked array along the given axis in Numpy

To count the non-masked elements of the masked array along the given axis, use the **ma.MaskedArray.count()** method in Python Numpy. The axis is set using the "**axis**" parameter. . The method returns 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.

The axis parameter is the axis or axes along which the count is performed. The default, 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.

The keepdims parameter, if 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.

## Steps

At first, import the required library −

import numpy as np import numpy.ma as ma

Create an array with int elements using the numpy.array() method −

arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]]) print("Array...\n", arr) print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...\n",arr.ndim)

Create a masked array and mask some of them as invalid −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]]) print("\nOur Masked Array\n", maskArr) print("\nOur Masked Array type...\n", maskArr.dtype)

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...\n",maskArr.ndim)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...\n",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...\n",maskArr.size)

To count the non-masked elements of the masked array along the given axis, use the ma.MaskedArray.count() method. The axis is set using the "axis" parameter −

print("\nResult...\n",maskArr.count(axis = 1))

## Example

# Python ma.MaskedArray - Count the non-masked elements of the masked array along the given axis import numpy as np import numpy.ma as ma # Create an array with int elements using the numpy.array() method arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]]) print("Array...\n", arr) print("\nArray type...\n", arr.dtype) # Get the dimensions of the Array print("\nArray Dimensions...\n",arr.ndim) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]]) print("\nOur Masked Array\n", maskArr) print("\nOur Masked Array type...\n", maskArr.dtype) # Get the dimensions of the Masked Array print("\nOur Masked Array Dimensions...\n",maskArr.ndim) # Get the shape of the Masked Array print("\nOur Masked Array Shape...\n",maskArr.shape) # Get the number of elements of the Masked Array print("\nElements in the Masked Array...\n",maskArr.size) # To count the non-masked elements of the masked array along the given axis, use the ma.MaskedArray.count() method # The axis is set using the "axis" parameter print("\nResult...\n",maskArr.count(axis = 1))

## Output

Array... [[65 68 81] [93 33 39] [73 88 51] [62 45 67]] Array type... int64 Array Dimensions... 2 Our Masked Array [[-- -- 81] [-- 33 39] [73 -- 51] [62 -- 67]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 3) Elements in the Masked Array... 12 Result... [1 2 2 2]

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