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# Compute the minimum of the masked array elements over axis 0 in Numpy

To compute the minimum of the masked array elements along a given axis, use the **MaskedArray.min()** method in Python Numpy −

- The axis is set using the "axis" parameter
- The axis is the axis along which to operate

The function min() returns a new array holding the result. If out was specified, out is returned. The out parameter is alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. The fill_value is a value used to fill in the masked values. If None, use the output of minimum_fill_value(). The keepdims, if 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, 76], [73, 88, 51], [62, 45, 67]]) print("Array...\n", arr)

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

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

Get the type of the masked array −

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("\nNumber of elements in the Masked Array...\n",maskArr.size)

To compute the minimum of the masked array elements along a given axis, use the MaskedArray.min() method. The axis is set using the "axis" parameter. The axis is the axis along which to operate −

resArr = maskArr.min(axis = 0) print("\nResultant Array..\n.", resArr)

## Example

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, 76], [73, 88, 51], [62, 45, 67]]) print("Array...\n", arr) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 0, 0, 0], [0, 1, 0], [0, 1, 0]]) print("\nOur Masked Array...\n", maskArr) # Get the type of the masked array 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("\nNumber of elements in the Masked Array...\n",maskArr.size) # To compute the minimum of the masked array elements along a given axis, use the MaskedArray.min() method in Python Numpy # The axis is set using the "axis" parameter # The axis is the axis along which to operate resArr = maskArr.min(axis = 0) print("\nResultant Array..\n.", resArr)

## Output

Array... [[65 68 81] [93 33 76] [73 88 51] [62 45 67]] Our Masked Array... [[-- -- 81] [93 33 76] [73 -- 51] [62 -- 67]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 3) Number of elements in the Masked Array... 12 Resultant Array.. . [62 33 51]

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