Suppress only rows that contain masked values using compress_rowcols() along specific axis in Numpy

To suppress only rows that contain masked values along specific axis, use the np.ma.mask_compress_rowcols() method in Numpy. The suppression behavior is selected with the axis parameter −

• If axis is None, both rows and columns are suppressed.
• If axis is 0, only rows are suppressed.
• If axis is 1 or -1, only columns are suppressed

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...<br>", arr)
print("\nArray type...<br>", arr.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...<br>",arr.ndim)

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<br>", maskArr)
print("\nOur Masked Array type...<br>", maskArr.dtype)

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...<br>",maskArr.ndim)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...<br>",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...<br>",maskArr.size)

To suppress only rows of a 2-D array that contain masked values along specific axis, use the np.ma.mask_compress_rowcols() method. The suppression behavior is selected with the axis parameter. If axis is None, both rows and columns are suppressed. If axis is 0, only rows are suppressed. If axis is 1 or -1, only columns are suppressed −

print("\nResult...<br>",np.ma.compress_rowcols(maskArr, axis = 0))

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...<br>", arr)
print("\nArray type...<br>", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...<br>",arr.ndim)

# 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<br>", maskArr)
print("\nOur Masked Array type...<br>", maskArr.dtype)

# Get the dimensions of the Masked Array
print("\nOur Masked Array Dimensions...<br>",maskArr.ndim)

# Get the shape of the Masked Array
print("\nOur Masked Array Shape...<br>",maskArr.shape)

# Get the number of elements of the Masked Array
print("\nElements in the Masked Array...<br>",maskArr.size)

# To suppress only rows of a 2-D array that contain masked values alomg specific axis, use the np.ma.mask_compress_rowcols() method in Numpy

# The suppression behavior is selected with the axis parameter
# If axis is None, both rows and columns are suppressed.
# If axis is 0, only rows are suppressed.
# If axis is 1 or -1, only columns are suppressed
print("\nResult...<br>",np.ma.compress_rowcols(maskArr, axis = 0))

Output

Array...
[[65 68 81]
[93 33 76]
[73 88 51]
[62 45 67]]

Array type...
int64

Array Dimensions...
2

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)

Elements in the Masked Array...
12

Result...
[[93 33 76]]