# Suppress the rows and/or columns of a 2- D array that contain masked values in Numpy

NumpyServer Side ProgrammingProgramming

#### Python Data Science basics with Numpy, Pandas and Matplotlib

Most Popular

63 Lectures 6 hours

#### Data Analysis using NumPy and Pandas

19 Lectures 8 hours

#### Numpy with Python

Most Popular

12 Lectures 3 hours

To suppress the rows and/or columns of a 2-D array that contain masked values, 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.

A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.

## 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...", arr)
print("Array type...", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...",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("Our Masked Array type...", maskArr.dtype)

Get the dimensions of the Masked Array −

print("Our Masked Array Dimensions...",maskArr.ndim)


Get the shape of the Masked Array −

print("Our Masked Array Shape...",maskArr.shape)

Get the number of elements of the Masked Array −

print("Elements in the Masked Array...",maskArr.size)


To suppress the rows and/or columns of a 2-D array that contain masked values, use the np.ma.mask_compress_rowcols() method −

print("Result...",np.ma.compress_rowcols(maskArr))

## 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...", arr)
print("Array type...", arr.dtype)

# Get the dimensions of the Array
print("Array Dimensions...",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]])

# Get the dimensions of the Masked Array

# Get the shape of the Masked Array

# Get the number of elements of the Masked Array

# To suppress the rows and/or columns of a 2-D array that contain masked values, use the np.ma.mask_compress_rowcols() method in Numpy
print("Result...",np.ma.compress_rowcols(maskArr))

## Output

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

Array type...
int64

Array Dimensions...
2

[[-- -- 81]
[93 33 76]
[73 -- 51]
[62 -- 67]]

int64

[[76]]