# Stack masked arrays in sequence vertically (row wise) in Numpy

To stack masked arrays in sequence vertically (row wise), use the ma.vstack() method in Python Numpy. This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.

This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.

The parameters are the arrays that must have the same shape along all but the first axis. 1-D arrays must have the same length.

## Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

Create Array 1, a 3x3 array with int elements using the numpy.arange() method −

arr1 = np.arange(9).reshape((3,3))
print("Array1...", arr1)
print("Array type...", arr1.dtype)

Create a masked array1 −

arr1 = ma.array(arr1)

arr1[0, 1] = ma.masked
arr1[1, 1] = ma.masked

Display Masked Array 1 −

print("Masked Array1...",arr1)

Create Array 2, another 3x3 array with int elements using the numpy.arange() method −

arr2 = np.arange(9).reshape((3,3))
print("Array2...", arr2)
print("Array type...", arr2.dtype)

Create masked array2 −

arr2 = ma.array(arr2)

arr2[2, 1] = ma.masked
arr2[2, 2] = ma.masked

Display Masked Array 2 −

print("Masked Array2...",arr2)

To stack masked arrays in sequence vertically (row wise), use the ma.vstack() method in Python Numpy −

print("Result of stacking arrays vertically...",ma.vstack((arr1, arr2)))

## Example

# Python ma.MaskedArray - Stack masked arrays in sequence vertically (row wise)

import numpy as np
import numpy.ma as ma

# Array 1
# Creating a 3x3 array with int elements using the numpy.arange() method
arr1 = np.arange(9).reshape((3,3))
print("Array1...", arr1)
print("Array type...", arr1.dtype)

# Get the dimensions of the Array
print("Array Dimensions...",arr1.ndim)

# Get the shape of the Array
print("Our Array Shape...",arr1.shape)

# Get the number of elements of the Array
print("Elements in the Array...",arr1.size)

# Create a masked array
arr1 = ma.array(arr1)

arr1[0, 1] = ma.masked
arr1[1, 1] = ma.masked

# Display Masked Array 1

# Array 2
# Creating another 3x3 array with int elements using the numpy.arange() method
arr2 = np.arange(9).reshape((3,3))
print("Array2...", arr2)
print("Array type...", arr2.dtype)

# Get the dimensions of the Array
print("Array Dimensions...",arr2.ndim)

# Get the shape of the Array
print("Our Array Shape...",arr2.shape)

# Get the number of elements of the Array
print("Elements in the Array...",arr2.size)

# Create a masked array
arr2 = ma.array(arr2)

arr2[2, 1] = ma.masked
arr2[2, 2] = ma.masked

# Display Masked Array 2

# To stack masked arrays in sequence vertically (row wise), use the ma.vstack() method in Python Numpy
print("Result of stacking arrays vertically...",ma.vstack((arr1, arr2)))

## Output

Array1...
[[0 1 2]
[3 4 5]
[6 7 8]]

Array type...
int64

Array Dimensions...
2

Our Array Shape...
(3, 3)

Elements in the Array...
9

[[0 -- 2]
[3 -- 5]
[6 7 8]]

Array2...
[[0 1 2]
[3 4 5]
[6 7 8]]

Array type...
int64

Array Dimensions...
2

Our Array Shape...
(3, 3)

Elements in the Array...
9

[[0 1 2]
[3 4 5]
[6 -- --]]

Result of stacking arrays vertically...
[[0 -- 2]
[3 -- 5]
[6 7 8]
[0 1 2]
[3 4 5]
[6 -- --]]

Updated on: 03-Feb-2022

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