# Concatenate a sequence of masked arrays along axis 0 in Numpy

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To concatenate a sequence of masked arrays, use the ma.concatenate() method in Python Numpy. The axis is set using the "axis" parameter. Here, we have set axis 0.

The parameters are the arrays that must have the same shape, except in the dimension corresponding to axis (the first, by default). The axis is the axis along which the arrays will be joined. Default is 0. The function returns the concatenated array with any masked entries preserved.

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

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

Create a masked array 1 −

arr1 = ma.array(arr1)


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

Display Masked Array 1 −

print("\nMasked Array1...\n",arr1)


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

arr2 = np.arange(9).reshape((3,3))
print("\nArray2...\n", arr2)
print("\nArray type...\n", arr2.dtype)

Create a masked array 2 −

arr2 = ma.array(arr2)


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

Display Masked Array 2 −

print("\nMasked Array2...\n",arr2)


To concatenate a sequence of masked arrays, use the ma.concatenate() method in Python Numpy. The axis is set using the "axis" parameter. Here, we have set axis 0 −

print("\nResult of concatenation...\n",ma.concatenate([arr1, arr2], axis = 0))

## Example

# Python ma.MaskedArray - Concatenate a sequence of masked arrays along axis 0

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...\n", arr1)
print("\nArray type...\n", arr1.dtype)

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

# Get the shape of the Array
print("\nOur Array Shape...\n",arr1.shape)

# Get the number of elements of the Array
print("\nElements in the Array...\n",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("\nArray2...\n", arr2)
print("\nArray type...\n", arr2.dtype)

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

# Get the shape of the Array
print("\nOur Array Shape...\n",arr2.shape)

# Get the number of elements of the Array
print("\nElements in the Array...\n",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 concatenate a sequence of arrays, use the ma.concatenate() method in Python Numpy
print("\nResult of concatenation...\n",ma.concatenate([arr1, arr2], axis = 0))

## 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

[6 -- --]]