Join a sequence of masked arrays along axis 0 in Numpy

NumpyServer Side ProgrammingProgramming

To join a sequence of masked arrays along axis 0, use the ma.stack() method in Python Numpy. The axis is set using the "axis" parameter. The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.

The out parameter, if provided, is the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified.

The function returns the stacked array has one more dimension than the input arrays. It is applied to both the _data and the _mask, if any.

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 array1 −

arr1 = ma.array(arr1)

Mask Array1 −

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 array2 −

arr2 = ma.array(arr2)

Mask Array2 −

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

Display Masked Array 2 −

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

To join a sequence of masked arrays along specific axis, use the ma.stack() method. The axis is set using the "axis" parameter −

print("\nResult of joining arrays...\n",ma.stack((arr1, arr2), axis = 0))

Example

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)

# Mask Array1
arr1[0, 1] = ma.masked
arr1[1, 1] = ma.masked

# Display Masked Array 1
print("\nMasked Array1...\n",arr1)

# 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)

# Mask Array2
arr2[2, 1] = ma.masked
arr2[2, 2] = ma.masked

# Display Masked Array 2
print("\nMasked Array2...\n",arr2)

# To join a sequence of masked arrays along specific axis, use the ma.stack() method in Python Numpy
# The axis is set using the "axis" parameter
print("\nResult of joining arrays...\n",ma.stack((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

Masked Array1...
[[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

Masked Array2...
[[0 1 2]
[3 4 5]
[6 -- --]]

Result of joining arrays...
[[[0 -- 2]
[3 -- 5]
[6 7 8]]
[[0 1 2]
[3 4 5]
[6 -- --]]]
raja
Updated on 04-Feb-2022 11:40:45

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