# Python Numpy matrix.itemset() Function

NumPy Library

NumPy is a library in python that is created to work efficiently with arrays in python. It is fast, easy to learn, and provides efficient storage. It also provides a better way of handling data for the process. We can create an n-dimensional array in NumPy. To use NumPy simply have to import it into our program and then we can easily use the functionality of NumPy in our program.

NumPy is a Python library that is frequently used for scientific and statistical analysis. NumPy arrays are grids of the same datatype’s values.

Numpy matrix.itemset() Function:

We can set the items in a given matrix using the matrix.itemset() method of the Numpy module by passing the index number and the item as arguments.

Syntax:

 matrix.itemset(index, item)

Return Value:

A new matrix containing item is returned by the itemset() function.

## Numpy matrix.itemset() Function in Python

For 2-Dimensional (2D) Matrix

Approach:

• Import numpy module using the import keyword
• Create a matrix(2-Dimensional) using the matrix() function of numpy module by passing some random 2D matrix as arguments to it and store it in a variable
• Pass the index(row, column) and item/value as an argument to the itemset() function and apply it to the given matrix.
• Here it inserts the given item at the specified index in a matrix(inserts 50 at 0th row, 1st col).
• Store it in another variable
• Print the given matrix after inserting an item at the specified index.
• The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np

# Create a matrix(2-Dimensional) using the matrix() function of numpy module by passing
# some random 2D matrix as arguments to it and store it in a variable
gvn_matrx = np.matrix('[2, 1; 6, 3]')

# Pass the index(row, column) and item/value as an argument to the itemset() function and
# apply it to the given matrix.
# Here it inserts the given item at the specified index in a matrix(inserts 50 at 0th row, 1st col).
# Store it in another variable
gvn_matrx.itemset((0, 1), 50)
# Print the given matrix after inserting an item at the specified index
print("The given matrix after inserting an item {50} at the specified index(0th row, 1st col):")
print(gvn_matrx)

Output:

The given matrix after inserting an item {50} at the specified index(0th row, 1st col):
[[ 2 50]
[ 6 3]]

For 3-Dimensional (3D) Matrix

Approach:

• Import numpy module using the import keyword
• Create a matrix(3-Dimensional) using the matrix() function of numpy module by passing some random 3D matrix as arguments to it and store it in a variable
• Pass the index(row, column) and item/value as an argument to the itemset() function and apply it to the given matrix.
• Here it inserts the given item at the specified index in a matrix(inserts 50 at 0th row, 1st col).
• Store it in another variable
• Print the given matrix after inserting an item at the specified index.
• The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np

# Create a matrix(3-Dimensional) using the matrix() function of numpy module by passing
# some random 3D matrix as an argument to it and store it in a variable
gvn_matrx = np.matrix('[2, 4, 1; 8, 7, 3; 10, 9, 5]')

# Pass the index(row, column) and item/value as an argument to the itemset() function and
# apply it to the given matrix.
# Here it inserts the given item at the specified index in a matrix(inserts 100 at 1st row, 2nd col).
# Store it in another variable
gvn_matrx.itemset((1, 2), 100)
# Print the given matrix after inserting an item at the specified index
print("The given matrix after inserting an item {100} at the specified index(1st row, 2nd col):")
print(gvn_matrx)

Output:

The given matrix after inserting an item {100} at the specified index(1st row, 2nd col):
[[ 2 4 1]
[ 8 7 100]
[ 10 9 5]]