# NumPy Inverse Matrix in Python

NumPy `linalg.inv()` function in Python is used to compute the (multiplicative) inverse of a matrix. The inverse of a matrix is that matrix which when multiplied with the original matrix, results in an identity matrix. In this article, I will explain how to use the NumPy inverse matrix to compute the inverse of the matrix array using this function.

## 1. Quick Examples of Inverse Matrix

If you are in a hurry, below are some quick examples of how to use Python NumPy inverse matrix.

``````
# Below are the quick examples

# Example 1: Use numpy.linalg.inv() to get inverse of a matrix
arr = np.array([[7, 2,], [3, -5]])
arr2 = np.linalg.inv(arr)

# Example 2: Get the inverse of a matrix using scipy.linalg.inv() function
arr = np.matrix([[7, 2,], [3, -5]])
arr2 = linalg.inv(arr)

# Example 3: Use np.linalg.inv() function
arr = np.array([[[2., 6.], [5., 8.]],
[[3, 7], [4, 1]]])
arr2 = np.linalg.inv(arr)
``````

## 2. Syntax of numpy.linalg.inv() Function

Following is the syntax to create `numpy.linalg.inv()` function.

``````
# Syntax of numpy.linalg.inv() function
numpy.linalg.inv(arr)
``````

### 2.1 Parameter of Inverse Matrix

Following are the parameters of the inverse matrix.

`arr` : This parameter represents the matrix to be inverted.

### 2.2 Return Value of Inverse Matrix

This function returns the inverse of the matrix array.

## 3. Usage of numpy.linalg.inv() Function

Using Python `numpy.linalg.inv()` function to the inverse of a matrix in simple mathematics can be defined as a matrix.

### 3.1 Use numpy.linalg.inv() Function

We can use a matrix as a rectangular arrangement of data or numbers, in other words, we can say that it is a rectangular array of data the horizontal entries in the matrix are called rows and the vertical entries are called columns. For the matrix inverse function, we need to use `np.linalg.inv()` function. This function will inverse the given matrix. Python NumPy provides an easy function to calculate the inverse of the matrix. The function helps the user to check `numpy.linalg.inv()` is available in the Python library.

``````
import numpy as np

# Creating an input array
arr = np.array([[7, 2,], [3, -5]])

# Use numpy.linalg.inv() to get inverse of a matrix
arr2 = np.linalg.inv(arr)
print(arr2)

# Output:
# [[ 0.12195122  0.04878049]
#  [ 0.07317073 -0.17073171]]
``````

## 4. Get the Inverse of a Matrix Using scipy.linalg.inv() Function

We can also use the `scipy` module to perform different scientific calculations using its functionalities. Using `scipy.linalg.inv()` function is used to return the inverse of a given square matrix in NumPy Python. It works the same way as the `numpy.linalg.inv()` function.

``````
import numpy as np
from scipy import linalg

# Creating an input array
arr = np.matrix([[7, 2,],[3, -5]])

# Get the inverse of a matrix using scipy.linalg.inv() function
arr2 = linalg.inv(arr)
print(arr2)
``````

Yields the same output as above.

## 5. Inverse of a Matrix NumPy Two Multi-Dimensional Arrays

We can also use `np.linalg.inv()` function to compute the multiplicative inverse of a matrix of the two multi-dimensional arrays elementwise.

``````
import numpy as np

# Inverse of 4X4 matrix
arr = np.array([[[2., 6.], [5., 8.]],
[[3, 7], [4, 1]]])

# Use np.linalg.inv() function
arr2 = np.linalg.inv(arr)
print(arr2)

# Output:
# [[[-0.57142857  0.42857143]
#  [ 0.35714286 -0.14285714]]

# [[-0.04        0.28   ]
# [ 0.16       -0.12   ]]]
``````

## 6. Conclusion

In this article, I have explained how to use the inverse matrix to compute the inverse of the matrix array with examples.

Happy Learning!!

## References

### Vijetha

With 5 of experience in technical writing, I have had the privilege to work with a diverse range of technologies like Python, Pandas, NumPy and R. During this time, I have consistently demonstrated my ability to grasp intricate technical details and transform them into comprehensible materials. 