np.linalg.inv() for matrix inverse in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the numpy library with the common alias.

NumPy

import [1] as np

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Anumpy

Bpandas

Cmatplotlib

Dscipy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong library like pandas or matplotlib.

Not using the alias np.

We import the numpy library as np to use its functions easily.

2fill in blank

medium

Complete the code to create a 2x2 numpy array named matrix.

NumPy

matrix = np.array([1])

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A[[1, 2, 3], [4, 5, 6]]

B[1, 2, 3, 4]

C[[1, 2], [3, 4]]

D[[1, 2], 3, 4]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a flat list instead of nested lists.

Creating a matrix with wrong dimensions.

The matrix must be a 2x2 nested list to create a 2D numpy array.

3fill in blank

hard

Fix the error in the code to compute the inverse of the matrix.

NumPy

inverse = np.linalg.[1](matrix)

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Aeig

Bdet

Csolve

Dinv

Attempts:

3 left

💡 Hint

Common Mistakes

Using det which computes determinant, not inverse.

Using solve which solves linear systems.

The function np.linalg.inv() computes the inverse of a matrix.

4fill in blank

hard

Fill both blanks to create a dictionary with keys as matrix elements and values as their inverses.

NumPy

inverse_dict = {element: [1] for row in matrix for element in row if element [2] 0}

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A1/element

Belement

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using equality check instead of inequality in the condition.

Not handling zero elements causing division by zero.

We calculate the inverse of each element by dividing 1 by it, and skip zero elements to avoid division errors.

5fill in blank

hard

Fill all three blanks to compute the inverse matrix and print it.

NumPy

import numpy as np
matrix = np.array([1])
inverse = np.linalg.[2](matrix)
print([3])

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A[[2, 0], [0, 2]]

Binv

Cinverse

D[[1, 2], [3, 4]]

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong matrix shape.

Calling a wrong function instead of inv.

Printing the original matrix instead of the inverse.

We create a 2x2 matrix, compute its inverse using np.linalg.inv(), and print the inverse matrix.