Covariance with np.cov() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the covariance matrix of data using numpy.

NumPy

import numpy as np

data = np.array([[1, 2, 3], [4, 5, 6]])
cov_matrix = np.cov([1])
print(cov_matrix)

Drag options to blanks, or click blank then click option'

Anp.mean

Bnp.array

Cnp.cov

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function np.cov instead of the data array.

Passing np.mean which calculates mean, not covariance.

The np.cov() function calculates the covariance matrix of the input data array. Here, we pass the variable data to it.

2fill in blank

medium

Complete the code to calculate covariance matrix with rows as variables.

NumPy

import numpy as np

data = np.array([[1, 2, 3], [4, 5, 6]])
cov_matrix = np.cov(data, rowvar=[1])
print(cov_matrix)

Drag options to blanks, or click blank then click option'

ATrue

BFalse

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Setting rowvar=False when rows are variables.

Passing None which is invalid for rowvar.

By default, rowvar=True means each row represents a variable. Here, data rows are variables, so we set rowvar=True.

3fill in blank

hard

Fix the error in the code to get covariance matrix of columns as variables.

NumPy

import numpy as np

data = np.array([[1, 2, 3], [4, 5, 6]])
cov_matrix = np.cov(data, rowvar=[1])
print(cov_matrix)

Drag options to blanks, or click blank then click option'

ATrue

BFalse

C1

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using rowvar=True when columns are variables.

Passing invalid values like None or 1.

When columns represent variables, set rowvar=False so covariance is calculated between columns.

4fill in blank

hard

Fill both blanks to create a covariance matrix from data with variables in columns and normalize by N (not N-1).

NumPy

import numpy as np

data = np.array([[1, 2, 3], [4, 5, 6]])
cov_matrix = np.cov(data, rowvar=[1], bias=[2])
print(cov_matrix)

Drag options to blanks, or click blank then click option'

AFalse

BTrue

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using rowvar=True when variables are columns.

Setting bias=False which normalizes by N-1.

Set rowvar=False because variables are in columns. Set bias=True to normalize by N instead of N-1.

5fill in blank

hard

Fill the blanks to compute covariance matrix of variables in rows, with bias normalization, and print shape.

NumPy

import numpy as np

data = np.array([[7, 8, 9], [10, 11, 12]])
cov_matrix = np.cov(data, rowvar=[1], bias=[2])
print(cov_matrix.shape, cov_matrix)

Drag options to blanks, or click blank then click option'

ATrue

BFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using rowvar=False when variables are rows.

Setting bias=False which normalizes by N-1.

Set rowvar=True because variables are rows. Set bias=True to normalize by N. The shape printed shows the covariance matrix size.