Covariance with np.cov() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the np.cov() function calculate?

It calculates the covariance matrix, which shows how two or more variables change together.

beginner

What is covariance in simple terms?

Covariance measures if two variables increase or decrease together. Positive means they move together, negative means opposite.

intermediate

How do you interpret a covariance matrix output from np.cov()?

The diagonal shows variance of each variable. Off-diagonal values show covariance between pairs of variables.

intermediate

What does setting rowvar=False in np.cov() do?

It tells np.cov() that each column represents a variable, and rows are observations.

beginner

Why is covariance important in data science?

It helps understand relationships between variables, which is key for analysis and building models.

What does a positive covariance between two variables indicate?

A They increase or decrease together

B They move in opposite directions

C They are independent

D They have no relationship

Positive covariance means both variables tend to increase or decrease at the same time.

In np.cov(), what does the default rowvar=True mean?

A Covariance is scaled

B Each row is a variable

C Data is normalized

D Each column is a variable

By default, np.cov() treats each row as a variable and columns as observations.

What shape is the output of np.cov() when given two variables?

A 2x1 matrix

B 1x2 matrix

C 2x2 matrix

D Scalar value

The covariance matrix for two variables is 2x2, showing variance and covariance.

Which numpy function is used to calculate covariance?

A np.mean()

B np.std()

C np.var()

D np.cov()

np.cov() calculates covariance matrices.

If covariance between two variables is zero, what does it mean?

A Variables have no linear relationship

B Variables are perfectly correlated

C Variables are identical

D Variables have negative correlation

Zero covariance means no linear relationship between variables.

Explain what covariance is and how np.cov() helps calculate it.

Think about how two variables move together and how numpy shows this.

Describe how to use np.cov() with a dataset where each column is a variable and each row is an observation.

Remember the parameter that changes how variables are read.