Masked arrays concept in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a masked array in numpy?

A masked array is like a regular array but with a mask that hides or ignores certain elements during computations. It helps handle missing or invalid data easily.

beginner

How do you create a masked array in numpy?

You can create a masked array using numpy.ma.array(data, mask=mask_array), where mask_array is a boolean array marking elements to hide.

intermediate

What happens when you perform operations on masked arrays?

Operations ignore the masked elements, so calculations like sums or means only consider visible data, avoiding errors from missing or invalid values.

beginner

How can masked arrays help with real-world data?

They help handle missing or corrupted data points without removing entire rows or columns, making analysis more accurate and easier.

beginner

What is the difference between a masked array and a regular numpy array?

A masked array has an extra mask that marks elements to ignore, while a regular array treats all elements equally without hiding any.

What does the mask in a numpy masked array do?

A Duplicates the array

B Changes the data type of elements

C Sorts the array elements

D Marks elements to ignore in calculations

The mask is a boolean array that marks which elements to ignore during computations.

How do masked arrays handle missing data?

A They hide missing data using a mask

B They replace missing data with zeros

C They convert missing data to strings

D They remove missing data permanently

Masked arrays hide missing or invalid data using a mask, so calculations ignore those elements.

Which numpy module provides masked array functionality?

A numpy.ma

B numpy.linalg

C numpy.random

D numpy.fft

The masked array functions are in the numpy.ma module.

What is the output of sum on a masked array with some masked elements?

A Sum of all elements including masked

B Always zero

C Sum ignoring masked elements

D Error due to masked elements

Sum ignores masked elements and adds only visible data.

Which of these is a valid way to create a masked array?

A numpy.array([1, 2, 3], mask=[False, True, False])

B numpy.ma.array([1, 2, 3], mask=[False, True, False])

C numpy.masked([1, 2, 3])

D numpy.array.mask([1, 2, 3])

The correct way is to use numpy.ma.array with a mask argument.

Explain what a masked array is and why it is useful in data analysis.

Think about how you handle missing data in a spreadsheet.

Describe how you would create a masked array in numpy and perform a sum ignoring certain values.

Remember the mask is a boolean array matching the data.