Masked arrays concept in NumPy - Step-by-Step Execution

Concept Flow - Masked arrays concept

Create normal array

↓

Define mask (True=hide, False=show)

↓

Apply mask to array

↓

Use masked array for calculations

↓

Masked elements ignored in output

Start with a normal array, define which elements to hide using a mask, apply it, then use the masked array where hidden elements are ignored.

Execution Sample

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
mask = [False, True, False, True, False]
masked_arr = np.ma.masked_array(arr, mask=mask)
print(masked_arr)

Create an array, mask some elements, and print the masked array showing hidden elements.

Execution Table

Step	Action	Array	Mask	Masked Array Output
1	Create array	[1 2 3 4 5]	[False False False False False]	[1 2 3 4 5]
2	Define mask	[1 2 3 4 5]	[False True False True False]	[1 -- 3 -- 5]
3	Apply mask	[1 2 3 4 5]	[False True False True False]	[1 -- 3 -- 5]
4	Print masked array	[1 2 3 4 5]	[False True False True False]	[1 -- 3 -- 5]
5	Sum masked array	[1 2 3 4 5]	[False True False True False]	9 (2 and 4 ignored)
6	Exit	-	-	Mask applied, masked elements hidden

💡 Mask applied, masked elements are hidden and ignored in calculations

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
arr	None	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]
mask	None	[False False False False False]	[False True False True False]	[False True False True False]	[False True False True False]
masked_arr	None	None	None	[1 -- 3 -- 5]	[1 -- 3 -- 5]

Key Moments - 3 Insights

Why do masked elements show as '--' instead of their original values?

Masked elements are hidden by design to indicate they are ignored. The '--' shows these values are masked, as seen in execution_table rows 2-4.

Does the mask change the original array values?

No, the original array stays the same. The mask only hides elements in the masked array view, shown in variable_tracker where 'arr' stays unchanged.

Are masked elements included in calculations like sum?

No, masked elements are ignored in calculations. For example, in execution_table step 5, sum excludes masked values 2 and 4.

Visual Quiz - 3 Questions

Look at the execution table at step 2. What is the mask applied to the array?

A [True False True False True]

B [False False False False False]

C [False True False True False]

D [True True True True True]

💡 Hint

Check the 'Mask' column at step 2 in the execution_table.

At which step does the masked array first show masked elements as '--'?

A Step 1

B Step 2

C Step 4

D Step 5

💡 Hint

Look at the 'Masked Array Output' column in execution_table rows 1 and 2.

If the mask was all False, what would the masked array output be at step 3?

A [1 2 3 4 5]

B [1 -- 3 -- 5]

C [-- -- -- -- --]

D [0 0 0 0 0]

💡 Hint

Refer to variable_tracker for mask values and their effect on masked_arr.

Concept Snapshot

Masked arrays let you hide elements in data.
Use a mask of True/False to mark hidden elements.
Masked elements show as '--' and are ignored in calculations.
Original data stays unchanged.
Useful for ignoring invalid or missing data.

Full Transcript

Masked arrays in numpy allow you to hide certain elements in an array using a mask. The mask is a list or array of True or False values, where True means the element is hidden. When you apply the mask, the masked array shows hidden elements as '--'. These masked elements do not affect calculations like sum or mean. The original array remains unchanged. This is helpful when you want to ignore some data points without deleting them.