Shuffling arrays in NumPy - Step-by-Step Execution

Concept Flow - Shuffling arrays

Start with original array

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Call shuffle function

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Randomly reorder elements

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Return shuffled array

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Use shuffled array for analysis

We start with an original array, then call a shuffle function that randomly reorders its elements, returning the shuffled array for further use.

Execution Sample

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
np.random.shuffle(arr)
print(arr)

This code shuffles the elements of the array in place and prints the new order.

Execution Table

Step	Action	Array State Before	Array State After	Notes
1	Initialize array	[1, 2, 3, 4, 5]	[1, 2, 3, 4, 5]	Original array created
2	Call np.random.shuffle(arr)	[1, 2, 3, 4, 5]	[3, 1, 5, 2, 4]	Elements reordered randomly (example)
3	Print array	[3, 1, 5, 2, 4]	[3, 1, 5, 2, 4]	Shuffled array output
4	End	[3, 1, 5, 2, 4]	[3, 1, 5, 2, 4]	Shuffling complete

💡 Shuffling stops after array is reordered in place.

Variable Tracker

Variable	Start	After shuffle	Final
arr	[1, 2, 3, 4, 5]	[3, 1, 5, 2, 4]	[3, 1, 5, 2, 4]

Key Moments - 3 Insights

Why does the original array change after shuffle?

Because np.random.shuffle modifies the array in place, it does not create a new array. See execution_table step 2 where the array state changes directly.

Is the shuffle result always the same?

No, the shuffle is random each time you run it, so the order changes. The example in execution_table step 2 is just one possible outcome.

Can shuffle be used on multi-dimensional arrays?

Yes, but it only shuffles along the first axis (rows). The internal order of elements in each row stays the same.

Visual Quiz - 3 Questions

Look at the execution_table step 2, what is the array state after shuffle?

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

B [3, 1, 5, 2, 4]

C [5, 4, 3, 2, 1]

D [2, 3, 1, 5, 4]

💡 Hint

Check the 'Array State After' column in step 2 of execution_table.

At which step does the array get shuffled in place?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look for the step where np.random.shuffle is called in execution_table.

If we want to keep the original array unchanged, what should we do before shuffling?

A Create a copy of the array and shuffle the copy

B Shuffle directly with np.random.shuffle

C Use print before shuffle

D Use np.random.shuffle with a seed

💡 Hint

Recall that shuffle modifies the array in place as shown in variable_tracker.

Concept Snapshot

Shuffling arrays with numpy:
- Use np.random.shuffle(array) to reorder elements randomly.
- This changes the original array in place.
- For multi-dimensional arrays, shuffle affects only the first axis.
- To keep original data, shuffle a copy instead.

Full Transcript

We start with an original numpy array. Calling np.random.shuffle on it rearranges its elements randomly in place, changing the original array. The shuffled array can then be used for analysis or modeling. The shuffle is random each time, so results differ. For multi-dimensional arrays, only rows are shuffled, not elements inside rows. To preserve the original array, make a copy before shuffling.