reshape() for changing dimensions in NumPy - Step-by-Step Execution

Concept Flow - reshape() for changing dimensions

Start with 1D or ND array

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Call reshape(new_shape)

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Check if total elements match

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Create new array

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Return reshaped array

reshape() changes the shape of an array if the total number of elements stays the same. If not, it gives an error.

Execution Sample

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
new_arr = arr.reshape((2, 3))
print(new_arr)

This code changes a 1D array of 6 elements into a 2x3 2D array.

Execution Table

Step	Action	Input Shape	New Shape	Check Total Elements	Result
1	Create array	(6,)	-	-	Array with elements [1 2 3 4 5 6]
2	Call reshape((2,3))	(6,)	(2,3)	6 == 2*3 (6)	Pass
3	Reshape array	(6,)	(2,3)	-	New array: [[1 2 3] [4 5 6]]
4	Print new array	(2,3)	-	-	[[1 2 3] [4 5 6]]
5	Try invalid reshape	(6,)	(3,4)	6 != 3*4 (12)	Error: cannot reshape array of size 6 into shape (3,4)

💡 Execution stops after error because reshape dimensions do not match total elements.

Variable Tracker

Variable	Start	After reshape((2,3))	After failed reshape((3,4))
arr	[1 2 3 4 5 6]	[1 2 3 4 5 6]	[1 2 3 4 5 6]
new_arr	N/A	[[1 2 3] [4 5 6]]	Error - no change

Key Moments - 2 Insights

Why does reshape((3,4)) cause an error when the original array has 6 elements?

Because 3*4=12 which is not equal to the original 6 elements. reshape() requires total elements to match exactly (see execution_table row 5).

Does reshape() change the data or just the shape?

reshape() only changes the shape, not the data order or values (see execution_table row 3 and 4).

Visual Quiz - 3 Questions

Look at the execution table, what is the shape of new_arr after step 3?

A (2, 3)

B (3, 2)

C (6,)

D (3, 4)

💡 Hint

Check the 'New Shape' and 'Result' columns at step 3 in the execution_table.

At which step does the reshape operation fail due to incompatible dimensions?

A Step 2

B Step 5

C Step 3

D Step 4

💡 Hint

Look for the 'Error' message in the 'Result' column of the execution_table.

If the original array had 12 elements, which reshape would NOT cause an error?

A reshape((2, 3))

B reshape((4, 4))

C reshape((3, 4))

D reshape((1, 5))

💡 Hint

Check the total elements needed for each shape and compare with 12.

Concept Snapshot

reshape() changes the shape of a numpy array without changing data.
Syntax: array.reshape(new_shape)
Total elements must match: product of new_shape == original size.
Raises error if sizes mismatch.
Useful to convert 1D arrays to 2D or higher dimensions.

Full Transcript

The reshape() function in numpy changes the shape of an array while keeping the same data. It requires that the total number of elements before and after reshaping is the same. For example, a 1D array with 6 elements can be reshaped into a 2x3 2D array. If you try to reshape into a shape that does not match the total elements, numpy raises an error. The data order stays the same; only the shape changes.