np.full() for custom-filled arrays in NumPy - Step-by-Step Execution

Concept Flow - np.full() for custom-filled arrays

Start

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Call np.full(shape, fill_value)

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Create empty array of given shape

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Fill every element with fill_value

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Return filled array

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End

np.full() creates an array of a given shape and fills every element with the value you choose.

Execution Sample

NumPy

import numpy as np
arr = np.full((3, 2), 7)
print(arr)

This code creates a 3x2 array where every element is 7.

Execution Table

Step	Action	Shape	Fill Value	Array State
1	Call np.full	(3, 2)	7	Array not created yet
2	Create empty array of shape (3, 2)	(3, 2)	7	[[?, ?], [?, ?], [?, ?]] (uninitialized)
3	Fill all elements with 7	(3, 2)	7	[[7, 7], [7, 7], [7, 7]]
4	Return filled array	(3, 2)	7	[[7, 7], [7, 7], [7, 7]]

💡 Array is fully filled with 7, np.full returns the array.

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
arr	None	[[?, ?], [?, ?], [?, ?]]	[[7, 7], [7, 7], [7, 7]]	[[7, 7], [7, 7], [7, 7]]

Key Moments - 3 Insights

Why does the array show '?' or uninitialized values before filling?

At Step 2, the array memory is allocated but not yet filled, so it contains whatever was in memory (uninitialized). Only at Step 3 does np.full fill all elements with the fill value.

Can np.full create arrays of any shape?

Yes, np.full accepts any shape tuple (like (3, 2), (4,), (2, 2, 2)) and creates arrays of that shape filled with the value, as shown in the Shape column.

What happens if the fill value is a different data type?

np.full will try to cast the fill value to the array's data type. If not specified, it infers from the fill value. This means the array elements will all be the same type.

Visual Quiz - 3 Questions

Look at the execution table, what is the array state after Step 2?

A None

B [[7, 7], [7, 7], [7, 7]]

C [[?, ?], [?, ?], [?, ?]]

D Empty list []

💡 Hint

Check the 'Array State' column for Step 2 in the execution_table.

At which step does the array get filled with the value 7?

A Step 3

B Step 1

C Step 2

D Step 4

💡 Hint

Look at the 'Action' and 'Array State' columns in the execution_table.

If we change the fill value to 0, how would the final array look?

A [[?, ?], [?, ?], [?, ?]]

B [[0, 0], [0, 0], [0, 0]]

C [[7, 7], [7, 7], [7, 7]]

D An error occurs

💡 Hint

Refer to the final 'Array State' in variable_tracker and replace 7 with 0.

Concept Snapshot

np.full(shape, fill_value) creates a new array of the given shape.
Every element in the array is set to fill_value.
Shape is a tuple like (rows, columns).
Useful to initialize arrays with a constant value.
Returns a NumPy ndarray filled with the value.

Full Transcript

np.full() is a NumPy function that creates an array with a shape you choose and fills every element with the value you provide. First, it allocates memory for the array with the given shape. Initially, this memory is uninitialized and may contain random values. Then, np.full fills every element with the fill value. Finally, it returns the filled array. For example, np.full((3, 2), 7) creates a 3-row, 2-column array where every element is 7. This is useful when you want an array pre-filled with a specific number instead of zeros or random values.