np.full() for custom-filled arrays in NumPy - Mini Project: Build & Apply

Create Custom-Filled Arrays Using np.full()

📖 Scenario: You are working on a data science project where you need to create arrays filled with specific values to represent initial conditions or placeholders.

🎯 Goal: Learn how to use np.full() to create NumPy arrays filled with a custom value.

📋 What You'll Learn

Create a NumPy array with a specific shape filled with a custom value using np.full().

Use variables to define the shape and fill value.

Print the resulting array to see the output.

💡 Why This Matters

🌍 Real World

Creating arrays filled with specific values is useful in data science for initializing data structures, placeholders, or masks.

💼 Career

Understanding how to create and manipulate arrays with NumPy is a fundamental skill for data scientists and analysts working with numerical data.

1

Create the array shape variable

Create a variable called shape and set it to a tuple with values (3, 4) representing the dimensions of the array.

NumPy

# Create a variable called shape with the tuple (3, 4)
# Your code here

Hint

Use parentheses to create a tuple for the shape, like (3, 4).

2

Set the fill value

Create a variable called fill_value and set it to the integer 7 which will be used to fill the array.

NumPy

shape = (3, 4)
# Create a variable called fill_value and set it to 7
# Your code here

Hint

Just assign the number 7 to the variable fill_value.

3

Create the filled array using np.full()

Import the numpy library as np. Then create a variable called filled_array using np.full() with the shape and fill_value variables.

NumPy

shape = (3, 4)
fill_value = 7
# Import numpy as np
# Create filled_array using np.full() with shape and fill_value
# Your code here

Hint

Use import numpy as np and then np.full(shape, fill_value) to create the array.

4

Print the filled array

Print the variable filled_array to display the array filled with the custom value.

NumPy

import numpy as np
shape = (3, 4)
fill_value = 7
filled_array = np.full(shape, fill_value)
# Print the filled_array
# Your code here

Hint

Use print(filled_array) to show the array.

Practice

1. What does the np.full() function do in NumPy?

easy

A. Creates an empty array with no values

B. Creates an array with random values

C. Creates an array with increasing numbers

D. Creates an array filled with the same specified value

Solution

1. Step 1: Understand the purpose of np.full()

   The function np.full() is designed to create arrays where every element has the same value.

2. Step 2: Compare with other array creation functions

   Unlike functions that create random or sequential arrays, np.full() fills the array with a constant value.

3. Final Answer:

   Creates an array filled with the same specified value -> Option D

4. Quick Check:

   np.full() = constant value array [OK]

Hint: Remember: full means fill with one value [OK]

Common Mistakes:

• Confusing np.full() with np.random functions
• Thinking it creates empty or zero arrays
• Assuming it creates sequences of numbers

2. Which of the following is the correct syntax to create a 3x3 NumPy array filled with the value 7 using np.full()?

easy

A. np.full((3, 3), 7)

B. np.full(3, 3, 7)

C. np.full([3, 3], value=7)

D. np.full(7, shape=(3,3))

Solution

1. Step 1: Identify the correct parameter order for np.full()

   np.full() takes the shape as the first argument (tuple), then the fill value as the second argument.

2. Step 2: Check each option's syntax

   np.full((3, 3), 7) uses np.full((3, 3), 7) which matches the correct order and types. Others misuse argument order or syntax.

3. Final Answer:

   np.full((3, 3), 7) -> Option A

4. Quick Check:

   Shape tuple first, fill value second [OK]

Hint: Shape is first tuple, value is second argument [OK]

Common Mistakes:

• Passing shape and value as separate arguments without tuple
• Using keyword arguments incorrectly
• Swapping the order of shape and fill value

3. What is the output of the following code?

import numpy as np
arr = np.full((2, 2), 5)
print(arr)

medium

A. [[5 5]
[5 5]]

B. [[2 2]
[2 2]]

C. [[0 0]
[0 0]]

D. [[5 5 5]
[5 5 5]]

Solution

1. Step 1: Understand np.full() parameters

   The shape is (2, 2), so the array will have 2 rows and 2 columns. The fill value is 5.

2. Step 2: Predict the printed array

   The array will be a 2x2 matrix with all elements equal to 5, printed as [[5 5]\n [5 5]].

3. Final Answer:

   [[5 5] [5 5]] -> Option A

4. Quick Check:

   Shape (2,2) with 5 fills = [[5 5]\n [5 5]] [OK]

Hint: Shape defines size, fill value repeats [OK]

Common Mistakes:

• Confusing shape dimensions
• Expecting zeros instead of fill value
• Misreading the printed array shape

4. Identify the error in this code snippet:

import numpy as np
arr = np.full(3, 4, 2)
print(arr)

medium

A. Fill value must be a string

B. Shape argument must be a tuple

C. The third positional argument `2` is an invalid dtype specifier

D. np.full() cannot create 1D arrays

Solution

1. Step 1: Check np.full() argument count and order

   np.full(shape, fill_value, dtype=None, ...). The call np.full(3, 4, 2) sets shape=3, fill_value=4, dtype=2, but 2 is not a valid dtype (raises ValueError: data type "2" not understood).

2. Step 2: Confirm correct usage

   Correct syntax is np.full(3, 4) for 1D array of 4s, or specify valid dtype like dtype=float. Extra or invalid dtype causes error.

3. Final Answer:

   The third positional argument `2` is an invalid dtype specifier -> Option C

4. Quick Check:

   np.full(3,4,2) -> ValueError: data type "2" [OK]

Hint: Optional dtype must be valid (e.g., float, int) [OK]

Common Mistakes:

• Passing multiple positional arguments instead of tuple shape
• Using wrong data types for shape
• Assuming np.full() needs three arguments

5. You want to create a 4x4 NumPy array filled with the value 9, but only the diagonal elements should be 0. Which code correctly achieves this using np.full() and other NumPy functions?

hard

A. arr = np.zeros((4,4)); arr[:] = 9; print(arr)

B. arr = np.full((4,4), 9); np.fill_diagonal(arr, 0); print(arr)

C. arr = np.full((4,4), 0); np.fill_diagonal(arr, 9); print(arr)

D. arr = np.full(4, 9); np.fill_diagonal(arr, 0); print(arr)

Solution

1. Step 1: Create a 4x4 array filled with 9

   Using np.full((4,4), 9) creates a 4x4 array where every element is 9.

2. Step 2: Replace diagonal elements with 0

   np.fill_diagonal(arr, 0) sets the diagonal elements of the array to 0.

3. Step 3: Verify the output

   Printing arr shows a 4x4 array with 9 everywhere except 0 on the diagonal.

4. Final Answer:

   arr = np.full((4,4), 9); np.fill_diagonal(arr, 0); print(arr) -> Option B

5. Quick Check:

   Fill then fill_diagonal for custom values [OK]

Hint: Fill full array first, then change diagonal [OK]

Common Mistakes:

• Filling zeros first then trying to fill diagonal with 9
• Using wrong shape or 1D array for 2D problem
• Misusing np.fill_diagonal on 1D arrays