np.full() for custom-filled arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

np.full() creates a new array of a given shape and fills it with a specified value.

beginner

How do you specify the shape of the array in np.full()?

You provide the shape as the first argument, either as an integer for 1D or a tuple for multi-dimensional arrays.

beginner

What is the role of the fill_value parameter in np.full()?

fill_value is the value used to fill every element of the new array.

intermediate

Can np.full() create arrays of different data types? How?

Yes, by using the optional dtype parameter, you can specify the data type of the array elements.

beginner

Why might you use np.full() instead of np.zeros() or np.ones()?

Use np.full() when you want an array filled with a custom value other than 0 or 1.

What is the correct way to create a 3x3 array filled with the number 7 using np.full()?

A np.full(3, 3, 7)

B np.full(3, 7)

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

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

The shape must be a tuple (3, 3) and the fill value is 7.

Which parameter in np.full() controls the data type of the array elements?

A shape

B dtype

C fill_value

D order

dtype sets the data type of the array elements.

What will np.full(5, 0) create?

A A 5x5 array filled with zeros

B A scalar zero

C A 1D array of length 5 filled with zeros

D An error

Shape is 5, so a 1D array of length 5 filled with 0.

If you want a 2x2 array filled with the string 'hi', which code is correct?

A np.full((2, 2), 'hi')

B np.full((2, 2), hi)

C np.full(2, 'hi')

D np.full('hi', (2, 2))

The shape is (2, 2) and the fill value is the string 'hi'.

What happens if you do not specify dtype in np.full()?

A It infers the data type from the fill value

B It defaults to float64

C It raises an error

D It defaults to int32

NumPy guesses the data type based on the fill value.

Explain how to create a 4x4 NumPy array filled with the number 9 using np.full(). Include how to specify the shape and fill value.

Think about the first argument as shape and second as the value to fill.

Describe the difference between np.full() and np.zeros(). When would you use each?

Consider what value you want in 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