np.full() for custom-filled arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.full() with integer fill value

What is the output of this code?

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

NumPy

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

A

[[7 7 7]
 [7 7 7]]

B

[[7 7 7 7]
 [7 7 7 7]]

C

[[7 7]
 [7 7]
 [7 7]]

D

[[0 0 0]
 [0 0 0]]

💡 Hint

Look at the shape argument (2, 3) and the fill value 7.

np.full creates an array of the given shape filled with the fill value. Here, shape (2,3) means 2 rows and 3 columns filled with 7.

❓ data_output

intermediate

Shape and dtype of np.full() array

What is the shape and data type of the array created by this code?

import numpy as np
arr = np.full((4,), 3.14, dtype=np.float32)
print(arr.shape, arr.dtype)

NumPy

import numpy as np
arr = np.full((4,), 3.14, dtype=np.float32)
print(arr.shape, arr.dtype)

A (4,) int64

B (4, 1) float64

C (1, 4) float32

D (4,) float32

💡 Hint

Check the shape tuple and the dtype argument.

The shape is (4,) meaning a 1D array with 4 elements. The dtype is float32 as specified.

🔧 Debug

advanced

Identify the error in np.full() usage

What error does this code raise?

import numpy as np
arr = np.full(3, [1, 2])
print(arr)

NumPy

import numpy as np
arr = np.full(3, [1, 2])
print(arr)

A ValueError: could not broadcast input array from shape (2,) into shape ()

B No error, prints array with shape (3, 2)

C SyntaxError: invalid syntax

D TypeError: fill_value must be a scalar

💡 Hint

np.full expects a scalar fill value, but here a list is given.

np.full expects a scalar fill value. Passing a list causes a broadcasting error because it cannot fit the list into each scalar element.

🚀 Application

advanced

Creating a masked array with np.full()

You want to create a 3x3 numpy array filled with -1 to mark missing data. Which code correctly creates this array with integer type?

A np.full((3, 3), '-1')

B np.full((3, 3), -1, dtype=int)

C np.full([3, 3], -1.0, dtype=int)

D np.full((3, 3), -1, dtype='float')

💡 Hint

Check the shape format and dtype for integer type.

Option B uses a tuple for shape, integer fill value, and dtype=int which is correct. Option B uses string fill value, C uses list for shape which is allowed but less common, D uses float dtype which is not integer.

🧠 Conceptual

expert

Memory behavior of np.full() with mutable fill value

What happens if you run this code?

import numpy as np
arr = np.full((2,), [0])
arr[0].append(1)
print(arr)

NumPy

import numpy as np
arr = np.full((2,), [0])
arr[0].append(1)
print(arr)

A Raises TypeError because fill_value is a list

B First element is [0, 1], second is [0] because lists are copied

C Both elements show [0, 1] because the same list object is used

D Prints [[0] [0]] unchanged

💡 Hint

Think about how np.full handles mutable objects as fill_value.

np.full uses the same object reference for each element if fill_value is mutable. So modifying one element modifies all references.

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