ndarray as the core data structure in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of ndarray shape and size

What is the output of the following code snippet?

NumPy

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

A (3, 2) 6

B (2, 3) 6

C (2, 3) 5

D (3, 2) 5

💡 Hint

Shape gives rows and columns, size is total elements.

The array has 2 rows and 3 columns, so shape is (2, 3). Total elements are 6.

❓ data_output

intermediate

Result of ndarray slicing

What is the output of slicing the array below?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])

A [20 30 40 50]

B [10 20 30]

C [30 40 50]

D [20 30 40]

💡 Hint

Slicing excludes the last index.

Slicing from index 1 to 4 includes elements at positions 1, 2, and 3.

🔧 Debug

advanced

Identify the error in ndarray reshaping

What error does this code raise?

NumPy

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

A ValueError: cannot reshape array of size 5 into shape (2,3)

B TypeError: reshape argument must be tuple

C IndexError: index out of range

D No error, reshaping works

💡 Hint

Total elements must match new shape.

The array has 5 elements but reshape tries to make shape (2,3) which needs 6 elements.

❓ visualization

advanced

Visualize the effect of ndarray transpose

What is the output of transposing this array?

NumPy

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

A

[[1 3 5]
 [2 4 6]]

B

[[1 2 3]
 [4 5 6]]

C

[[1 2]
 [3 4]
 [5 6]]

D

[[6 5 4]
 [3 2 1]]

💡 Hint

Transpose flips rows and columns.

Original shape is (3,2), transpose shape is (2,3) with rows and columns swapped.

🧠 Conceptual

expert

Understanding ndarray broadcasting rules

Given arrays a with shape (3,1) and b with shape (1,4), what is the shape of a + b?

A (3, 1)

B (1, 1)

C (3, 4)

D (1, 4)

💡 Hint

Broadcasting matches dimensions by expanding singleton dimensions.

Shapes (3,1) and (1,4) broadcast to (3,4) for element-wise addition.

Practice

1.

What is the main purpose of a numpy ndarray?

easy

A. To display images on screen

B. To create text documents

C. To store numbers in one or more dimensions for fast math operations

D. To connect to databases

Solution

1. Step 1: Understand what ndarray stores

   ndarray is designed to hold numbers arranged in rows and columns or more dimensions.

2. Step 2: Recognize its use for math

   It allows fast math operations on many numbers at once, unlike regular Python lists.

3. Final Answer:

   To store numbers in one or more dimensions for fast math operations -> Option C

4. Quick Check:

   ndarray = fast number storage [OK]

Hint: ndarray = fast multi-dimensional number storage [OK]

Common Mistakes:

• Thinking ndarray stores text or images
• Confusing ndarray with database tools
• Assuming ndarray is for display only

2.

Which of the following is the correct way to create a 1D ndarray from a Python list [1, 2, 3]?

import numpy as np
# Which line is correct?

easy

A. arr = np.array([1, 2, 3])

B. arr = np.ndarray([1, 2, 3])

C. arr = np.array(1, 2, 3)

D. arr = np.array{1, 2, 3}

Solution

1. Step 1: Recall correct syntax for np.array()

   np.array() takes a list or nested lists inside parentheses.

2. Step 2: Check each option

   arr = np.array([1, 2, 3]) uses correct syntax with square brackets inside parentheses. Others have wrong function calls or braces.

3. Final Answer:

   arr = np.array([1, 2, 3]) -> Option A

4. Quick Check:

   np.array(list) syntax = correct [OK]

Hint: Use np.array() with list inside parentheses [OK]

Common Mistakes:

• Using np.ndarray() directly without parameters
• Passing multiple arguments instead of one list
• Using curly braces {} instead of []

3.

What will be the output of the following code?

import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(arr.shape)

medium

A. (4,)

B. (2, 2)

C. (1, 4)

D. (2,)

Solution

1. Step 1: Understand the array shape

   The array has 2 rows and 2 columns, so shape is (2, 2).

2. Step 2: Check print output

   arr.shape returns a tuple with dimensions, so output is (2, 2).

3. Final Answer:

   (2, 2) -> Option B

4. Quick Check:

   2 rows and 2 columns = (2, 2) [OK]

Hint: Shape shows rows and columns as tuple [OK]

Common Mistakes:

• Confusing shape with total elements
• Expecting a list instead of tuple
• Mixing up rows and columns count

4.

Find the error in this code snippet:

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

medium

A. SyntaxError due to wrong print statement

B. No error, prints 3

C. TypeError because arr is not a list

D. IndexError because index 3 is out of bounds

Solution

1. Step 1: Check array length and indexing

   arr has elements at indices 0, 1, 2 only. Index 3 is out of range.

2. Step 2: Understand error type

   Accessing arr[3] causes IndexError because index is invalid.

3. Final Answer:

   IndexError because index 3 is out of bounds -> Option D

4. Quick Check:

   Index out of range = IndexError [OK]

Hint: Check array length before indexing [OK]

Common Mistakes:

• Thinking arr[3] returns last element
• Confusing IndexError with SyntaxError
• Assuming numpy arrays allow out-of-range access

5.

You have a nested list [[1, 2], [3, 4], [5, 6]]. How do you create a numpy ndarray and then get the sum of all elements?

import numpy as np
nested_list = [[1, 2], [3, 4], [5, 6]]
# Fill in the blanks
arr = np.array(nested_list)
total = _____
print(total)

hard

A. arr.sum()

B. sum(arr)

C. arr.add()

D. np.sum(arr, axis=1)

Solution

1. Step 1: Create ndarray from nested list

   np.array(nested_list) creates a 2D array with shape (3, 2).

2. Step 2: Use ndarray method to sum all elements

   arr.sum() returns sum of all elements in the array.

3. Final Answer:

   arr.sum() -> Option A

4. Quick Check:

   Use arr.sum() for total sum [OK]

Hint: Use arr.sum() to sum all elements [OK]

Common Mistakes:

• Using Python sum() which sums rows, not elements
• Using arr.add() which does not exist
• Using np.sum with axis=1 sums rows, not total