ndarray as the core data structure in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array from a Python list.

NumPy

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

Drag options to blanks, or click blank then click option'

Aarray

Blist

Cndarray

Dmatrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.list instead of np.array

Using np.matrix which creates a different object

Trying to use np.ndarray directly without arguments

The np.array function converts a Python list into a NumPy ndarray.

2fill in blank

medium

Complete the code to find the shape of the NumPy array.

NumPy

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

Drag options to blanks, or click blank then click option'

Asize

Bdim

Clength

Dshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using size which returns total elements, not shape

Using length which is not an ndarray attribute

Using dim which is not a valid attribute

The shape attribute shows the dimensions of the array as a tuple.

3fill in blank

hard

Fix the error in the code to correctly create a 2D NumPy array.

NumPy

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

Drag options to blanks, or click blank then click option'

A[1, [2, 3, 4]]

B[1, 2, 3, 4]

C[[1, 2], [3, 4]]

D[[1, 2, 3], 4]

Attempts:

3 left

💡 Hint

Common Mistakes

Using uneven nested lists causing ragged arrays

Using a flat list when a 2D array is needed

Mixing data types inside the list

A 2D array requires a list of lists with equal lengths. Option C is correct.

4fill in blank

hard

Fill both blanks to create a 3x3 array of zeros and print its data type.

NumPy

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

Drag options to blanks, or click blank then click option'

Azeros

Bones

Cint

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.ones instead of zeros

Using int dtype when float is expected

Forgetting to specify shape as a tuple

np.zeros creates an array of zeros. Setting dtype=float makes elements floats.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its length if length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = { [1]: [2] for word in words if [3] }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using word > 3 which compares string to number

Using len(word) without comparison in if condition

Swapping key and value positions

The dictionary comprehension uses the word as key, its length as value, and filters words longer than 3 characters.

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