NumPy array foundation review in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

SciPy

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

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

Arange

Blist

Cmatrix

Darray

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.list instead of np.array

Trying to use np.range which creates sequences, not arrays

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

2fill in blank

medium

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

SciPy

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

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

Ashape

Blen

Cdim

Dsize

Attempts:

3 left

💡 Hint

Common Mistakes

Using size which returns total elements, not shape

Trying to call len() which only returns the first dimension

The shape attribute returns 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 filled with zeros.

SciPy

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

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

A5

B(5, 5)

C[5]

D5, 5

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single integer instead of a tuple

Passing a list instead of a tuple

Passing two separate arguments instead of a tuple

The np.zeros function requires a tuple to specify the shape of the array.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

SciPy

words = ['apple', 'bat', 'cat', 'doggy']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword

Dlen(word) < 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length

Using the wrong condition like less than 3

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths if length is greater than 2.

SciPy

words = ['hi', 'hello', 'hey']
result = { [1]: [2] for word in words if [3] }

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

Aword.upper()

Blen(word)

Clen(word) > 2

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of uppercase

Not filtering words by length

Mapping words instead of their lengths

The comprehension maps the uppercase version of each word to its length, filtering words longer than 2 characters.

Practice

1. What is a key advantage of using a NumPy array over a regular Python list for storing numbers?

easy

A. NumPy arrays automatically sort the data

B. NumPy arrays can store different data types in the same array

C. NumPy arrays use less memory and allow faster math operations

D. NumPy arrays are slower but more flexible than lists

Solution

1. Step 1: Understand memory and speed benefits

   NumPy arrays store data in a compact way and use optimized code for math.

2. Step 2: Compare with Python lists

   Python lists store references and are slower for math operations.

3. Final Answer:

   NumPy arrays use less memory and allow faster math operations -> Option C

4. Quick Check:

   NumPy arrays = efficient storage + fast math [OK]

Hint: NumPy arrays are built for fast math and memory efficiency [OK]

Common Mistakes:

• Thinking NumPy arrays can store mixed data types like lists
• Assuming NumPy arrays sort data automatically
• Believing NumPy arrays are slower than lists

2. Which of the following is the correct way to create a 1-dimensional NumPy array with elements 1, 2, and 3?

easy

A. np.array(1, 2, 3)

B. np.array([1, 2, 3])

C. np.array{1, 2, 3}

D. np.array([1; 2; 3])

Solution

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

   NumPy arrays are created by passing a list inside parentheses: np.array([elements]).

2. Step 2: Check each option

   np.array([1, 2, 3]) uses correct syntax with square brackets inside parentheses. Others have syntax errors.

3. Final Answer:

   np.array([1, 2, 3]) -> Option B

4. Quick Check:

   np.array needs a list inside parentheses [OK]

Hint: Use square brackets inside parentheses to create arrays [OK]

Common Mistakes:

• Omitting square brackets around elements
• Using curly braces instead of square brackets
• Separating elements with semicolons inside list

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. (2, 2)

B. (4,)

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: Confirm shape output

   Printing arr.shape returns a tuple with dimensions (2, 2).

3. Final Answer:

   (2, 2) -> Option A

4. Quick Check:

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

Hint: Shape shows (rows, columns) for 2D arrays [OK]

Common Mistakes:

• Confusing total elements with shape
• Expecting a flat tuple like (4,)
• Misreading rows and columns order

4. Identify the error in this code snippet:

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

medium

A. IndexError because index 3 is out of bounds

B. SyntaxError due to wrong array creation

C. TypeError because arr is not subscriptable

D. No error, prints 3

Solution

1. Step 1: Check array length and indexing

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

2. Step 2: Understand error type

   Accessing arr[3] causes IndexError for out-of-bounds access.

3. Final Answer:

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

4. Quick Check:

   Index 3 invalid for length 3 array [OK]

Hint: Array indices start at 0; max index is length-1 [OK]

Common Mistakes:

• Thinking index 3 accesses the last element
• Confusing syntax error with runtime error
• Assuming arr[3] prints 3

5. Given a 2D NumPy array arr = np.array([[1, 2, 3], [4, 5, 6]]), which code correctly extracts the second column as a 1D array?

hard

A. arr[:, 2]

B. arr[1, :]

C. arr[1]

D. arr[:, 1]

Solution

1. Step 1: Understand array slicing for columns

   To get a column, select all rows (:) and the column index.

2. Step 2: Identify correct column index

   Second column has index 1 (zero-based indexing).

3. Step 3: Check options

   arr[:, 1] uses arr[:, 1] which selects all rows, column 1 correctly.

4. Final Answer:

   arr[:, 1] -> Option D

5. Quick Check:

   All rows, column 1 = arr[:, 1] [OK]

Hint: Use arr[:, col_index] to get a column [OK]

Common Mistakes:

• Using arr[1, :] which selects a row, not a column
• Using arr[1] which selects second row
• Using wrong column index