NumPy and scientific computing ecosystem - 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

Cseries

Dmatrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'array' causes an AttributeError.

Using 'matrix' creates a different NumPy object, not a simple array.

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

2fill in blank

medium

Complete the code to calculate the mean of a NumPy array.

NumPy

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

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

Amode

Baverage

Cmean

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'average' is not a NumPy array method, it causes an AttributeError.

Using 'median' calculates the middle value, not the average.

The mean() method calculates the average value of the array elements.

3fill in blank

hard

Fix the error in the code to create a 2D NumPy array with shape (2, 3).

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, [1]]])
print(arr.shape)
# Fix the inner list to have equal length by adding [1]

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

A0

B6

C3

D7

Attempts:

3 left

💡 Hint

Common Mistakes

Adding a number larger than 3 changes the data meaning.

Leaving the lists uneven causes an array of lists, not a 2D array.

All inner lists must have the same length to form a proper 2D array. Adding 0 to the second list makes it length 3.

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.

NumPy

words = ['data', 'ai', 'science', 'ml']
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(words)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(words) instead of len(word) causes wrong lengths.

Using word instead of len(word) as the value.

The dictionary comprehension maps each word to its length using len(word). The condition len(word) > 3 filters words longer than 3 characters.

5fill in blank

hard

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

NumPy

words = ['cat', 'dog', 'a', 'bird']
result = { [1]: [2] for w in words if [3] }

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

Aw.upper()

Blen(w)

Clen(w) > 2

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of w.upper() changes the key format.

Using len(w) > 3 excludes words of length 3.

The dictionary comprehension uses w.upper() as keys, len(w) as values, and filters words with length greater than 2.

Practice

1. What is the main purpose of NumPy in scientific computing?

easy

A. To create graphical user interfaces

B. To write web applications

C. To manage databases

D. To provide fast and efficient array operations for numerical data

Solution

1. Step 1: Understand NumPy's role

   NumPy is designed to handle numerical data efficiently using arrays.

2. Step 2: Compare with other options

   Creating graphical user interfaces, managing databases, and writing web applications are unrelated to NumPy's purpose in scientific computing.

3. Final Answer:

   To provide fast and efficient array operations for numerical data -> Option D

4. Quick Check:

   NumPy = fast numerical arrays [OK]

Hint: NumPy is all about fast number arrays [OK]

Common Mistakes:

• Confusing NumPy with GUI or web tools
• Thinking NumPy manages databases
• Assuming NumPy is for general programming

2. Which of the following is the correct way to import NumPy with its common alias?

easy

A. import np as numpy

B. import numpy as np

C. from numpy import *

D. import numpy as numpy

Solution

1. Step 1: Recall standard import syntax

   The common and recommended way is 'import numpy as np' for short alias.

2. Step 2: Check other options

   'import np as numpy' has invalid syntax, 'import numpy as numpy' uses a redundant alias, 'from numpy import *' imports all without the np alias.

3. Final Answer:

   import numpy as np -> Option B

4. Quick Check:

   Standard alias = np [OK]

Hint: Use 'import numpy as np' for short and clear code [OK]

Common Mistakes:

• Using wrong alias syntax
• Importing all without alias
• Confusing alias and module names

3. What will be the output of this code?

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

medium

A. [2 4 6]

B. [1 2 3 1 2 3]

C. [3 4 5]

D. Error: unsupported operand

Solution

1. Step 1: Understand array multiplication

   Multiplying a NumPy array by a number multiplies each element by that number.

2. Step 2: Calculate element-wise multiplication

   Each element in [1, 2, 3] multiplied by 2 gives [2, 4, 6].

3. Final Answer:

   [2 4 6] -> Option A

4. Quick Check:

   Array * scalar = element-wise multiply [OK]

Hint: Array * number multiplies each element [OK]

Common Mistakes:

• Thinking it concatenates arrays
• Expecting addition instead of multiplication
• Confusing with Python list behavior

4. Identify the error in this code snippet:

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

medium

A. SyntaxError due to wrong print statement

B. It will add 2 to each element

C. TypeError because you cannot add string to array

D. It will concatenate '2' to each element

Solution

1. Step 1: Check operation types

   Adding a string '2' to a numeric NumPy array causes a type error.

2. Step 2: Understand error cause

   NumPy cannot add string to integer array elements directly.

3. Final Answer:

   TypeError because you cannot add string to array -> Option C

4. Quick Check:

   Array + string = TypeError [OK]

Hint: NumPy arrays need matching types for operations [OK]

Common Mistakes:

• Expecting automatic conversion
• Thinking it concatenates strings
• Ignoring type mismatch errors

5. You have two NumPy arrays:

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

How do you create a new array that contains the element-wise sum of a and b?

hard

A. np.add(a, b)

B. a + b + 1

C. np.concatenate((a, b))

D. a * b

Solution

1. Step 1: Understand element-wise addition

   NumPy's np.add function adds arrays element-wise.

2. Step 2: Check other options

   'a + b + 1' adds an extra 1, np.concatenate((a, b)) joins arrays end-to-end, 'a * b' multiplies elements pairwise.

3. Final Answer:

   np.add(a, b) -> Option A

4. Quick Check:

   np.add = element-wise sum [OK]

Hint: Use np.add for clear element-wise addition [OK]

Common Mistakes:

• Using + 1 mistakenly
• Confusing concatenate with add
• Using multiplication instead of addition