Why math functions matter in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the square root of each number in the array.

NumPy

import numpy as np
numbers = np.array([4, 9, 16, 25])
sqrt_values = np.[1](numbers)
print(sqrt_values)

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

Asqrt

Bsquare

Clog

Dexp

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.square instead of np.sqrt, which squares the numbers instead of finding roots.

Using np.log or np.exp which calculate logarithm or exponentials, not roots.

The np.sqrt function calculates the square root of each element in the array.

2fill in blank

medium

Complete the code to calculate the natural logarithm of each number in the array.

NumPy

import numpy as np
values = np.array([1, 10, 100, 1000])
log_values = np.[1](values)
print(log_values)

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

Aexp

Bsqrt

Cabs

Dlog

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sqrt which calculates square roots, not logarithms.

Using np.exp which calculates exponentials, the opposite of logarithms.

The np.log function calculates the natural logarithm (base e) of each element.

3fill in blank

hard

Fix the error in the code to calculate the exponential of each number in the array.

NumPy

import numpy as np
values = np.array([0, 1, 2, 3])
exp_values = np.[1](values)
print(exp_values)

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

Alog

Bexp

Csqrt

Dabs

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.log which calculates logarithms, not exponentials.

Using np.sqrt which calculates square roots.

The np.exp function calculates the exponential (e to the power) of each element.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

B<=

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' instead of '>' which would select words of length 3 or less.

Using 'word' instead of 'len(word)' for the dictionary values.

The dictionary comprehension uses len(word) to get the length and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = { [1]: [2] for word in words if len(word) [3] 3 }
print(result)

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

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of 'word.upper()' for keys.

Using '<=' instead of '>' which would select shorter words.

Using 'word' instead of 'len(word)' for values.

The dictionary comprehension uses word.upper() as keys, len(word) as values, and filters words longer than 3 with >.

Practice

1. Why do we use math functions like np.sum() or np.mean() in data science?

easy

A. To quickly calculate important values from data arrays

B. To create new arrays from scratch

C. To change the data type of arrays

D. To sort the data alphabetically

Solution

1. Step 1: Understand the purpose of math functions

   Math functions like np.sum() and np.mean() help us find total or average values quickly.

2. Step 2: Recognize their use in data analysis

   These functions work on arrays to give useful summary numbers fast, which is key in data science.

3. Final Answer:

   To quickly calculate important values from data arrays -> Option A

4. Quick Check:

   Math functions = fast calculations [OK]

Hint: Math functions summarize data fast, like sum or average [OK]

Common Mistakes:

• Thinking math functions create new arrays
• Confusing math functions with sorting
• Believing math functions change data types

2. Which of the following is the correct way to use the NumPy function to find the maximum value in an array arr?

easy

A. np.max(arr)

B. arr.max()

C. max(arr)

D. np.maximum(arr)

Solution

1. Step 1: Identify the correct NumPy function syntax

   The function to find the max value in NumPy is np.max(), which takes the array as argument.

2. Step 2: Check other options for correctness

   arr.max() works but is a method, not a function call; max(arr) is Python built-in, not NumPy; np.maximum(arr) requires two arrays, so incorrect here.

3. Final Answer:

   np.max(arr) -> Option A

4. Quick Check:

   Use np.max(array) for max value [OK]

Hint: Use np.max(array) to get max value quickly [OK]

Common Mistakes:

• Using np.maximum with one array instead of two
• Confusing Python max() with NumPy max()
• Using method arr.max() when function np.max() is asked

3. What is the output of the following code?

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

medium

A. [1 2 3 4]

B. [1. 1.41421356 1.73205081 2.]

C. [1. 2. 3. 4.]

D. Error: sqrt() not defined for arrays

Solution

1. Step 1: Understand np.sqrt() on arrays

   NumPy's np.sqrt() calculates the square root of each element in the array individually.

2. Step 2: Calculate square roots of each element

   Square roots: sqrt(1)=1.0, sqrt(2)=1.41421356, sqrt(3)=1.73205081, sqrt(4)=2.0.

3. Final Answer:

   [1. 1.41421356 1.73205081 2.] -> Option B

4. Quick Check:

   np.sqrt(array) = element-wise roots [OK]

Hint: np.sqrt(array) returns roots for each element [OK]

Common Mistakes:

• Expecting sqrt to return original array
• Thinking sqrt only works on single numbers
• Assuming sqrt causes an error on arrays

4. The following code is intended to calculate the mean of an array, but it causes an error. What is the problem?

import numpy as np
arr = np.array([10, 20, 30])
mean_val = np.mean arr
print(mean_val)

medium

A. print statement is incorrect

B. Array is not defined correctly

C. np.mean cannot be used on arrays

D. Missing parentheses after np.mean

Solution

1. Step 1: Check syntax of np.mean usage

   The function np.mean requires parentheses around the argument, like np.mean(arr).

2. Step 2: Identify the error in the code

   The code uses np.mean arr without parentheses, causing a syntax error.

3. Final Answer:

   Missing parentheses after np.mean -> Option D

4. Quick Check:

   Functions need parentheses: np.mean(arr) [OK]

Hint: Always use parentheses when calling functions [OK]

Common Mistakes:

• Forgetting parentheses on function calls
• Thinking np.mean can't handle arrays
• Misreading print syntax as error

5. You have an array of temperatures in Celsius: temps = np.array([0, 20, 37, 100]). You want to convert them to Fahrenheit using the formula F = C * 9/5 + 32. Which NumPy code correctly applies this math function to all elements?

hard

A. fahrenheit = temps * 9 / (5 + 32)

B. fahrenheit = np.add(temps, 32) * 9 / 5

C. fahrenheit = np.multiply(temps, 9/5) + 32

D. fahrenheit = temps + 32 * 9 / 5

Solution

1. Step 1: Understand the formula and vectorized operations

   The formula is F = C * 9/5 + 32. NumPy allows element-wise multiplication and addition.

2. Step 2: Check each option for correct order and operations

   fahrenheit = temps * 9 / (5 + 32) misplaces parentheses causing incorrect calculation. fahrenheit = np.add(temps, 32) * 9 / 5 adds 32 before multiplying, wrong order. fahrenheit = np.multiply(temps, 9/5) + 32 uses np.multiply for multiplication then adds 32, correct vectorized math. fahrenheit = temps + 32 * 9 / 5 adds 32 * 9/5 to temps, wrong formula.

3. Step 3: Choose the best NumPy function usage

   fahrenheit = np.multiply(temps, 9/5) + 32 explicitly uses NumPy math functions correctly and clearly.

4. Final Answer:

   fahrenheit = np.multiply(temps, 9/5) + 32 -> Option C

5. Quick Check:

   Use np.multiply(array, factor) + addend [OK]

Hint: Use np.multiply(array, factor) + addend for formulas [OK]

Common Mistakes:

• Adding before multiplying in formula
• Using Python operators without vectorization
• Misplacing parentheses causing wrong order