Universal functions (ufuncs) in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a numpy array and apply the square root ufunc.

NumPy

import numpy as np
arr = np.array([1, 4, 9, 16])
sqrt_arr = np.[1](arr)
print(sqrt_arr)

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

Aexp

Bsqrt

Clog

Dsquare

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'square' instead of 'sqrt' which squares the numbers instead of square rooting.

Using 'log' or 'exp' which perform different mathematical operations.

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

2fill in blank

medium

Complete the code to apply the numpy ufunc that calculates the exponential of each element.

NumPy

import numpy as np
arr = np.array([0, 1, 2])
exp_arr = np.[1](arr)
print(exp_arr)

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

Alog

Bsqrt

Cabs

Dexp

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'log' which calculates the logarithm, not the exponential.

Using 'sqrt' which calculates square roots.

The exp function calculates the exponential (e^x) of each element in the array.

3fill in blank

hard

Fix the error in the code by choosing the correct numpy ufunc to calculate the absolute values.

NumPy

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

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

Aabs

Bfabs

Cabsolute

Dsign

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'abs', which is an alias but the full ufunc name is 'absolute'.

Using 'sign' which returns the sign of each element, not the absolute value.

Using 'fabs' which is specifically for floating-point absolute value.

The correct numpy ufunc for absolute values is absolute (with abs as an alias).

4fill in blank

hard

Complete the code to apply the numpy ufunc that calculates the base-10 logarithm of each element.

NumPy

import numpy as np
arr = np.array([1, 10, 100])
log_arr = np.[1](arr)
print(log_arr)

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

Alog

Bsqrt

Clog10

Dexp

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'log' which computes the natural logarithm (base e).

Using 'exp' or 'sqrt' which perform different operations.

The log10 ufunc computes the base-10 logarithm of each element in the array.

5fill in blank

hard

Complete the code to apply the floor ufunc, which rounds each element down to the nearest integer.

NumPy

import numpy as np
arr = np.array([0.7, 1.5, -1.8, -0.1])
floor_arr = np.[1](arr)
print(floor_arr)

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

Afloor

Bceil

Crint

Dfix

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ceil' which rounds up to the nearest integer.

Using 'fix' which rounds towards zero.

The floor ufunc rounds each element towards negative infinity to the nearest integer.

Practice

1. What is the main purpose of NumPy universal functions (ufuncs)?

easy

A. To convert arrays into lists

B. To perform element-wise operations on arrays efficiently

C. To sort arrays in ascending order

D. To create new arrays from scratch

Solution

1. Step 1: Understand what ufuncs do

   Ufuncs apply mathematical operations element-wise on arrays, making calculations fast and simple.

2. Step 2: Compare with other options

   Creating arrays, sorting, or converting to lists are not the main roles of ufuncs.

3. Final Answer:

   To perform element-wise operations on arrays efficiently -> Option B

4. Quick Check:

   Ufuncs = element-wise fast operations [OK]

Hint: Remember: ufuncs work element-wise on arrays fast [OK]

Common Mistakes:

• Confusing ufuncs with array creation functions
• Thinking ufuncs sort arrays
• Assuming ufuncs convert arrays to lists

2. Which of the following is the correct syntax to use the NumPy universal function for square root on array arr?

easy

A. np.sqrt(arr)

B. np.square(arr)

C. np.root(arr)

D. np.sqrt_of(arr)

Solution

1. Step 1: Identify the correct ufunc for square root

   The NumPy function for square root is np.sqrt().

2. Step 2: Check the syntax with the array

   Calling np.sqrt(arr) applies square root element-wise on arr.

3. Final Answer:

   np.sqrt(arr) -> Option A

4. Quick Check:

   Square root ufunc = np.sqrt() [OK]

Hint: Square root ufunc is always np.sqrt(array) [OK]

Common Mistakes:

• Using np.square instead of np.sqrt
• Trying non-existent functions like np.root
• Adding extra words in function name

3. What is the output of the following code?

import numpy as np
arr = np.array([1, 4, 9, 16])
result = np.sqrt(arr)
print(result)

medium

A. [1. 2. 3. 4.]

B. [1 2 3 4]

C. [1. 4. 9. 16.]

D. [0. 2. 3. 4.]

Solution

1. Step 1: Understand np.sqrt on array elements

   Applying np.sqrt on each element: sqrt(1)=1.0, sqrt(4)=2.0, sqrt(9)=3.0, sqrt(16)=4.0.

2. Step 2: Check output format

   NumPy returns floats for sqrt, so output is [1. 2. 3. 4.].

3. Final Answer:

   [1. 2. 3. 4.] -> Option A

4. Quick Check:

   np.sqrt([1,4,9,16]) = [1. 2. 3. 4.] [OK]

Hint: Square root returns floats, watch decimal points [OK]

Common Mistakes:

• Expecting integer output instead of float
• Confusing np.sqrt with np.square
• Misreading array output format

4. The following code throws an error. What is the problem?

import numpy as np
arr = np.array([1, 2, 3])
result = np.add(arr, 5, 10)
print(result)

medium

A. np.add cannot add a scalar to an array

B. The array must be of float type for np.add

C. np.add takes only two arguments, but three were given

D. The print statement is incorrect

Solution

1. Step 1: Check np.add function signature

   np.add takes exactly two arguments: two arrays or an array and a scalar.

2. Step 2: Identify the error in arguments

   Here, three arguments are passed: arr, 5, and 10, which is invalid.

3. Final Answer:

   np.add takes only two arguments, but three were given -> Option C

4. Quick Check:

   np.add = 2 args only [OK]

Hint: np.add needs exactly two inputs, no extras [OK]

Common Mistakes:

• Passing more than two arguments to np.add
• Thinking np.add requires float arrays
• Blaming print instead of function call

5. You have two arrays:

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

Which code correctly computes the element-wise product and then applies the square root to each product?

hard

A. np.multiply(a, b) + np.sqrt(a)

B. np.multiply(np.sqrt(a), np.sqrt(b))

C. np.sqrt(a) * np.sqrt(b)

D. np.sqrt(np.multiply(a, b))

Solution

1. Step 1: Understand element-wise product and sqrt

   First multiply elements of a and b, then apply square root on the result.

2. Step 2: Check each option

   np.sqrt(np.multiply(a, b)) does np.multiply(a, b) then np.sqrt(), which is correct. Options B and C apply sqrt before multiply, which is different. np.multiply(a, b) + np.sqrt(a) adds sqrt(a), which is incorrect.

3. Final Answer:

   np.sqrt(np.multiply(a, b)) -> Option D

4. Quick Check:

   Multiply then sqrt = np.sqrt(np.multiply(a,b)) [OK]

Hint: Multiply first, then sqrt for correct order [OK]

Common Mistakes:

• Applying sqrt before multiplication
• Adding instead of multiplying
• Mixing order of operations