np.vectorize() for custom functions in NumPy - Mini Project: Build & Apply

Using np.vectorize() for Custom Functions

📖 Scenario: Imagine you have a list of temperatures in Celsius from different cities. You want to convert all these temperatures to Fahrenheit using a custom formula.

🎯 Goal: You will create a custom function to convert Celsius to Fahrenheit, then use np.vectorize() to apply this function to a list of temperatures efficiently.

📋 What You'll Learn

Create a list of Celsius temperatures called celsius_temps with the values 0, 20, 37, 100

Create a custom function called c_to_f that converts Celsius to Fahrenheit using the formula F = C * 9/5 + 32

Use np.vectorize() to apply c_to_f to celsius_temps and store the result in fahrenheit_temps

Print the fahrenheit_temps array

💡 Why This Matters

🌍 Real World

Scientists and engineers often need to apply custom calculations to many data points quickly. Vectorizing functions helps speed up these operations.

💼 Career

Data scientists and analysts use vectorized operations to handle large datasets efficiently without writing slow loops.

1

Create the Celsius temperatures list

Create a list called celsius_temps with these exact values: 0, 20, 37, 100

NumPy

# Create the list of Celsius temperatures
# Your code here

Hint

Use square brackets to create a list with the given numbers separated by commas.

2

Define the Celsius to Fahrenheit function

Define a function called c_to_f that takes one parameter c and returns the Fahrenheit value using the formula c * 9/5 + 32

NumPy

celsius_temps = [0, 20, 37, 100]
# Define the function c_to_f below
# Your code here

Hint

Use def to create the function and return the calculated Fahrenheit value.

3

Use np.vectorize() to apply the function

Import numpy as np. Then create a variable called vectorized_c_to_f by applying np.vectorize() to the c_to_f function. Use vectorized_c_to_f to convert celsius_temps and store the result in fahrenheit_temps

NumPy

celsius_temps = [0, 20, 37, 100]
def c_to_f(c):
    return c * 9 / 5 + 32

# Import numpy and vectorize the c_to_f function
# Your code here

Hint

Remember to import numpy as np before using np.vectorize().

4

Print the Fahrenheit temperatures

Print the variable fahrenheit_temps to display the converted temperatures

NumPy

import numpy as np

celsius_temps = [0, 20, 37, 100]
def c_to_f(c):
    return c * 9 / 5 + 32

vectorized_c_to_f = np.vectorize(c_to_f)
fahrenheit_temps = vectorized_c_to_f(celsius_temps)

# Print the fahrenheit_temps array
# Your code here

Hint

Use print(fahrenheit_temps) to show the converted temperatures.

Practice

1. What is the main purpose of np.vectorize() in numpy?

easy

A. To apply a custom function element-wise on numpy arrays without explicit loops

B. To create random numbers in numpy arrays

C. To sort numpy arrays quickly

D. To reshape numpy arrays into different dimensions

Solution

1. Step 1: Understand the role of np.vectorize()

   np.vectorize() takes a function that works on single values and makes it work on arrays element-wise.

2. Step 2: Compare with other numpy functions

   It is not used for random number generation, sorting, or reshaping arrays, which are handled by other numpy functions.

3. Final Answer:

   To apply a custom function element-wise on numpy arrays without explicit loops -> Option A

4. Quick Check:

   np.vectorize() = element-wise function application [OK]

Hint: Remember: vectorize applies functions element-wise on arrays [OK]

Common Mistakes:

• Confusing vectorize with random number generation
• Thinking vectorize reshapes arrays
• Assuming vectorize sorts arrays

2. Which of the following is the correct way to create a vectorized version of a function f using numpy?

easy

A. vfunc = np.vectorize[f]

B. vfunc = np.vectorize(f)

C. vfunc = np.vectorize.f()

D. vfunc = np.vectorize->f

Solution

1. Step 1: Recall the syntax of np.vectorize

   The correct syntax is to call np.vectorize as a function with the target function as argument: np.vectorize(f).

2. Step 2: Check the other options for syntax errors

   Options A, C, and D use incorrect syntax like square brackets, dot notation, or arrows which are invalid in Python.

3. Final Answer:

   vfunc = np.vectorize(f) -> Option B

4. Quick Check:

   Correct syntax uses parentheses: np.vectorize(f) [OK]

Hint: Use parentheses, not brackets or dots, with np.vectorize [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Trying to access vectorize as an attribute
• Using invalid arrow syntax

3. What will be the output of the following code?

import numpy as np
def square_plus_one(x):
    return x**2 + 1
vfunc = np.vectorize(square_plus_one)
arr = np.array([1, 2, 3])
result = vfunc(arr)
print(result)

medium

A. [2 5 10]

B. [1 4 9]

C. [2 3 4]

D. [1 2 3]

Solution

1. Step 1: Understand the function logic

   The function square_plus_one returns x**2 + 1. For inputs 1, 2, 3, the outputs are 1^2+1=2, 2^2+1=5, 3^2+1=10.

2. Step 2: Apply vectorized function on array

   Using np.vectorize, the function applies element-wise on the array [1, 2, 3], producing [2, 5, 10].

3. Final Answer:

   [2 5 10] -> Option A

4. Quick Check:

   Element-wise square plus one = [2 5 10] [OK]

Hint: Apply function to each element, then collect results [OK]

Common Mistakes:

• Forgetting to add 1 after squaring
• Confusing output with input array
• Assuming vectorize changes function logic

4. Identify the error in this code snippet:

import numpy as np
def add_five(x):
    return x + 5
vfunc = np.vectorize(add_five)
arr = [1, 2, 3]
result = vfunc(arr)
print result

medium

A. np.vectorize cannot be used with functions that add numbers

B. The function add_five should return x * 5 instead of x + 5

C. The print statement is missing parentheses

D. The input array should be a numpy array, not a list

Solution

1. Step 1: Check the print statement syntax

   In Python 3, print is a function and requires parentheses like print(result). print result causes a SyntaxError.

2. Step 2: Verify other parts of the code

   The function logic is correct, np.vectorize works fine with addition functions, and input lists are accepted by np.vectorize.

3. Final Answer:

   The print statement is missing parentheses -> Option C

4. Quick Check:

   print requires parentheses in Python 3 [OK]

Hint: print requires parentheses in Python 3 [OK]

Common Mistakes:

• Forgetting parentheses on print statement
• Assuming list input is invalid for vectorize
• Changing function logic unnecessarily

5. You want to apply a custom function that returns 'even' if a number is even and 'odd' if it is odd, on a numpy array. Which code correctly uses np.vectorize() to achieve this?

def even_odd(x):
    if x % 2 == 0:
        return 'even'
    else:
        return 'odd'
arr = np.array([1, 2, 3, 4])
# What next?

hard

A. result = np.apply_along_axis(even_odd, 1, arr)
print(result)

B. result = even_odd(arr)
print(result)

C. vfunc = np.vectorize(even_odd)
result = even_odd(arr.tolist())
print(result)

D. vfunc = np.vectorize(even_odd)
result = vfunc(arr)
print(result)

Solution

1. Step 1: Vectorize the custom function

   Use np.vectorize(even_odd) to create a vectorized version that works element-wise on arrays.

2. Step 2: Apply vectorized function on numpy array

   Call the vectorized function with the numpy array arr to get the result array of 'even'/'odd' strings.

3. Step 3: Check other options

   result = even_odd(arr) print(result) fails because even_odd does not support array input directly. vfunc = np.vectorize(even_odd) result = even_odd(arr.tolist()) print(result) fails because even_odd does not support list input. result = np.apply_along_axis(even_odd, 1, arr) print(result) fails with axis out of bounds for 1D array.

4. Final Answer:

   vfunc = np.vectorize(even_odd) result = vfunc(arr) print(result) -> Option D

5. Quick Check:

   Vectorize then apply on numpy array = vfunc = np.vectorize(even_odd) result = vfunc(arr) print(result) [OK]

Hint: Vectorize first, then apply on numpy array directly [OK]

Common Mistakes:

• Calling function directly on array without vectorize
• Calling non-vectorized function on list input
• Using invalid axis in apply_along_axis