np.vectorize() for custom functions in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a vectorized version of the function square.

NumPy

import numpy as np

def square(x):
    return x ** 2

vectorized_square = np.vectorize([1])

result = vectorized_square(np.array([1, 2, 3]))
print(result)

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

Alambda x: x*2

Bnp.square

Cnp.array

Dsquare

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a NumPy function like np.square instead of the custom function.

Passing a lambda that does not match the intended operation.

Passing np.array instead of a function.

The np.vectorize() function takes a Python function as input and returns a vectorized version of it. Here, we pass the square function to vectorize it.

2fill in blank

medium

Complete the code to vectorize a function that returns 'even' or 'odd' for each number.

NumPy

import numpy as np

def even_or_odd(x):
    if x % 2 == 0:
        return 'even'
    else:
        return 'odd'

vec_func = np.vectorize([1])

result = vec_func(np.array([1, 2, 3, 4]))
print(result)

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

Anp.vectorize

Beven_or_odd

Clambda x: x % 2

Dnp.array

Attempts:

3 left

💡 Hint

Common Mistakes

Passing np.vectorize itself instead of the function.

Passing a lambda that returns a number instead of 'even' or 'odd'.

Passing np.array instead of a function.

We pass the custom function even_or_odd to np.vectorize() to create a vectorized version that works element-wise on arrays.

3fill in blank

hard

Fix the error in the code by filling the blank to correctly vectorize the function add_one.

NumPy

import numpy as np

def add_one(x):
    return x + 1

vec_add_one = np.vectorize([1])

arr = np.array([10, 20, 30])
result = vec_add_one(arr)
print(result)

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

Aadd_one

Badd_one()

Cnp.add

Dlambda x: x + 1

Attempts:

3 left

💡 Hint

Common Mistakes

Using add_one() instead of add_one.

Passing np.add which is a different function.

Passing a lambda instead of the named function.

You must pass the function object add_one without calling it (no parentheses) to np.vectorize().

4fill in blank

hard

Fill both blanks to create a vectorized function that returns True if a number is greater than 5 and False otherwise.

NumPy

import numpy as np

def greater_than_five(x):
    return x [1] 5

vec_func = np.vectorize(greater_than_five)

arr = np.array([3, 6, 9])
result = vec_func(arr) == [2]
print(result)

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

A>

BTrue

CFalse

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' in the function.

Comparing the result to False instead of True.

Using '==' inside the function instead of '>'.

The function checks if x > 5. The vectorized function returns boolean values. We compare the result to True to confirm the condition.

5fill in blank

hard

Fill all three blanks to create a vectorized function that returns the length of a string if it is longer than 3 characters, else returns 0.

NumPy

import numpy as np

def length_if_long(word):
    return len(word) if len(word) [1] 3 else [2]

vec_length = np.vectorize([3])

words = np.array(['cat', 'elephant', 'dog'])
result = vec_length(words)
print(result)

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

A>

B0

Clength_if_long

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' instead of '>' in the condition.

Returning the word instead of 0 in the else part.

Passing a lambda instead of the function name to np.vectorize.

The function returns the length of the word if its length is greater than 3, otherwise returns 0. We vectorize the function by passing its name to np.vectorize().