np.vectorize() for custom functions in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.vectorize() do in NumPy?

np.vectorize() turns a regular Python function into one that can be applied element-wise to arrays, like a loop but simpler to write.

beginner

How does np.vectorize() help with custom functions?

It lets you apply your own function to each element of an array without writing explicit loops.

intermediate

What is a limitation of np.vectorize() compared to true vectorized NumPy functions?

np.vectorize() is basically a loop under the hood, so it is not faster than a Python loop. It just makes code cleaner.

beginner

Example: How to use np.vectorize() to square each element in an array using a custom function?

<pre>import numpy as np

def square(x):
    return x * x

vec_square = np.vectorize(square)
arr = np.array([1, 2, 3])
result = vec_square(arr)
print(result)  # Output: [1 4 9]</pre>

intermediate

Can np.vectorize() handle functions with multiple inputs?

Yes, you can pass multiple arrays as arguments, and np.vectorize() will apply the function element-wise across all inputs.

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

A To apply a function element-wise to arrays

B To speed up NumPy operations

C To create new NumPy arrays

D To sort arrays

np.vectorize() applies a function element-wise, making it easier to work with arrays.

Does np.vectorize() improve performance compared to a Python loop?

A Yes, it is much faster

B Only for large arrays

C No, it is similar in speed

D It depends on the array size

np.vectorize() is a convenience tool and does not speed up execution compared to loops.

Which of these is a correct way to create a vectorized function from def add(x, y): return x + y?

A vec_add = np.add(add)

B vec_add = np.vectorize(add)

C vec_add = np.vectorize(x + y)

D vec_add = np.vectorize()

You pass the function add to np.vectorize() to create vec_add.

What happens if you pass multiple arrays to a vectorized function?

A It applies the function element-wise across all arrays

B It only uses the first array

C It raises an error

D It concatenates the arrays

np.vectorize() applies the function element-wise to all input arrays.

Which statement is true about np.vectorize()?

A It replaces all NumPy vectorized functions

B It only works with built-in NumPy functions

C It always improves code speed

D It is a convenience wrapper for element-wise operations

np.vectorize() is a convenience tool to apply custom functions element-wise.

Explain how np.vectorize() works and when you might use it.

Think about applying your own function to each item in a list or array.

Describe a simple example using np.vectorize() with a custom function that takes two inputs.

Imagine adding or multiplying two arrays element by element.