Vectorization over loops in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is vectorization in the context of numpy?

Vectorization means performing operations on whole arrays at once instead of using loops to process elements one by one. It makes code faster and simpler.

beginner

Why is vectorization faster than using loops in numpy?

Because numpy uses optimized C code and parallel processing under the hood, it can handle whole arrays at once, avoiding slow Python loops.

beginner

Example: How to add two arrays element-wise using vectorization?

Use the '+' operator directly on numpy arrays. For example, if a = np.array([1,2]) and b = np.array([3,4]), then a + b gives np.array([4,6]).

intermediate

What is a common mistake when switching from loops to vectorized numpy code?

Trying to use Python loops inside numpy operations or forgetting that numpy operations work element-wise on arrays, not on single elements.

beginner

How does vectorization improve code readability?

Vectorized code is shorter and clearer because it expresses operations on whole arrays directly, avoiding complex loop structures.

What does vectorization help you avoid in numpy?

A Importing numpy

B Using numpy arrays

C Using functions

D Writing explicit loops over array elements

Vectorization lets you perform operations on whole arrays without writing loops.

Which of these is a vectorized operation in numpy?

A for i in range(len(a)): c[i] = a[i] + b[i]

B c = a + b

C while i < len(a): c[i] = a[i] + b[i]

D c = [] for x in a: c.append(x)

Adding two arrays directly with '+' is vectorized.

Why is vectorized numpy code usually faster?

A It uses optimized C code and avoids Python loops

B It uses Python loops

C It uses more memory

D It runs slower but looks nicer

Numpy vectorization uses fast C code and avoids slow Python loops.

What happens if you try to add two numpy arrays of different shapes without broadcasting?

A It raises an error

B It returns zeros

C It ignores the smaller array

D It works fine

Numpy raises a ValueError if shapes are incompatible for broadcasting.

Which numpy function can help apply a vectorized operation to each element?

A np.loop

B np.for_each

C np.vectorize

D np.iter

np.vectorize wraps a function to apply it element-wise on arrays.

Explain in your own words why vectorization is preferred over loops in numpy.

Think about how numpy handles arrays internally.

Describe how you would convert a loop that adds two arrays element-wise into a vectorized numpy operation.

Focus on replacing the loop with a single numpy expression.