apply() function for custom logic in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the apply() function do in pandas?

The apply() function lets you run a custom function on each row or column of a DataFrame or on each element of a Series.

beginner

How do you apply a function to each row in a DataFrame?

Use df.apply(your_function, axis=1). The axis=1 means the function runs on each row.

beginner

What is the difference between axis=0 and axis=1 in apply()?

axis=0 applies the function to each column, while axis=1 applies it to each row.

beginner

Can apply() be used with lambda functions?

Yes! You can pass a lambda function directly to apply() for quick custom logic without defining a separate function.

intermediate

Why use apply() instead of a for loop?

apply() is often faster and cleaner than a for loop because it uses optimized internal code and works well with pandas data structures.

What does df.apply(func, axis=1) do?

A Applies func to each column

B Applies func to each element individually

C Applies func to the entire DataFrame at once

D Applies func to each row

axis=1 means the function runs on each row.

Which of these is a valid way to use apply() with a lambda function?

A All of the above

B df.apply(lambda x: x + 1, axis=1)

C df.apply(lambda x: x + 1, axis=0)

D df.apply(lambda x: x + 1)

You can use apply() with lambda functions and specify axis as needed.

What happens if you use apply() without specifying axis on a DataFrame?

A It applies the function to each row

B It applies the function to each column

C It throws an error

D It applies the function to the entire DataFrame

The default axis is 0, so it applies to each column.

Which is NOT a benefit of using apply()?

A Ability to use custom functions easily

B Cleaner code than loops

C Faster execution than vectorized operations

D Works well with pandas data structures

Vectorized operations are usually faster than apply().

If you want to apply a function to each element of a Series, which method is best?

A apply()

B map()

C applymap()

D groupby()

apply() works on Series elements; applymap() is for DataFrames.

Explain how to use the apply() function to run a custom calculation on each row of a DataFrame.

Think about how to tell pandas to work row by row.

Describe the difference between apply() with axis=0 and axis=1.

Consider what each axis means in a table.