applymap() for DataFrame-wide operations in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the applymap() function do in pandas?

applymap() applies a given function to every single element in a DataFrame.

It works element-wise across the whole table.

beginner

How is applymap() different from apply() in pandas?

applymap() works on each element individually in the whole DataFrame.

apply() works on rows or columns (Series) instead.

beginner

Write a simple example of using applymap() to square every number in a DataFrame.

df.applymap(lambda x: x**2)

This squares each number in the DataFrame.

beginner

Can applymap() be used on a pandas Series?

No, applymap() is only for DataFrames.

For Series, use map() or apply().

beginner

Why might you use applymap() instead of looping through DataFrame elements manually?

applymap() is simpler and usually faster than manual loops.

It keeps your code clean and easy to read.

What does applymap() apply a function to?

A Each element in the DataFrame

B Each row in the DataFrame

C Each column in the DataFrame

D Only numeric columns

applymap() applies a function to every single element in the DataFrame.

Which pandas method would you use to apply a function to each row or column instead of each element?

A map()

B applymap()

C apply()

D transform()

apply() works on rows or columns (Series), not element-wise.

If you want to convert all strings in a DataFrame to lowercase, which method is best?

A map(str.lower)

B apply(str.lower)

C transform(str.lower)

D applymap(str.lower)

applymap() applies the function to each element, perfect for element-wise string operations.

Can applymap() be used on a pandas Series?

A No, use <code>map()</code> or <code>apply()</code> instead

B Yes, it works the same as on DataFrames

C Only if the Series has numeric data

D Only if the Series has string data

applymap() is only for DataFrames; Series use map() or apply().

What is a benefit of using applymap() over manual loops?

A It is slower but more readable

B It is faster and keeps code clean

C It only works on numeric data

D It requires less memory

applymap() is usually faster and makes code easier to read than manual loops.

Explain how applymap() works on a pandas DataFrame and give a simple example.

Think about applying a function to every cell in a table.

Compare applymap() and apply() in pandas. When would you use each?

Consider if you want to change each cell or whole rows/columns.