Aggregation with agg() in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the agg() function do in pandas?

The agg() function lets you apply one or more aggregation operations like sum, mean, or max on DataFrame columns.

beginner

How can you apply multiple aggregation functions to a single column using agg()?

You can pass a list of functions to agg(), like df['col'].agg(['sum', 'mean']), to get multiple results at once.

intermediate

Can agg() be used on grouped data in pandas?

Yes! After grouping data with groupby(), you can use agg() to apply aggregation functions to each group separately.

intermediate

How do you apply different aggregation functions to different columns using agg()?

Pass a dictionary to agg() where keys are column names and values are functions or lists of functions, e.g., df.agg({'col1': 'sum', 'col2': ['min', 'max']}).

beginner

What types of inputs can agg() accept for aggregation?

agg() accepts a single function name as a string, a list of functions, or a dictionary mapping columns to functions.

What will df['sales'].agg(['sum', 'mean']) return?

A Sum of all columns

B Sum and mean of the 'sales' column

C Mean of all columns

D Error because multiple functions are not allowed

Passing a list of functions to agg() applies each function to the specified column.

Which of these is a valid way to use agg() on a DataFrame?

A df.agg([col1, col2])

B df.agg('sum', 'mean')

C df.agg(col1='sum')

D df.agg({'col1': 'max', 'col2': ['min', 'mean']})

You can pass a dictionary mapping columns to functions or lists of functions to agg().

What happens if you use agg() after groupby()?

A DataFrame is flattened

B Aggregation functions are ignored

C Aggregation functions are applied to each group separately

D It causes an error

Using agg() after groupby() applies the functions to each group.

Which input is NOT valid for agg()?

A A DataFrame

B A list of function names

C A dictionary mapping columns to functions

D A single function name as a string

agg() expects functions or lists/dictionaries of functions, not a DataFrame.

How do you get the minimum and maximum of a column 'age' using agg()?

A df['age'].agg(['min', 'max'])

B df['age'].agg('min', 'max')

C df.agg('min', 'max')

D df['age'].agg({'min', 'max'})

Passing a list of function names as strings to agg() applies them all.

Explain how to use agg() to apply different aggregation functions to different columns in a pandas DataFrame.

Think about how to tell pandas what to do for each column separately.

Describe the role of agg() when used after groupby() in pandas.

Consider how grouping splits data and aggregation summarizes it.