agg() for multiple aggregations in Data Analysis Python - 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 do you apply multiple aggregations to a single column using agg()?

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

intermediate

How can you apply different aggregations to different columns in a DataFrame?

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

beginner

What type of output does agg() produce when using multiple aggregations?

It returns a DataFrame where rows or columns represent the aggregation functions applied, showing results for each requested aggregation.

intermediate

Can you use custom functions with agg() for aggregation?

Yes, you can pass your own functions or lambda expressions to agg() to perform custom aggregation calculations.

What argument type does agg() accept to apply different aggregations on different columns?

A A boolean value

B A single string with function name

C A dictionary with column names as keys and list of functions as values

D A list of column names

To apply different aggregations on different columns, you pass a dictionary where keys are column names and values are lists of aggregation functions.

Which of these is a valid way to apply sum and mean aggregations on column 'A' using agg()?

A df['A'].agg(['sum', 'mean'])

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

C df['A'].agg('sum', 'mean')

D df.agg(['A', 'sum', 'mean'])

Passing a list of functions like ['sum', 'mean'] to agg() on a column applies both aggregations.

What will df.agg({'A': ['max'], 'B': ['min', 'mean']}) return?

A Aggregated results with max of A, min and mean of B

B Error because of multiple functions

C Only max of A

D Only min and mean of B

This applies max aggregation on column A and min and mean on column B, returning all results.

Can you use a lambda function inside agg()?

A No, only built-in functions are allowed

B Yes, lambda functions can be used for custom aggregation

C Only if the lambda returns a string

D Only if the lambda has no parameters

Custom functions including lambdas can be passed to agg() for flexible aggregation.

What type of object is returned by agg() when multiple aggregations are applied?

A A Series with original data

B A list of values

C A single scalar value

D A DataFrame with aggregation results

Multiple aggregations return a DataFrame showing each aggregation result clearly.

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

Think about how to tell pandas which functions to use on which columns.

Describe the output format you get when using agg() with multiple aggregation functions on a DataFrame.

Consider how the results are organized for easy reading.

Practice

1. What does the agg() function do when used on a pandas DataFrame?

easy

A. It merges two DataFrames together.

B. It filters rows based on a condition.

C. It applies one or more aggregation functions to columns and returns summary results.

D. It sorts the DataFrame by column values.

Solution

1. Step 1: Understand the purpose of agg()

   agg() is used to apply aggregation functions like sum, mean, count on DataFrame columns.

2. Step 2: Identify what agg() returns

   It returns a summary DataFrame or Series with the aggregated results for specified columns.

3. Final Answer:

   It applies one or more aggregation functions to columns and returns summary results. -> Option C

4. Quick Check:

   agg() = summary aggregation [OK]

Hint: Remember: agg() summarizes columns with functions fast [OK]

Common Mistakes:

• Thinking agg() filters or sorts data
• Confusing agg() with merge or join
• Expecting agg() to modify original data

2. Which of the following is the correct syntax to apply agg() with multiple functions on a DataFrame column named 'sales'?

easy

A. df['sales'].agg(['sum', 'mean'])

B. df.agg('sales', ['sum', 'mean'])

C. df.agg({'sales': 'sum', 'mean'})

D. df['sales'].agg('sum', 'mean')

Solution

1. Step 1: Check correct agg() syntax for multiple functions

   To apply multiple functions on one column, pass a list of function names inside agg() like agg(['sum', 'mean']).

2. Step 2: Validate the syntax options

   df['sales'].agg(['sum', 'mean']) uses df['sales'].agg(['sum', 'mean']), which is correct. Others have wrong argument order or structure.

3. Final Answer:

   df['sales'].agg(['sum', 'mean']) -> Option A

4. Quick Check:

   Multiple functions in list inside agg() [OK]

Hint: Use a list of functions inside agg() for multiple aggregations [OK]

Common Mistakes:

• Passing multiple functions as separate arguments
• Using dict without proper key-value pairs
• Wrong argument order in agg()

3. Given the DataFrame:

import pandas as pd
data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}
df = pd.DataFrame(data)
result = df.agg({'A': ['sum', 'max'], 'B': ['min', 'mean']})
print(result)

What is the output of print(result)?

medium

A. A B
sum 10.0 NaN
max 4.0 NaN
min NaN 5.0
mean NaN 6.5

B. A B
sum 10 5
max 4 8
min 1 5
mean 2.5 6.5

C. A B
sum 10 26
max 4 8
min NaN NaN
mean NaN NaN

D. A B
sum 10 5
max 4 7
min 5 6.5
mean 2.5 6.5

Solution

1. Step 1: Calculate aggregations for column 'A'

   Sum of A = 1+2+3+4 = 10, Max of A = 4.

2. Step 2: Calculate aggregations for column 'B'

   Min of B = 5, Mean of B = (5+6+7+8)/4 = 6.5.

3. Step 3: Understand agg() output format

   Output is a DataFrame with aggregation functions as index and columns as 'A' and 'B'. For functions not applied to a column, NaN appears.

4. Final Answer:

   A B sum 10.0 NaN max 4.0 NaN min NaN 5.0 mean NaN 6.5 -> Option A

5. Quick Check:

   agg() with dict of lists returns DataFrame with NaNs for missing aggregations [OK]

Hint: Calculate each agg function separately, then check output shape [OK]

Common Mistakes:

• Mixing up min and mean values
• Confusing rows and columns in output
• Expecting all functions for all columns

4. What is wrong with this code snippet?

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

Assuming df has columns 'col1' and 'col2'.

medium

A. You cannot mix single function strings and lists in the same agg dictionary.

B. agg() requires all functions to be passed as a list, even if only one.

C. The column names must be passed as a list, not a dictionary.

D. The syntax is correct; no error will occur.

Solution

1. Step 1: Check if mixing single function and list is allowed in agg()

   agg() accepts a dict where values can be a string (single function) or list (multiple functions).

2. Step 2: Confirm syntax validity

   Passing {'col1': 'sum', 'col2': ['mean', 'max']} is valid and works correctly.

3. Final Answer:

   The syntax is correct; no error will occur. -> Option D

4. Quick Check:

   agg() accepts mixed single and multiple functions [OK]

Hint: agg() dict values can be string or list of functions [OK]

Common Mistakes:

• Assuming all dict values must be lists
• Trying to pass columns as list instead of dict keys
• Confusing agg() with apply() syntax

5. You have a DataFrame df with columns 'score' and 'group'. You want to get the mean and standard deviation of 'score' for each 'group'. Which code correctly uses agg() to do this after grouping?

hard

A. df.groupby('group')['score'].agg('mean', 'std')

B. df.groupby('group').agg({'score': ['mean', 'std']})

C. df.agg({'score': ['mean', 'std']}, groupby='group')

D. df.groupby('score')['group'].agg(['mean', 'std'])

Solution

1. Step 1: Understand grouping and aggregation

   To get stats per group, first group by 'group', then aggregate 'score' with mean and std.

2. Step 2: Check each option

   df.groupby('group')['score'].agg('mean', 'std') passes functions as separate arguments, incorrect syntax. df.groupby('group').agg({'score': ['mean', 'std']}) groups by 'group' and applies agg on 'score' column correctly. df.agg({'score': ['mean', 'std']}, groupby='group') has invalid syntax. df.groupby('score')['group'].agg(['mean', 'std']) groups by 'score' which is wrong.

3. Step 3: Choose best option

   df.groupby('group').agg({'score': ['mean', 'std']}) is the correct way to get mean and std of 'score' per 'group'.

4. Final Answer:

   df.groupby('group').agg({'score': ['mean', 'std']}) -> Option B

5. Quick Check:

   Group by then agg with dict for columns [OK]

Hint: Group first, then agg with dict for multiple functions per column [OK]

Common Mistakes:

• Grouping by wrong column
• Passing groupby as argument to agg()
• Aggregating wrong columns