Aggregation with agg() in Pandas

Introduction

Aggregation helps you summarize data by calculating values like sums or averages. The agg() function lets you do many summaries at once.

You want to find the average and maximum sales for each product.

You need to calculate multiple statistics like mean, sum, and count on a dataset.

You want to quickly summarize data columns with different functions.

You want to group data and apply different aggregations to each group.

You want to create a report showing key numbers from your data.

Syntax

Pandas

DataFrame.agg(func=None, axis=0, *args, **kwargs)

func can be a single function, a list of functions, or a dictionary mapping columns to functions.

You can apply different functions to different columns by passing a dictionary.

Examples

Calculate the sum of all numeric columns in the DataFrame.

Pandas

df.agg('sum')

Calculate minimum, maximum, and mean for all numeric columns.

Pandas

df.agg(['min', 'max', 'mean'])

Calculate sum of 'sales' column and mean of 'quantity' column.

Pandas

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

Sample Program

This code creates a small table of products with sales and quantity. Then it calculates the total and average sales and quantity using agg().

Pandas

import pandas as pd

data = {
    'product': ['A', 'B', 'A', 'B'],
    'sales': [100, 200, 150, 300],
    'quantity': [1, 3, 2, 4]
}
df = pd.DataFrame(data)

# Aggregate sum and mean for sales and quantity
result = df.agg({'sales': ['sum', 'mean'], 'quantity': ['sum', 'mean']})
print(result)

Important Notes

If you use agg() on a grouped DataFrame, it applies the functions to each group separately.

You can pass your own custom functions to agg() as well.

Summary

agg() helps summarize data with one or many functions.

You can apply different functions to different columns easily.

It works well with grouped data for detailed summaries.

Practice

1.

What does the agg() function do in pandas?

easy

A. It sorts the DataFrame rows by index.

B. It applies one or more aggregation functions to DataFrame columns.

C. It filters rows based on a condition.

D. It merges two DataFrames together.

Solution

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

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

2. Step 2: Compare with other options

   Sorting, filtering, and merging are done by other pandas functions, not agg().

3. Final Answer:

   It applies one or more aggregation functions to DataFrame columns. -> Option B

4. Quick Check:

   agg() = aggregation [OK]

Hint: Remember agg() means aggregate functions on columns [OK]

Common Mistakes:

• Confusing agg() with sorting or filtering functions
• Thinking agg() modifies row order
• Assuming agg() merges DataFrames

2.

Which of the following is the correct syntax to apply agg() with multiple functions on a DataFrame df?

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

easy

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

B. df.aggregate(['sum', 'mean'], ['min', 'max'])

C. df.agg(['col1': 'sum', 'col2': 'min'])

D. df.agg({'sum', 'mean', 'min', 'max'})

Solution

1. Step 1: Review correct agg() syntax

   The correct syntax uses a dictionary mapping column names to lists of functions: {'col1': ['sum', 'mean'], 'col2': ['min', 'max']}.

2. Step 2: Check each option

   df.agg({'col1': ['sum', 'mean'], 'col2': ['min', 'max']}) matches the correct syntax. Options A, B, and D have syntax errors or wrong argument formats.

3. Final Answer:

   df.agg({'col1': ['sum', 'mean'], 'col2': ['min', 'max']}) -> Option A

4. Quick Check:

   Dictionary with column keys and function lists = df.agg({'col1': ['sum', 'mean'], 'col2': ['min', 'max']}) [OK]

Hint: Use dict with columns as keys and list of funcs as values [OK]

Common Mistakes:

• Passing functions as separate arguments instead of dict
• Using sets {} instead of dict for agg()
• Incorrect list or dict syntax inside agg()

3.

Given the DataFrame:

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

What is the output?

medium

A. A B
sum 6 15
max 3 6

B. A B
sum 6 4
max 3 4

C. A B
sum 6 5
max 3 6

D. A B
sum 6 5
max 3 4

Solution

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

   Sum of [1, 2, 3] is 6; max is 3.

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

   Sum of [4, 5, 6] is 15; max is 6.

3. Final Answer:

   A B sum 6 15 max 3 6 -> Option A

4. Quick Check:

   Sum A=6, Max A=3, Sum B=15, Max B=6 [OK]

Hint: Apply each function only to specified columns [OK]

Common Mistakes:

• Assuming max applies to column B
• Mixing up min and max values
• Confusing row and column labels in output

4.

What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
result = df.agg({'X': 'sum', 'Y': ['mean', 'max']})
print(result)

medium

A. Error because 'agg()' requires all functions as strings.

B. Error because 'sum' should be in a list for column 'X'.

C. Error because 'agg()' cannot mix single and multiple functions.

D. No error; code runs and prints correct aggregation.

Solution

1. Step 1: Check if mixing single and multiple functions is allowed

   In pandas, you can pass a single function as a string or multiple as a list per column in agg().

2. Step 2: Verify syntax correctness

   Passing 'sum' as a string for 'X' and ['mean', 'max'] for 'Y' is valid syntax.

3. Final Answer:

   No error; code runs and prints correct aggregation. -> Option D

4. Quick Check:

   Single or list functions per column both allowed [OK]

Hint: Single function can be string; multiple must be list [OK]

Common Mistakes:

• Thinking all functions must be lists
• Assuming mixing single and list causes error
• Confusing function names with strings vs objects

5.

You have a DataFrame df with columns 'Product', 'Sales', and 'Quantity'. You want to group by 'Product' and get the total sales, average quantity, and maximum sales per product. Which agg() call achieves this?

hard

A. df.groupby('Product').agg({'Sales': ['mean'], 'Quantity': ['sum', 'max']})

B. df.groupby('Product').agg({'Sales': 'mean', 'Quantity': ['sum', 'max']})

C. df.groupby('Product').agg({'Sales': ['sum', 'max'], 'Quantity': 'mean'})

D. df.groupby('Product').agg({'Product': ['sum'], 'Sales': 'max', 'Quantity': 'mean'})

Solution

1. Step 1: Identify required aggregations per column

   We want total sales (sum), average quantity (mean), and maximum sales (max) grouped by product.

2. Step 2: Match functions to columns

   For 'Sales', apply 'sum' and 'max'; for 'Quantity', apply 'mean'. Grouping is by 'Product'.

3. Step 3: Check options

   df.groupby('Product').agg({'Sales': ['sum', 'max'], 'Quantity': 'mean'}) matches the requirements exactly. Others have wrong functions or columns.

4. Final Answer:

   df.groupby('Product').agg({'Sales': ['sum', 'max'], 'Quantity': 'mean'}) -> Option C

5. Quick Check:

   Group by Product, Sales sum & max, Quantity mean = df.groupby('Product').agg({'Sales': ['sum', 'max'], 'Quantity': 'mean'}) [OK]

Hint: Map each column to needed functions in a dict inside agg() [OK]

Common Mistakes:

• Applying wrong functions to columns
• Including grouping column in agg()
• Mixing aggregation functions incorrectly