Multiple aggregation functions in Pandas - Mini Project: Build & Apply

Using Multiple Aggregation Functions with pandas

📖 Scenario: You work in a small store and have a list of sales data for different products. You want to learn how to find useful summary information like the total sales, average sales, and the highest sale for each product.

🎯 Goal: Build a pandas DataFrame with sales data, then use multiple aggregation functions to find the total, average, and maximum sales for each product.

📋 What You'll Learn

Create a pandas DataFrame with product sales data

Create a list of aggregation functions

Use the agg() method with multiple aggregation functions on grouped data

Print the final aggregated DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize sales data to understand performance by product or category.

💼 Career

Data analysts and scientists use grouping and aggregation to prepare reports and find insights from data.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and rows:
Product: 'Apple', 'Banana', 'Apple', 'Banana', 'Apple'
Sales: 10, 15, 7, 20, 5

Pandas

# Import pandas and create the sales_data DataFrame
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Create a list of aggregation functions

Create a list called agg_functions that contains the strings 'sum', 'mean', and 'max' to use for aggregation.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Sales': [10, 15, 7, 20, 5]
})
# Create the agg_functions list
# Your code here

Hint

Make a list with the exact strings 'sum', 'mean', and 'max'.

3

Group by product and apply multiple aggregation functions

Use sales_data.groupby('Product')['Sales'].agg(agg_functions) and save the result in a variable called summary.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Sales': [10, 15, 7, 20, 5]
})
agg_functions = ['sum', 'mean', 'max']
# Group by 'Product' and aggregate 'Sales' using agg_functions
# Your code here

Hint

Use groupby('Product') on sales_data, select the 'Sales' column, then call agg(agg_functions).

4

Print the aggregated summary

Print the variable summary to display the total, average, and maximum sales for each product.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'],
    'Sales': [10, 15, 7, 20, 5]
})
agg_functions = ['sum', 'mean', 'max']
summary = sales_data.groupby('Product')['Sales'].agg(agg_functions)
# Print the summary DataFrame
# Your code here

Hint

Use print(summary) to show the results.

Practice

1. What does the agg() function do when used after groupby() in pandas?

easy

A. It merges two DataFrames together.

B. It sorts the groups in ascending order.

C. It filters rows based on a condition.

D. It applies multiple aggregation functions to each group and returns their results.

Solution

1. Step 1: Understand groupby operation

   The groupby() function splits data into groups based on column values.

2. Step 2: Apply agg() after grouping

   The agg() function applies one or more aggregation functions like sum, mean, count to each group and returns the results.

3. Final Answer:

   It applies multiple aggregation functions to each group and returns their results. -> Option D

4. Quick Check:

   agg() summarizes groups [OK]

Hint: Remember: agg() summarizes groups with many functions [OK]

Common Mistakes:

• Thinking agg() sorts data
• Confusing agg() with filtering
• Assuming agg() merges DataFrames

2. Which of the following is the correct syntax to apply both sum and mean aggregations on a grouped DataFrame df grouped by column 'Category'?

easy

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

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

C. df.groupby('Category').agg('sum', 'mean')

D. df.groupby('Category').aggregate(sum, mean)

Solution

1. Step 1: Identify correct method chaining

   Grouping must happen first with groupby('Category'), then aggregation with agg().

2. Step 2: Use list of functions inside agg()

   The correct syntax uses a list of function names: agg(['sum', 'mean']).

3. Final Answer:

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

4. Quick Check:

   Group first, then agg with list [OK]

Hint: Use list inside agg() after groupby() [OK]

Common Mistakes:

• Calling agg() before groupby()
• Passing multiple args instead of list to agg()
• Using aggregate() without list syntax

3. Given the DataFrame:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B'], 'Points': [10, 15, 10, 20], 'Assists': [5, 7, 8, 6]}
df = pd.DataFrame(data)
result = df.groupby('Team').agg({'Points': ['sum', 'mean'], 'Assists': 'max'})
print(result)

What is the output?

medium

A. A DataFrame with sum and mean of Points and max of Assists for each Team.

B. A DataFrame with sum and mean of Assists and max of Points for each Team.

C. An error because agg() cannot mix list and string in dict.

D. A Series with aggregated values for all columns combined.

Solution

1. Step 1: Group by 'Team'

   The data is grouped into two groups: Team A and Team B.

2. Step 2: Apply multiple aggregations

   For 'Points', sum and mean are calculated; for 'Assists', max is calculated for each group.

3. Final Answer:

   A DataFrame with sum and mean of Points and max of Assists for each Team. -> Option A

4. Quick Check:

   agg() supports dict with list and string [OK]

Hint: Use dict with list/string in agg() for different columns [OK]

Common Mistakes:

• Expecting error mixing list and string in agg dict
• Confusing which function applies to which column
• Thinking output is a Series, not DataFrame

4. What is wrong with this code snippet?

df.groupby('Category').agg({'Sales': 'sum', 'Profit': ['mean', 'max']})

It raises an error. Why?

medium

A. agg() does not accept multiple functions for different columns.

B. The column names 'Sales' and 'Profit' must be passed as a list, not dict.

C. The code is correct and runs without error.

D. The dictionary mixes single string and list of functions for different columns, which is invalid.

Solution

1. Step 1: Check agg() dictionary syntax

   agg() accepts a dictionary where values can be a string or list of functions per column.

2. Step 2: Verify code correctness

   The code correctly applies 'sum' to 'Sales' and ['mean', 'max'] to 'Profit' without error.

3. Final Answer:

   The code is correct and runs without error. -> Option C

4. Quick Check:

   agg() accepts mixed string and list in dict [OK]

Hint: agg() allows string or list per column in dict [OK]

Common Mistakes:

• Thinking mixing string and list causes error
• Expecting all functions must be in a list
• Confusing agg() with apply() syntax

5. You have a DataFrame df with columns 'Store', 'Sales', and 'Expenses'. You want to group by 'Store' and get the total sales, average expenses, and count of sales entries per store in one step. Which code correctly does this?

hard

A. df.groupby('Store').agg({'Sales': 'sum', 'Expenses': ['mean', 'count']})

B. df.groupby('Store').agg({'Sales': ['sum', 'count'], 'Expenses': 'mean'})

C. df.groupby('Store').agg({'Sales': 'mean', 'Expenses': 'sum', 'Store': 'count'})

D. df.groupby('Store').agg(['sum', 'mean', 'count'])

Solution

1. Step 1: Identify required aggregations per column

   We want total sales (sum), average expenses (mean), and count of sales entries (count).

2. Step 2: Map functions correctly in agg()

   Use a dict: 'Sales' gets ['sum', 'count'], 'Expenses' gets 'mean'. This matches df.groupby('Store').agg({'Sales': ['sum', 'count'], 'Expenses': 'mean'}).

3. Final Answer:

   df.groupby('Store').agg({'Sales': ['sum', 'count'], 'Expenses': 'mean'}) -> Option B

4. Quick Check:

   Dict with list and string per column [OK]

Hint: Use dict with list for multiple funcs per column [OK]

Common Mistakes:

• Putting count under wrong column
• Using agg() without dict for columns
• Applying all functions to all columns