Named aggregation in Pandas - Mini Project: Build & Apply

Summarizing Sales Data with Named Aggregation

📖 Scenario: You work in a small store and have a list of sales records. Each record shows the product sold, the quantity sold, and the price per item. You want to find out the total quantity sold and the average price for each product.

🎯 Goal: Build a pandas DataFrame from the sales data, then use groupby with named aggregation to calculate the total quantity and average price per product.

📋 What You'll Learn

Create a pandas DataFrame called sales with columns product, quantity, and price using the exact data provided.

Create a variable called agg_funcs that holds the named aggregation dictionary for total quantity and average price.

Use sales.groupby('product').agg(**agg_funcs) to group and aggregate the data.

Print the resulting DataFrame.

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize sales data to understand product performance and pricing trends.

💼 Career

Data analysts and data scientists use named aggregation in pandas to create clear, readable summaries of grouped data for reports and decision making.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with these exact rows: {'product': ['apple', 'banana', 'apple', 'banana', 'orange'], 'quantity': [10, 5, 7, 3, 8], 'price': [0.5, 0.3, 0.55, 0.35, 0.8]}.

Pandas

import pandas as pd
# Create the sales DataFrame with the exact data
# Your code here

Hint

Use pd.DataFrame and pass a dictionary with keys 'product', 'quantity', and 'price' and their respective lists.

2

Define the named aggregation dictionary

Create a variable called agg_funcs that holds a dictionary for named aggregation with these keys and values: total_quantity=('quantity', 'sum') and average_price=('price', 'mean').

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 7, 3, 8],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})

# Create the agg_funcs dictionary for named aggregation
# Your code here

Hint

Define agg_funcs as a dictionary with keys for the new column names and values as tuples of (column, aggregation function).

3

Group and aggregate the sales data

Use sales.groupby('product').agg(**agg_funcs) to group the sales DataFrame by product and apply the named aggregation stored in agg_funcs. Store the result in a variable called summary.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 7, 3, 8],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})

agg_funcs = {
    'total_quantity': ('quantity', 'sum'),
    'average_price': ('price', 'mean')
}

# Group sales by product and aggregate using agg_funcs
# Your code here

Hint

Use groupby('product') and then agg(**agg_funcs) to apply the named aggregation.

4

Print the summary DataFrame

Print the summary DataFrame to see the total quantity and average price for each product.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 7, 3, 8],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})

agg_funcs = {
    'total_quantity': ('quantity', 'sum'),
    'average_price': ('price', 'mean')
}

summary = sales.groupby('product').agg(**agg_funcs)

# Print the summary DataFrame
# Your code here

Hint

Use print(summary) to display the grouped and aggregated data.

Practice

1. What is the main purpose of named aggregation in pandas?

easy

A. To assign custom names to aggregated summary columns

B. To rename columns before aggregation

C. To filter rows before grouping

D. To sort data after aggregation

Solution

1. Step 1: Understand aggregation in pandas

   Aggregation combines data by groups to produce summary statistics.

2. Step 2: Role of named aggregation

   Named aggregation lets you specify clear names for these summary columns, making results easier to read.

3. Final Answer:

   To assign custom names to aggregated summary columns -> Option A

4. Quick Check:

   Named aggregation = custom summary names [OK]

Hint: Named aggregation means naming summary columns clearly [OK]

Common Mistakes:

• Confusing named aggregation with renaming columns before aggregation
• Thinking it filters or sorts data
• Assuming it changes the grouping keys

2. Which of the following is the correct syntax for using named aggregation in groupby.agg()?

easy

A. df.groupby('A').agg({'new_col': 'B', 'sum'})

B. df.groupby('A').agg('new_col' = ('B', 'sum'))

C. df.groupby('A').agg(new_col=('B', 'sum'))

D. df.groupby('A').agg(('B', 'sum') = 'new_col')

Solution

1. Step 1: Recall named aggregation syntax

   Named aggregation uses keyword arguments with the format new_name=(column, function).

2. Step 2: Check each option

   df.groupby('A').agg(new_col=('B', 'sum')) matches this syntax exactly: new_col=('B', 'sum'). Others have invalid syntax or misplaced equals signs.

3. Final Answer:

   df.groupby('A').agg(new_col=('B', 'sum')) -> Option C

4. Quick Check:

   Named aggregation syntax = new_name=(col, func) [OK]

Hint: Use new_name=(column, function) inside agg() [OK]

Common Mistakes:

• Using equals sign inside dictionary instead of keyword argument
• Misplacing parentheses or quotes
• Trying to assign new_col outside agg()

3. Given the DataFrame:

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

What is the output?

medium

A. Team index with total_points as sum of Points and max_assists as max of Assists

B. DataFrame with columns Points and Assists unchanged

C. Error because named aggregation syntax is wrong

D. Empty DataFrame because groupby is missing

Solution

1. Step 1: Understand the groupby and aggregation

   Grouping by 'Team' groups rows by 'A' and 'B'. Then sum Points and max Assists per group.

2. Step 2: Check named aggregation results

   total_points sums Points per team: A=10+15=25, B=10+20=30; max_assists picks max Assists per team: A=max(5,7)=7, B=max(8,6)=8.

3. Final Answer:

   Team index with total_points as sum of Points and max_assists as max of Assists -> Option A

4. Quick Check:

   Named aggregation sums and maxes grouped data [OK]

Hint: Sum and max per group with named aggregation [OK]

Common Mistakes:

• Expecting original columns unchanged
• Confusing max with sum
• Thinking groupby needs reset_index() to work

4. What is wrong with this code snippet?

df.groupby('Category').agg(total=('Value', 'sum'), mean='Value')

medium

A. The sum function cannot be used with named aggregation

B. The groupby column 'Category' does not exist

C. The agg method requires a list, not keyword arguments

D. The mean aggregation is missing the function tuple syntax

Solution

1. Step 1: Review named aggregation syntax

   Each named aggregation must be a keyword argument with a tuple: new_name=(column, function).

2. Step 2: Identify the error

   The 'mean' aggregation is given as mean='Value' without specifying the aggregation function, which is incorrect.

3. Final Answer:

   The mean aggregation is missing the function tuple syntax -> Option D

4. Quick Check:

   Named aggregation needs (column, function) tuples [OK]

Hint: Each agg must be new_name=(col, func) [OK]

Common Mistakes:

• Passing column name as string without function
• Assuming agg accepts mixed syntax
• Using list instead of keyword arguments

5. You have a DataFrame with sales data:

import pandas as pd
df = pd.DataFrame({'Store': ['X', 'X', 'Y', 'Y', 'Z'], 'Sales': [100, 150, 200, 250, 300], 'Returns': [5, 3, 2, 4, 1]})

You want to create a summary DataFrame grouped by 'Store' with:

• Total sales named 'total_sales'
• Average returns named 'avg_returns'
• Count of sales entries named 'sales_count'

Which code correctly uses named aggregation to achieve this?

hard

A. df.groupby('Store').agg(sum('Sales') as total_sales, mean('Returns') as avg_returns, count('Sales') as sales_count)

B. df.groupby('Store').agg(total_sales=('Sales', 'sum'), avg_returns=('Returns', 'mean'), sales_count=('Sales', 'count'))

C. df.groupby('Store').agg(total_sales='sum', avg_returns='mean', sales_count='count')

D. df.groupby('Store').agg({'total_sales': ('Sales', 'sum'), 'avg_returns': ('Returns', 'mean'), 'sales_count': ('Sales', 'count')})

Solution

1. Step 1: Recall named aggregation syntax

   Use keyword arguments with new_name=(column, function) inside agg().

2. Step 2: Evaluate each option

   df.groupby('Store').agg(total_sales=('Sales', 'sum'), avg_returns=('Returns', 'mean'), sales_count=('Sales', 'count')) correctly uses named aggregation syntax. df.groupby('Store').agg({'total_sales': ('Sales', 'sum'), 'avg_returns': ('Returns', 'mean'), 'sales_count': ('Sales', 'count')}) uses a dictionary but this is not valid for named aggregation. df.groupby('Store').agg(total_sales='sum', avg_returns='mean', sales_count='count') uses strings without specifying columns. df.groupby('Store').agg(sum('Sales') as total_sales, mean('Returns') as avg_returns, count('Sales') as sales_count) uses invalid syntax.

3. Final Answer:

   df.groupby('Store').agg(total_sales=('Sales', 'sum'), avg_returns=('Returns', 'mean'), sales_count=('Sales', 'count')) -> Option B

4. Quick Check:

   Named aggregation = keyword args with (col, func) tuples [OK]

Hint: Use new_name=(col, func) for multiple summaries [OK]

Common Mistakes:

• Using dictionary instead of keyword arguments
• Passing only function names without columns
• Trying SQL-like syntax inside agg()