Aggregation with agg() in Pandas - Mini Project: Build & Apply

Aggregation with agg()

📖 Scenario: You work in a small store that sells fruits. You have a list of sales data with fruit names, quantities sold, and prices. You want to find out some summary information like total quantity sold and average price for each fruit.

🎯 Goal: Build a program that uses pandas to group the sales data by fruit and then uses agg() to calculate total quantity sold and average price for each fruit.

📋 What You'll Learn

Create a pandas DataFrame with the given sales data

Create a grouping variable for the fruit column

Use agg() to calculate total quantity and average price per fruit

Print the resulting aggregated DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize sales data to understand which products sell the most and at what average price.

💼 Career

Data analysts and scientists use aggregation functions like <code>agg()</code> to quickly get insights from large datasets.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with these exact columns and values: fruit with ['apple', 'banana', 'apple', 'banana', 'orange'], quantity with [10, 5, 8, 7, 3], and price with [0.5, 0.3, 0.55, 0.35, 0.8].

Pandas

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

Hint

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

2

Group the data by fruit

Create a variable called grouped that groups the sales DataFrame by the fruit column using groupby().

Pandas

import pandas as pd
sales = pd.DataFrame({
    'fruit': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
# Group the sales DataFrame by the fruit column
# Your code here

Hint

Use sales.groupby('fruit') to group rows by fruit.

3

Aggregate total quantity and average price

Create a variable called summary that uses agg() on grouped to calculate the total of quantity using 'sum' and the average of price using 'mean'.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'fruit': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
grouped = sales.groupby('fruit')
# Use agg() to get total quantity and average price per fruit
# Your code here

Hint

Use agg({'quantity': 'sum', 'price': 'mean'}) on the grouped object.

4

Print the aggregated summary

Write a print statement to display the summary DataFrame.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'fruit': ['apple', 'banana', 'apple', 'banana', 'orange'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.55, 0.35, 0.8]
})
grouped = sales.groupby('fruit')
summary = grouped.agg({'quantity': 'sum', 'price': 'mean'})
# Print the summary DataFrame
# Your code here

Hint

Use print(summary) to show the aggregated results.

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