groupby() basics in Data Analysis Python - Mini Project: Build & Apply

groupby() basics

📖 Scenario: You work in a small store and have a list of sales records. Each record shows the product sold and the quantity sold. You want to find out how many items of each product were sold in total.

🎯 Goal: Use the groupby() function from pandas to group sales by product and calculate the total quantity sold for each product.

📋 What You'll Learn

Create a pandas DataFrame with sales data

Create a variable for grouping the data by product

Use groupby() and sum() to calculate total quantities

Print the grouped result showing total quantity per product

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize sales data by product to understand what sells best.

💼 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 with these exact columns and rows:
product: 'apple', 'banana', 'apple', 'orange', 'banana'
quantity: 10, 5, 7, 8, 3

Data Analysis Python

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

Hint

Use pd.DataFrame with a dictionary containing the columns and their values.

2

Create a group variable by product

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

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'orange', 'banana'],
    'quantity': [10, 5, 7, 8, 3]
})
# Group the sales DataFrame by product
# Your code here

Hint

Use sales.groupby('product') and assign it to grouped.

3

Calculate total quantity per product

Create a variable called total_quantity that uses the grouped variable to calculate the sum of quantity for each product.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'orange', 'banana'],
    'quantity': [10, 5, 7, 8, 3]
})
grouped = sales.groupby('product')
# Calculate total quantity per product
# Your code here

Hint

Use grouped['quantity'].sum() to get the total quantity per product.

4

Print the total quantity per product

Print the total_quantity variable to display the total quantity sold for each product.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'product': ['apple', 'banana', 'apple', 'orange', 'banana'],
    'quantity': [10, 5, 7, 8, 3]
})
grouped = sales.groupby('product')
total_quantity = grouped['quantity'].sum()
# Print the total quantity per product
# Your code here

Hint

Use print(total_quantity) to show the result.

Practice

1. What does the groupby() function do in pandas?

easy

A. It splits data into groups based on column values.

B. It sorts the data in ascending order.

C. It removes duplicate rows from the data.

D. It changes the data type of columns.

Solution

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

   The groupby() function is used to split data into groups based on the values in one or more columns.

2. Step 2: Compare with other options

   Sorting, removing duplicates, and changing data types are different operations and not the main function of groupby().

3. Final Answer:

   It splits data into groups based on column values. -> Option A

4. Quick Check:

   groupby() = split data by column [OK]

Hint: Remember: groupby splits data into groups by column values [OK]

Common Mistakes:

• Confusing groupby with sorting or filtering
• Thinking groupby changes data types
• Assuming groupby removes duplicates

2. Which of the following is the correct syntax to group a DataFrame df by the column 'Category'?

easy

A. df.group_by('Category')

B. df.groupby('Category')

C. df.groupby['Category']

D. df.groupby(Category)

Solution

1. Step 1: Recall correct method syntax

   The correct method name is groupby without underscore, called with parentheses and column name as a string.

2. Step 2: Check each option

   The correct syntax is df.groupby('Category'). Incorrect versions include df.group_by('Category') (wrong method name), df.groupby['Category'] (wrong brackets), and df.groupby(Category) (missing quotes).

3. Final Answer:

   df.groupby('Category') -> Option B

4. Quick Check:

   Correct syntax uses parentheses and quotes [OK]

Hint: Use df.groupby('column_name') with quotes and parentheses [OK]

Common Mistakes:

• Using underscore in groupby (group_by)
• Using square brackets instead of parentheses
• Not quoting the column name

3. Given the DataFrame df below, what is the output of df.groupby('Team')['Points'].sum()?

   Team  Points
0  A     10
1  B     15
2  A     5
3  B     10
4  C     7

medium

A. Team
A 20
B 25
C 7
Name: Points, dtype: int64

B. Team
A 10
B 15
C 7
Name: Points, dtype: int64

C. Team
A 15
B 25
C 7
Name: Points, dtype: int64

D. Team
A 5
B 10
C 7
Name: Points, dtype: int64

Solution

1. Step 1: Group data by 'Team'

   The groups are: A with points 10 and 5, B with 15 and 10, C with 7.

2. Step 2: Sum points in each group

   Sum for A = 10 + 5 = 15, for B = 15 + 10 = 25, for C = 7.

3. Final Answer:

   Team A 15 B 25 C 7 Name: Points, dtype: int64 -> Option C

4. Quick Check:

   Sum points per team = 15, 25, 7 [OK]

Hint: Add values in each group to get sum quickly [OK]

Common Mistakes:

• Not summing all values per group
• Mixing group labels and sums
• Using count instead of sum

4. What is wrong with this code snippet?

grouped = df.groupby('Category')
result = grouped.sum['Sales']
print(result)

medium

A. Using square brackets instead of parentheses after sum.

B. Missing quotes around 'Category' in groupby.

C. Calling sum() without parentheses.

D. Using print() incorrectly.

Solution

1. Step 1: Check method call syntax

   The sum function must be called with parentheses: sum(), not accessed with square brackets.

2. Step 2: Verify other parts

   The groupby column is correctly quoted, and print usage is correct.

3. Final Answer:

   Using square brackets instead of parentheses after sum. -> Option A

4. Quick Check:

   sum() needs parentheses, not brackets [OK]

Hint: Call aggregation functions with parentheses like sum() [OK]

Common Mistakes:

• Using brackets instead of parentheses for functions
• Forgetting parentheses after sum
• Misquoting column names

5. You have a DataFrame df with columns 'City', 'Category', and 'Sales'. How do you find the average sales for each city and category combination?

hard

A. df.groupby('City' & 'Category')['Sales'].mean()

B. df.groupby('City', 'Category')['Sales'].mean()

C. df.groupby('City' + 'Category')['Sales'].mean()

D. df.groupby(['City', 'Category'])['Sales'].mean()

Solution

1. Step 1: Group by multiple columns

   To group by both 'City' and 'Category', pass a list of column names: ['City', 'Category'].

2. Step 2: Calculate mean of 'Sales'

   After grouping, select 'Sales' column and apply mean() to get average sales per group.

3. Final Answer:

   df.groupby(['City', 'Category'])['Sales'].mean() -> Option D

4. Quick Check:

   Group by list of columns, then mean() [OK]

Hint: Group by list of columns: df.groupby(['col1','col2']) [OK]

Common Mistakes:

• Passing multiple columns as separate arguments
• Using bitwise AND (&) instead of list
• Using string concatenation (+ ) instead of list