Iterating over groups in Pandas - Mini Project: Build & Apply

Iterating over groups

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You have a table of sales data with the bread type and the number of loaves sold each day. You want to analyze the sales by bread type.

🎯 Goal: Build a program that groups the sales data by bread type and iterates over each group to see the sales details.

📋 What You'll Learn

Create a pandas DataFrame with bread sales data

Create a grouping variable using groupby on the bread type

Use a for loop to iterate over the groups

Print the group name and the group data

💡 Why This Matters

🌍 Real World

Grouping data is common in sales, finance, and many fields to analyze subsets of data separately.

💼 Career

Data analysts and scientists often group data to summarize and understand patterns in business data.

1

Create the sales DataFrame

Import pandas as pd and create a DataFrame called sales with these exact columns and values: 'Bread' with values ['Sourdough', 'Baguette', 'Sourdough', 'Baguette', 'Rye'] and 'Loaves' with values [10, 15, 7, 20, 5].

Pandas

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

Hint

Use pd.DataFrame with a dictionary to create the table.

2

Group the sales by bread type

Create a variable called grouped that groups the sales DataFrame by the 'Bread' column using groupby.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Bread': ['Sourdough', 'Baguette', 'Sourdough', 'Baguette', 'Rye'],
    'Loaves': [10, 15, 7, 20, 5]
})
# Group the sales DataFrame by the 'Bread' column
# Your code here

Hint

Use sales.groupby('Bread') to group the data.

3

Iterate over the groups

Use a for loop with variables bread_type and group_data to iterate over grouped. Inside the loop, print the bread_type and the group_data DataFrame.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Bread': ['Sourdough', 'Baguette', 'Sourdough', 'Baguette', 'Rye'],
    'Loaves': [10, 15, 7, 20, 5]
})
grouped = sales.groupby('Bread')
# Iterate over grouped and print bread_type and group_data
# Your code here

Hint

Use for bread_type, group_data in grouped: to loop through groups.

4

Display the grouped sales output

Run the program to print the bread type and the sales data for each group.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'Bread': ['Sourdough', 'Baguette', 'Sourdough', 'Baguette', 'Rye'],
    'Loaves': [10, 15, 7, 20, 5]
})
grouped = sales.groupby('Bread')
for bread_type, group_data in grouped:
    print(bread_type)
    print(group_data)
# Run the program to see the output

Hint

Check the console output to see each bread type and its sales rows.

Practice

1. What does the groupby method in pandas do when used on a DataFrame?

easy

A. It changes the data types of columns.

B. It sorts the DataFrame by a column.

C. It removes duplicate rows from the DataFrame.

D. It splits the DataFrame into groups based on column values.

Solution

1. Step 1: Understand the purpose of groupby

   The groupby method is used to split data into groups based on values in one or more columns.

2. Step 2: Compare with other options

   Sorting, removing duplicates, and changing data types are different operations, not what groupby does.

3. Final Answer:

   It splits the DataFrame into groups based on column values. -> Option D

4. Quick Check:

   groupby = split into groups [OK]

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

Common Mistakes:

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

2. Which of the following is the correct syntax to iterate over groups in a pandas DataFrame df grouped by column 'Category'?

easy

A. for name, group in df.groupby('Category'):

B. for group, name in df.groupby('Category'):

C. df.groupby('Category').for name, group:

D. for name in df.groupby('Category'):

Solution

1. Step 1: Recall groupby iteration syntax

   When iterating, the loop returns a tuple: (group name, group DataFrame).

2. Step 2: Match syntax to correct order

   for name, group in df.groupby('Category'): correctly uses for name, group in df.groupby('Category'):. Others have wrong order or syntax.

3. Final Answer:

   for name, group in df.groupby('Category'): -> Option A

4. Quick Check:

   for name, group in groupby [OK]

Hint: Use 'for name, group in df.groupby()' to iterate groups [OK]

Common Mistakes:

• Swapping group and name variables
• Using invalid for loop syntax
• Ignoring that groupby returns tuples

3. Given the DataFrame df below:

  Category  Value
0        A      1
1        B      2
2        A      3
3        B      4
4        C      5

What will be the output of this code?

for name, group in df.groupby('Category'):
    print(name, group['Value'].sum())

medium

A. A 4
B 2
C 5

B. A 4
B 6
C 5

C. A 3
B 6
C 5

D. A 1
B 4
C 5

Solution

1. Step 1: Group data by 'Category'

   Groups are: A with values [1,3], B with [2,4], C with [5].

2. Step 2: Sum 'Value' in each group

   Sum for A = 1+3=4, B = 2+4=6, C = 5.

3. Final Answer:

   A 4 B 6 C 5 -> Option B

4. Quick Check:

   Sum values per group = output [OK]

Hint: Sum values inside each group to find output [OK]

Common Mistakes:

• Summing only first value per group
• Mixing group names and sums
• Ignoring groupby splits

4. What is wrong with this code snippet that tries to iterate over groups?

for group in df.groupby('Type'):
    print(group['Value'].mean())

medium

A. It should unpack the tuple into name and group before accessing 'Value'.

B. The groupby method requires a list, not a string.

C. The print statement should be outside the loop.

D. The mean() function cannot be used on groups.

Solution

1. Step 1: Understand groupby iteration output

   Each iteration returns a tuple: (group name, group DataFrame).

2. Step 2: Identify error in code

   The code uses only one variable 'group', so 'group' is a tuple, not a DataFrame. Accessing group['Value'] causes error.

3. Final Answer:

   It should unpack the tuple into name and group before accessing 'Value'. -> Option A

4. Quick Check:

   Unpack tuple as (name, group) [OK]

Hint: Unpack groupby output as (name, group) to avoid errors [OK]

Common Mistakes:

• Using one variable instead of two in for loop
• Trying to access DataFrame methods on tuple
• Misunderstanding groupby output structure

5. You have a DataFrame df with columns 'Team' and 'Score'. You want to create a dictionary where keys are team names and values are the average score of each team. Which code correctly does this using iteration over groups?

hard

A. result = {team: group['Score'].mean() for team, group in df.iterrows()}

B. result = {}
for group in df.groupby('Team'):
result[group] = group['Score'].mean()

C. result = {}
for team, group in df.groupby('Team'):
result[team] = group['Score'].mean()

D. result = df.groupby('Team')['Score'].mean().to_dict()

Solution

1. Step 1: Understand iteration over groups

   Using for team, group in df.groupby('Team') correctly unpacks group name and data.

2. Step 2: Calculate mean and store in dictionary

   Inside loop, compute mean of 'Score' and assign to dictionary with team as key.

3. Final Answer:

   result = {} for team, group in df.groupby('Team'): result[team] = group['Score'].mean() -> Option C

4. Quick Check:

   Iterate groups, compute mean, store in dict [OK]

Hint: Use for team, group in groupby and assign mean to dict [OK]

Common Mistakes:

• Not unpacking groupby tuple
• Using iterrows instead of groupby
• Trying to assign group tuple as dict key