Split-apply-combine mental model in Pandas - Mini Project: Build & Apply

Split-apply-combine mental model

📖 Scenario: Imagine you work at a small bookstore. You have sales data for different book genres and want to find out the average sales for each genre. This helps you understand which types of books sell best.

🎯 Goal: You will create a pandas DataFrame with sales data, then use the split-apply-combine method to calculate the average sales per genre.

📋 What You'll Learn

Create a pandas DataFrame named sales_data with columns 'Genre' and 'Sales' using the exact data provided.

Create a variable named group_column and set it to the string 'Genre'.

Use the split-apply-combine method by grouping sales_data by group_column and calculating the mean sales for each genre, storing the result in average_sales.

Print the average_sales DataFrame to show the average sales per genre.

💡 Why This Matters

🌍 Real World

Bookstores and many businesses use the split-apply-combine method to analyze grouped data, like sales by category or customer segment.

💼 Career

Data analysts and data scientists often use pandas groupby operations to summarize and understand data quickly.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with two columns: 'Genre' and 'Sales'. Use this exact data: 'Genre' values are 'Fiction', 'Non-Fiction', 'Fiction', 'Science', 'Non-Fiction', 'Science'. Corresponding 'Sales' values are 100, 150, 200, 130, 170, 120. Import pandas as pd first.

Pandas

import pandas as pd
# Your code here

Hint

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

2

Set the group column

Create a variable called group_column and set it to the string 'Genre'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Science', 'Non-Fiction', 'Science'],
    'Sales': [100, 150, 200, 130, 170, 120]
})
# Your code here

Hint

Just assign the string 'Genre' to the variable group_column.

3

Calculate average sales per genre

Use the split-apply-combine method by grouping sales_data by group_column and calculating the mean of 'Sales'. Store the result in a variable called average_sales.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Science', 'Non-Fiction', 'Science'],
    'Sales': [100, 150, 200, 130, 170, 120]
})
group_column = 'Genre'
# Your code here

Hint

Use sales_data.groupby(group_column)['Sales'].mean() to get average sales per genre.

4

Print the average sales

Print the average_sales variable to display the average sales per genre.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Genre': ['Fiction', 'Non-Fiction', 'Fiction', 'Science', 'Non-Fiction', 'Science'],
    'Sales': [100, 150, 200, 130, 170, 120]
})
group_column = 'Genre'
average_sales = sales_data.groupby(group_column)['Sales'].mean()
# Your code here

Hint

Use print(average_sales) to show the result.

Practice

1. What is the main idea behind the split-apply-combine mental model in pandas?

easy

A. Create new columns, rename them, then delete old columns

B. Sort data, filter rows, then plot results

C. Split data into groups, apply a function to each group, then combine results

D. Load data, clean missing values, then save to file

Solution

1. Step 1: Understand the split step

   The data is divided into groups based on some criteria using groupby().

2. Step 2: Apply and combine steps

   A function is applied to each group separately, then the results are combined into a new DataFrame.

3. Final Answer:

   Split data into groups, apply a function to each group, then combine results -> Option C

4. Quick Check:

   Split-apply-combine = Group, apply, combine [OK]

Hint: Remember: split groups, apply function, combine results [OK]

Common Mistakes:

• Confusing split-apply-combine with sorting or filtering
• Thinking it only applies to plotting or saving files
• Mixing up the order of steps

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

easy

A. df.groupby('Category').mean()

B. df.mean().groupby('Category')

C. df.groupby.mean('Category')

D. df.groupby('Category').apply(mean)

Solution

1. Step 1: Identify correct groupby syntax

   The correct way to group by a column is df.groupby('Category').

2. Step 2: Apply aggregation function

   To get the mean of each group, use .mean() after grouping.

3. Final Answer:

   df.groupby('Category').mean() -> Option A

4. Quick Check:

   Groupby then mean = df.groupby('Category').mean() [OK]

Hint: Use df.groupby('col').mean() to get group means [OK]

Common Mistakes:

• Calling mean before groupby
• Using apply(mean) without importing mean function
• Wrong method chaining order

3. Given the DataFrame df below:

  Category  Value
0        A      5
1        B      3
2        A      7
3        B      2
4        C      4

What is the output of df.groupby('Category').sum()?

medium

A. Category as column with sums: A=5, B=3, C=4

B. Error because sum() cannot be applied

C. Sum of all values ignoring groups: 21

D. Category as index with sums: A=12, B=5, C=4

Solution

1. Step 1: Group data by 'Category'

   Rows are grouped by 'Category': A, B, and C groups.

2. Step 2: Sum values in each group

   Sum values for A: 5+7=12, B: 3+2=5, C: 4.

3. Final Answer:

   Category as index with sums: A=12, B=5, C=4 -> Option D

4. Quick Check:

   Sum per group = A:12, B:5, C:4 [OK]

Hint: Sum values after groupby to get totals per group [OK]

Common Mistakes:

• Summing all values ignoring groups
• Expecting category as a column instead of index
• Thinking sum() causes error

4. Consider this code snippet:

result = df.groupby('Type').agg({'Score': 'mean', 'Age': 'max'})

What is wrong if the code raises a KeyError?

medium

A. The aggregation functions 'mean' and 'max' are invalid

B. The DataFrame does not have a column named 'Type'

C. The groupby method cannot take a string argument

D. agg() requires a list, not a dictionary

Solution

1. Step 1: Understand KeyError cause

   A KeyError usually means the column used for grouping does not exist in the DataFrame.

2. Step 2: Check groupby argument and agg usage

   Using a string in groupby and a dictionary in agg is correct syntax.

3. Final Answer:

   The DataFrame does not have a column named 'Type' -> Option B

4. Quick Check:

   KeyError means missing column [OK]

Hint: Check column names before groupby to avoid KeyError [OK]

Common Mistakes:

• Assuming aggregation functions are invalid
• Thinking groupby can't take string columns
• Misunderstanding agg() argument types

5. You have a DataFrame df with columns 'Team', 'Player', and 'Points'. You want to find the player with the highest points in each team. Which code correctly uses the split-apply-combine model to achieve this?

hard

A. df.groupby('Team').apply(lambda g: g[g['Points'] == g['Points'].max()])

B. df.groupby('Team')['Points'].max()

C. df.groupby('Player')['Points'].sum()

D. df.groupby('Team').mean()['Points']

Solution

1. Step 1: Understand the goal

   We want the player with the highest points per team, not just the max points value.

2. Step 2: Use groupby and apply to filter rows

   Using apply with a lambda filters each group to rows where 'Points' equals the group's max.

3. Step 3: Check other options

   df.groupby('Team')['Points'].max() returns max points per team but not the player. df.groupby('Player')['Points'].sum() groups by player, not team. df.groupby('Team').mean()['Points'] returns mean points, not max player.

4. Final Answer:

   df.groupby('Team').apply(lambda g: g[g['Points'] == g['Points'].max()]) -> Option A

5. Quick Check:

   Use apply with filter to get max player per group [OK]

Hint: Use groupby + apply with filter to get max per group row [OK]

Common Mistakes:

• Using max() alone returns only values, not rows
• Grouping by wrong column
• Using mean() instead of max()