Split-apply-combine mental model in Pandas

Introduction

The split-apply-combine model helps you analyze data by breaking it into groups, doing calculations on each group, and then putting the results back together.

You want to find the average sales for each store in a chain.

You need to count how many times each category appears in a list.

You want to sum expenses by month from a big table of transactions.

You want to find the maximum score for each player in a game.

Syntax

Pandas

df.groupby('column_name').agg({'column_to_aggregate': 'aggregation_function'})

groupby() splits the data into groups based on one or more columns.

agg() applies a function like sum, mean, or count to each group.

Examples

Groups data by 'Category' and sums all numeric columns in each group.

Pandas

df.groupby('Category').sum()

Groups data by 'Store' and calculates the average sales for each store.

Pandas

df.groupby('Store')['Sales'].mean()

Groups data by 'Region' and 'Product' and counts rows in each group.

Pandas

df.groupby(['Region', 'Product']).count()

Sample Program

This code creates a small table of sales and expenses for stores A, B, and C. It then groups the data by store and sums the sales and expenses for each store.

Pandas

import pandas as pd

data = {'Store': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Sales': [100, 150, 200, 250, 300, 350],
        'Expenses': [80, 90, 120, 130, 160, 170]}
df = pd.DataFrame(data)

# Split by 'Store', then sum 'Sales' and 'Expenses' for each store
result = df.groupby('Store').sum()
print(result)

Important Notes

You can group by multiple columns by passing a list to groupby().

The aggregation function can be sum, mean, count, max, min, and others.

After grouping, you get a new DataFrame with the grouped keys as the index.

Summary

Split data into groups using groupby().

Apply calculations to each group with aggregation functions.

Combine the results into a new summarized DataFrame.

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()