groupby() basics in Data Analysis Python

Introduction

The groupby() function helps you split data into groups based on some criteria. Then you can analyze each group separately.

You want to find the average sales per store from a big sales list.

You need to count how many students are in each class from a school dataset.

You want to sum expenses by category from your budget data.

You want to see the maximum temperature recorded each day from weather data.

Syntax

Data Analysis Python

df.groupby('column_name')

This creates groups based on unique values in the specified column.

You usually follow it with an aggregation like .sum() or .mean().

Examples

Groups data by unique values in the 'Category' column.

Data Analysis Python

df.groupby('Category')

Groups by 'Store' and sums the 'Sales' for each store.

Data Analysis Python

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

Groups by both 'City' and 'Year', then finds average temperature for each group.

Data Analysis Python

df.groupby(['City', 'Year'])['Temperature'].mean()

Sample Program

This code groups sales data by store and sums the sales for each store.

Data Analysis Python

import pandas as pd

# Create sample data
sales_data = pd.DataFrame({
    'Store': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Sales': [100, 150, 200, 250, 300, 350]
})

# Group by 'Store' and sum sales
sales_sum = sales_data.groupby('Store')['Sales'].sum()

print(sales_sum)

Important Notes

After groupby(), you must use an aggregation function like sum(), mean(), or count() to get results.

You can group by multiple columns by passing a list, like groupby(['col1', 'col2']).

The result of groupby() with aggregation is usually a new DataFrame or Series.

Summary

groupby() splits data into groups based on column values.

Use aggregation functions after grouping to analyze each group.

It helps summarize and understand data by categories.

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