Pandas GroupBy and Aggregation Practice Questions with Solutions

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Introduction

Grouping and aggregation are among the most powerful features of Pandas for data analysis. The groupby() function allows you to divide data into groups based on one or more columns and then perform calculations such as sum, average, count, minimum, maximum, and more. These operations are widely used in business reports, sales analysis, finance, HR analytics, and data science projects. In this chapter, you’ll learn how to use GroupBy and aggregation functions through practical examples. Pandas GroupBy and Aggregation practice questions with solutions help to understand the concepts.

1. Python Program to Group Data by a Single Column

Problem Statement

Write a Python program to group students by their Department.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

grouped = df.groupby("Department")

for department, records in grouped:
    print(department)
    print(records)

Sample Output

HR
     Name Department  Marks
1    Aman         HR     90
3   Sneha         HR     88

IT
     Name Department  Marks
0   Rahul         IT     85
2   Priya         IT     78

Explanation

The groupby() function divides the DataFrame into groups based on the values in the Department column.

Concepts Covered

  • groupby()
  • Data Grouping
  • Group Iteration

2. Python Program to Calculate the Sum of Each Group

Problem Statement

Write a Python program to calculate the total marks for each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Marks"].sum()

print(result)

Sample Output

Department
HR    178
IT    163
Name: Marks, dtype: int64

Explanation

The sum() function adds all values within each group.

Concepts Covered

  • groupby()
  • sum()
  • Aggregation

3. Python Program to Calculate the Average of Each Group

Problem Statement

Write a Python program to calculate the average marks for each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Marks"].mean()

print(result)

Sample Output

Department
HR    89.0
IT    81.5
Name: Marks, dtype: float64

Explanation

The mean() function calculates the average value for each group.

Concepts Covered

  • groupby()
  • mean()
  • Average Calculation

4. Python Program to Count Records in Each Group

Problem Statement

Write a Python program to count the number of students in each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR", "IT"]
}

df = pd.DataFrame(data)

result = df.groupby("Department").size()

print(result)

Sample Output

Department
HR    2
IT    3
dtype: int64

Explanation

The size() function returns the total number of rows in each group.

Concepts Covered

  • groupby()
  • size()
  • Record Count

5. Python Program to Find the Maximum Value in Each Group

Problem Statement

Write a Python program to find the highest marks obtained in each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Marks"].max()

print(result)

Sample Output

Department
HR    90
IT    85
Name: Marks, dtype: int64

Explanation

The max() function returns the highest value within each group.

Concepts Covered

  • groupby()
  • max()
  • Maximum Value

6. Python Program to Find the Minimum Value in Each Group

Problem Statement

Write a Python program to find the lowest marks obtained in each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Marks"].min()

print(result)

Sample Output

Department
HR    88
IT    78
Name: Marks, dtype: int64

Explanation

The min() function returns the smallest value in each group.

Concepts Covered

  • groupby()
  • min()
  • Minimum Value

7. Python Program to Calculate Multiple Aggregations

Problem Statement

Write a Python program to calculate the sum, average, and maximum marks for each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Marks"].agg(
    ["sum", "mean", "max"]
)

print(result)

Sample Output

            sum  mean  max
Department
HR          178  89.0   90
IT          163  81.5   85

Explanation

The agg() function allows multiple aggregation operations to be performed simultaneously on grouped data.

Concepts Covered

  • groupby()
  • agg()
  • Multiple Aggregations

8. Python Program to Group by Multiple Columns

Problem Statement

Write a Python program to group data by Department and Gender.

Python Solution

import pandas as pd

data = {
    "Department": [
        "IT",
        "HR",
        "IT",
        "HR",
        "IT"
    ],
    "Gender": [
        "Male",
        "Female",
        "Female",
        "Female",
        "Male"
    ],
    "Marks": [85, 90, 78, 88, 95]
}

df = pd.DataFrame(data)

result = df.groupby(
    ["Department", "Gender"]
)["Marks"].mean()

print(result)

Sample Output

Department  Gender
HR          Female    89.0
IT          Female    78.0
            Male      90.0
Name: Marks, dtype: float64

Explanation

The groupby() function accepts multiple columns, creating hierarchical groups for more detailed analysis.

Concepts Covered

  • Multiple Grouping
  • groupby()
  • Hierarchical Index

9. Python Program to Reset Index After Grouping

Problem Statement

Write a Python program to reset the index after performing a GroupBy operation.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = (
    df.groupby("Department")["Marks"]
      .mean()
      .reset_index()
)

print(result)

Sample Output

  Department  Marks
0         HR   89.0
1         IT   81.5

Explanation

The reset_index() function converts the grouped index back into a regular column, making the result easier to read and export.

Concepts Covered

  • reset_index()
  • GroupBy Result
  • Data Formatting

10. Python Program to Count Unique Values in Each Group

Problem Statement

Write a Python program to count the number of unique students in each department.

Python Solution

import pandas as pd

data = {
    "Department": [
        "IT",
        "HR",
        "IT",
        "HR",
        "IT"
    ],
    "Name": [
        "Rahul",
        "Aman",
        "Rahul",
        "Sneha",
        "Priya"
    ]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Name"].nunique()

print(result)

Sample Output

Department
HR    2
IT    2
Name: Name, dtype: int64

Explanation

The nunique() function counts distinct values within each group, making it useful for identifying unique records.

Concepts Covered

  • nunique()
  • Unique Count
  • GroupBy Aggregation

11. Python Program to Apply Custom Aggregation Functions

Problem Statement

Write a Python program to calculate the minimum, maximum, and average marks for each department using custom aggregation.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department").agg(
    {
        "Marks": ["min", "max", "mean"]
    }
)

print(result)

Sample Output

           Marks
             min  max  mean
Department
HR            88   90  89.0
IT            78   85  81.5

Explanation

The agg() function accepts a dictionary to apply multiple aggregation functions to selected columns.

Concepts Covered

  • agg()
  • Custom Aggregation
  • Multiple Functions

12. Python Program to Retrieve the First Record from Each Group

Problem Statement

Write a Python program to display the first student from each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Name": ["Rahul", "Aman", "Priya", "Sneha"]
}

df = pd.DataFrame(data)

result = df.groupby("Department").first()

print(result)

Sample Output

             Name
Department
HR           Aman
IT         Rahul

Explanation

The first() function returns the first row from each group.

Concepts Covered

  • first()
  • GroupBy
  • First Record

13. Python Program to Retrieve the Last Record from Each Group

Problem Statement

Write a Python program to display the last student from each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Name": ["Rahul", "Aman", "Priya", "Sneha"]
}

df = pd.DataFrame(data)

result = df.groupby("Department").last()

print(result)

Sample Output

             Name
Department
HR         Sneha
IT         Priya

Explanation

The last() function retrieves the last row from every group.

Concepts Covered

  • last()
  • GroupBy
  • Last Record

14. Python Program to Calculate Standard Deviation for Each Group

Problem Statement

Write a Python program to calculate the standard deviation of marks for each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Marks"].std()

print(result)

Sample Output

Department
HR    1.414214
IT    4.949747
Name: Marks, dtype: float64

Explanation

The std() function calculates the standard deviation for each group, showing how much the values vary from the mean.

Concepts Covered

  • std()
  • Group Statistics
  • Standard Deviation

15. Python Program to Calculate Variance for Each Group

Problem Statement

Write a Python program to calculate the variance of marks for each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Marks"].var()

print(result)

Sample Output

Department
HR     2.0
IT    24.5
Name: Marks, dtype: float64

Explanation

The var() function calculates the variance of values within each group, measuring data dispersion.

Concepts Covered

  • var()
  • Variance
  • Group Statistics

16. Python Program to Apply Multiple Aggregation Functions on Multiple Columns

Problem Statement

Write a Python program to calculate the average Age and the maximum Marks for each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR"],
    "Age": [20, 21, 19, 22],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

result = df.groupby("Department").agg(
    {
        "Age": "mean",
        "Marks": "max"
    }
)

print(result)

Sample Output

             Age  Marks
Department
HR          21.5     90
IT          19.5     85

Explanation

The agg() function can perform different aggregation operations on multiple columns simultaneously.

Concepts Covered

  • agg()
  • Multiple Columns
  • GroupBy Aggregation

17. Python Program to Calculate the Total Salary by Department

Problem Statement

Write a Python program to calculate the total salary of employees in each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "HR", "IT", "HR", "Sales"],
    "Salary": [50000, 45000, 55000, 47000, 60000]
}

df = pd.DataFrame(data)

result = df.groupby("Department")["Salary"].sum()

print(result)

Sample Output

Department
HR        92000
IT       105000
Sales     60000
Name: Salary, dtype: int64

Explanation

The groupby() function groups employees by department, while the sum() function calculates the total salary for each group.

Concepts Covered

  • groupby()
  • sum()
  • Salary Analysis

Chapter Summary

In this chapter, you learned how to use the GroupBy feature in Pandas to organize and summarize data efficiently. You explored grouping data by one or multiple columns, calculating totals, averages, minimums, maximums, standard deviation, variance, unique counts, and applying multiple aggregation functions. You also learned how to retrieve the first and last records of each group and reset indexes after grouping. These techniques are widely used in reporting, business intelligence, finance, HR analytics, and data science projects.

Key Takeaways

  • groupby() divides data into meaningful groups.
  • sum(), mean(), min(), max(), count(), and size() summarize grouped data.
  • agg() applies multiple aggregation functions at once.
  • nunique() counts unique values within each group.
  • first() and last() retrieve the first and last records from groups.
  • std() calculates standard deviation.
  • var() calculates variance.
  • reset_index() converts grouped indexes into normal columns.
  • Grouping by multiple columns enables detailed hierarchical analysis.
  • GroupBy is one of the most frequently used features in real-world data analysis.

Frequently Asked Questions (FAQs)

1. What is the purpose of the groupby() function in Pandas?

The groupby() function divides data into groups based on one or more columns, allowing aggregation and analysis.

df.groupby("Department")

2. Which function calculates the average value for each group?

Use the mean() function.

df.groupby("Department")["Marks"].mean()

3. How do you calculate multiple statistics at once?

Use the agg() function.

df.groupby("Department")["Marks"].agg(
    ["sum", "mean", "max"]
)

4. How do you count unique values in each group?

Use the nunique() function.

df.groupby("Department")["Name"].nunique()

5. What is the difference between count() and size()?

  • count() counts only non-missing values.
  • size() counts all rows, including rows with missing values.

6. Why is reset_index() used after GroupBy?

reset_index() converts grouped indexes back into normal columns, making the output easier to read and export.

result.reset_index()

7. Can groupby() work with multiple columns?

Yes. You can group data using multiple columns.

df.groupby(
    ["Department", "Gender"]
)

8. Why is GroupBy important in Pandas?

GroupBy simplifies data summarization and reporting by allowing calculations such as totals, averages, counts, and other statistics for different categories. It is widely used in business analytics, dashboards, financial reporting, HR analytics, and machine learning data preparation.

Written by Shubhranshu Shekhar, who has trained 20000+ students in coding.

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