Pandas Series Practice Questions with Solutions

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A Pandas Series is a one-dimensional labeled array capable of storing different types of data such as integers, floats, strings, and even Python objects. Each element in a Series has an associated index, making data retrieval and manipulation efficient. Series is one of the fundamental data structures in Pandas and serves as the building block for DataFrames. In this chapter, you’ll learn how to create, access, modify, and analyze Pandas Series through practical coding examples. Pandas series practice questions with solutions help to understand the concepts.

1. Python Program to Create a Pandas Series from a List

Problem Statement

Write a Python program to create a Pandas Series using a Python list.

Python Solution

import pandas as pd

numbers = [10, 20, 30, 40, 50]

series = pd.Series(numbers)

print(series)

Sample Output

0    10
1    20
2    30
3    40
4    50
dtype: int64

Explanation

The pd.Series() function converts a Python list into a Pandas Series. By default, Pandas assigns integer indexes starting from 0.

Concepts Covered

  • pd.Series()
  • List to Series
  • Default Index

2. Python Program to Create a Series with Custom Index

Problem Statement

Write a Python program to create a Pandas Series using custom index labels.

Python Solution

import pandas as pd

marks = pd.Series(
    [85, 90, 78, 88],
    index=["Rahul", "Aman", "Priya", "Sneha"]
)

print(marks)

Sample Output

Rahul    85
Aman     90
Priya    78
Sneha    88
dtype: int64

Explanation

The index parameter allows you to assign meaningful labels instead of numeric indexes.

Concepts Covered

  • Custom Index
  • Series Labels
  • pd.Series()

3. Python Program to Create a Series from a Dictionary

Problem Statement

Write a Python program to create a Pandas Series using a Python dictionary.

Python Solution

import pandas as pd

student_marks = {
    "Rahul": 85,
    "Aman": 90,
    "Priya": 78,
    "Sneha": 88
}

series = pd.Series(student_marks)

print(series)

Sample Output

Rahul    85
Aman     90
Priya    78
Sneha    88
dtype: int64

Explanation

When a dictionary is passed to pd.Series(), dictionary keys become indexes and dictionary values become Series values.

Concepts Covered

  • Dictionary to Series
  • Keys as Index
  • pd.Series()

4. Python Program to Access a Value Using Index Label

Problem Statement

Write a Python program to access a specific value from a Pandas Series using its index label.

Python Solution

import pandas as pd

marks = pd.Series(
    [85, 90, 78, 88],
    index=["Rahul", "Aman", "Priya", "Sneha"]
)

print("Marks of Priya:")
print(marks["Priya"])

Sample Output

Marks of Priya:
78

Explanation

You can directly access any value in a Series using its index label.

Concepts Covered

  • Indexing
  • Label-Based Access
  • Series Index

5. Python Program to Access Multiple Values from a Series

Problem Statement

Write a Python program to access multiple values from a Pandas Series using a list of index labels.

Python Solution

import pandas as pd

marks = pd.Series(
    [85, 90, 78, 88, 95],
    index=["Rahul", "Aman", "Priya", "Sneha", "Rohit"]
)

print(marks[["Rahul", "Sneha", "Rohit"]])

Sample Output

Rahul    85
Sneha    88
Rohit    95
dtype: int64

Explanation

Passing a list of index labels returns multiple values from the Series.

Concepts Covered

  • Multiple Index Selection
  • Series Indexing
  • Label-Based Selection

6. Python Program to Access Series Values Using Integer Index

Problem Statement

Write a Python program to access values from a Pandas Series using integer positions.

Python Solution

import pandas as pd

cities = pd.Series(["Delhi", "Mumbai", "Jaipur", "Pune", "Chennai"])

print("First City:", cities[0])
print("Third City:", cities[2])
print("Last City:", cities[4])

Sample Output

First City: Delhi
Third City: Jaipur
Last City: Chennai

Explanation

A Pandas Series supports integer indexing, allowing you to access elements based on their position.

Concepts Covered

  • Integer Indexing
  • Series Access
  • Position-Based Selection

7. Python Program to Slice a Pandas Series

Problem Statement

Write a Python program to display a subset of values from a Pandas Series using slicing.

Python Solution

import pandas as pd

numbers = pd.Series([10, 20, 30, 40, 50, 60, 70])

print(numbers[2:6])

Sample Output

2    30
3    40
4    50
5    60
dtype: int64

Explanation

Series slicing works similarly to Python lists. The starting index is included, while the ending index is excluded.

Concepts Covered

  • Series Slicing
  • Index Range
  • Data Selection

8. Python Program to Update a Value in a Series

Problem Statement

Write a Python program to update an existing value in a Pandas Series.

Python Solution

import pandas as pd

marks = pd.Series(
    [85, 90, 78, 88],
    index=["Rahul", "Aman", "Priya", "Sneha"]
)

marks["Priya"] = 95

print(marks)

Sample Output

Rahul    85
Aman     90
Priya    95
Sneha    88
dtype: int64

Explanation

A Series is mutable, meaning values can be modified after creation using the index label.

Concepts Covered

  • Updating Values
  • Mutable Series
  • Assignment

9. Python Program to Add a New Element to a Series

Problem Statement

Write a Python program to add a new element to an existing Pandas Series.

Python Solution

import pandas as pd

marks = pd.Series(
    [85, 90, 78],
    index=["Rahul", "Aman", "Priya"]
)

marks["Sneha"] = 88

print(marks)

Sample Output

Rahul    85
Aman     90
Priya    78
Sneha    88
dtype: int64

Explanation

If the specified index does not exist, Pandas automatically creates a new element with that index.

Concepts Covered

  • Adding Elements
  • Dynamic Series
  • Index Assignment

10. Python Program to Delete an Element from a Series

Problem Statement

Write a Python program to remove a specific element from a Pandas Series.

Python Solution

import pandas as pd

marks = pd.Series(
    [85, 90, 78, 88],
    index=["Rahul", "Aman", "Priya", "Sneha"]
)

marks = marks.drop("Aman")

print(marks)

Sample Output

Rahul    85
Priya    78
Sneha    88
dtype: int64

Explanation

The drop() function removes the specified index from the Series and returns a new Series.

Concepts Covered

  • drop()
  • Removing Elements
  • Series Modification

11. Python Program to Find the Maximum and Minimum Values in a Series

Problem Statement

Write a Python program to find the maximum and minimum values in a Pandas Series.

Python Solution

import pandas as pd

marks = pd.Series([85, 90, 78, 88, 95, 81])

print("Maximum Marks:", marks.max())
print("Minimum Marks:", marks.min())

Sample Output

Maximum Marks: 95
Minimum Marks: 78

Explanation

The max() function returns the highest value, while min() returns the lowest value in the Series.

Concepts Covered

  • max()
  • min()
  • Aggregation Functions

12. Python Program to Calculate the Sum and Average of a Series

Problem Statement

Write a Python program to calculate the total sum and average of all values in a Pandas Series.

Python Solution

import pandas as pd

marks = pd.Series([85, 90, 78, 88, 95])

print("Total Marks:", marks.sum())
print("Average Marks:", marks.mean())

Sample Output

Total Marks: 436
Average Marks: 87.2

Explanation

  • sum() calculates the total of all values.
  • mean() calculates the arithmetic average.

Concepts Covered

  • sum()
  • mean()
  • Statistical Functions

13. Python Program to Count the Number of Elements in a Series

Problem Statement

Write a Python program to count the total number of elements in a Pandas Series.

Python Solution

import pandas as pd

cities = pd.Series([
    "Delhi",
    "Mumbai",
    "Jaipur",
    "Pune",
    "Chennai"
])

print("Total Elements:", cities.count())

Sample Output

Total Elements: 5

Explanation

The count() function returns the number of non-missing values present in the Series.

Concepts Covered

  • count()
  • Non-Null Values
  • Series Statistics

14. Python Program to Sort Values in a Series

Problem Statement

Write a Python program to sort the values of a Pandas Series in ascending order.

Python Solution

import pandas as pd

numbers = pd.Series([45, 12, 87, 23, 65])

sorted_numbers = numbers.sort_values()

print(sorted_numbers)

Sample Output

1    12
3    23
0    45
4    65
2    87
dtype: int64

Explanation

The sort_values() function sorts the values while preserving their original indexes.

Concepts Covered

  • sort_values()
  • Sorting
  • Ascending Order

15. Python Program to Sort a Series by Index

Problem Statement

Write a Python program to sort a Pandas Series according to its index labels.

Python Solution

import pandas as pd

marks = pd.Series(
    [85, 90, 78, 88],
    index=["Rahul", "Aman", "Priya", "Sneha"]
)

print(marks.sort_index())

Sample Output

Aman      90
Priya     78
Rahul     85
Sneha     88
dtype: int64

Explanation

The sort_index() function arranges the Series according to its index labels in alphabetical order.

Concepts Covered

  • sort_index()
  • Index Sorting
  • Series Labels

16. Python Program to Check Whether a Value Exists in a Series

Problem Statement

Write a Python program to check whether a specific value exists in a Pandas Series.

Python Solution

import pandas as pd

marks = pd.Series([85, 90, 78, 88, 95])

value = 90

if value in marks.values:
    print(f"{value} exists in the Series.")
else:
    print(f"{value} does not exist in the Series.")

Sample Output

90 exists in the Series.

Explanation

The values attribute returns all values stored in the Series as a NumPy array. The in operator is then used to check whether the specified value exists.

Concepts Covered

  • values
  • Membership Operator
  • Searching in Series

17. Python Program to Filter Values Greater Than a Given Number

Problem Statement

Write a Python program to display all values greater than 80 from a Pandas Series.

Python Solution

import pandas as pd

marks = pd.Series([85, 90, 78, 88, 95, 67, 81])

filtered_marks = marks[marks > 80]

print(filtered_marks)

Sample Output

0    85
1    90
3    88
4    95
6    81
dtype: int64

Explanation

Boolean indexing filters the Series based on a specified condition. Only values satisfying the condition are returned.

Concepts Covered

  • Boolean Indexing
  • Conditional Filtering
  • Series Selection

Chapter Summary

In this chapter, you learned how to work with Pandas Series, including creating a Series from lists and dictionaries, using custom indexes, accessing and updating values, adding and deleting elements, performing statistical operations, sorting data, checking the existence of values, and filtering data using conditions. These concepts provide a strong foundation for working efficiently with one-dimensional data in Pandas.

Key Takeaways

  • A Pandas Series is a one-dimensional labeled data structure.
  • You can create a Series from lists, dictionaries, NumPy arrays, and scalar values.
  • Series supports both integer indexing and custom label indexing.
  • Values in a Series can be updated, added, and removed.
  • Built-in functions like sum(), mean(), max(), and min() simplify data analysis.
  • Sorting and filtering operations are easy using Pandas methods.
  • Series serves as the building block for Pandas DataFrames.

Frequently Asked Questions (FAQs)

1. What is a Pandas Series?

A Pandas Series is a one-dimensional labeled array capable of storing different data types such as integers, floats, strings, and objects.

2. How do you create a Series in Pandas?

Use the pd.Series() function.

import pandas as pd

series = pd.Series([10, 20, 30])

3. Can a Series have custom indexes?

Yes. You can use the index parameter to assign custom labels.

import pandas as pd

series = pd.Series([85, 90], index=["Rahul", "Aman"])

4. How do you access a value in a Series?

You can access values using either the index label or the integer position.

print(series["Rahul"])

5. Which function is used to sort a Series?

Use:

  • sort_values() → Sort by values
  • sort_index() → Sort by index

6. How do you calculate the average of a Series?

Use the mean() function.

print(series.mean())

7. How do you filter values in a Series?

Use Boolean indexing.

import pandas as pd

marks = pd.Series([85, 90, 78, 88])

print(marks[marks > 80])

8. What is the difference between a Python list and a Pandas Series?

A Python list stores data without labels, whereas a Pandas Series stores data with indexes, making searching, filtering, and analysis much easier.

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

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