Pandas Data Selection and Indexing Practice Questions with Solutions

Data selection and indexing are among the most important skills in Pandas. They allow you to retrieve specific rows, columns, or subsets of data efficiently from a DataFrame. Pandas provides several powerful methods for data selection, including square bracket notation ([]), loc[], iloc[], Boolean indexing, and conditional filtering. Mastering these techniques is essential for cleaning, analyzing, and transforming datasets in real-world data analysis projects. Pandas Data Selection and Indexing practice questions with solutions help to understand the concepts.

1. Python Program to Select a Single Column from a DataFrame

Problem Statement

Write a Python program to select and display the Name column from a Pandas DataFrame.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Age": [20, 21, 19, 22],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

print(df["Name"])

Sample Output

0    Rahul
1     Aman
2    Priya
3   Sneha
Name: Name, dtype: object

Explanation

Selecting a single column using square brackets returns a Pandas Series containing all values from that column.

Concepts Covered

• Column Selection
• Series
• Square Bracket Notation

2. Python Program to Select Multiple Columns

Problem Statement

Write a Python program to display the Name and Marks columns from a DataFrame.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Age": [20, 21, 19, 22],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

print(df[["Name", "Marks"]])

Sample Output

     Name  Marks
0   Rahul     85
1    Aman     90
2   Priya     78
3   Sneha     88

Explanation

To retrieve multiple columns, pass a list of column names inside double square brackets.

Concepts Covered

• Multiple Column Selection
• DataFrame Columns
• List Indexing

3. Python Program to Select a Single Row Using loc[]

Problem Statement

Write a Python program to display the third row of a DataFrame using loc[].

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Age": [20, 21, 19, 22],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

print(df.loc[2])

Sample Output

Name     Priya
Age         19
Marks       78
Name: 2, dtype: object

Explanation

The loc[] method selects rows using index labels. Since the default index starts from 0, index 2 represents the third row.

Concepts Covered

• loc[]
• Label-Based Indexing
• Row Selection

4. Python Program to Select Multiple Rows Using loc[]

Problem Statement

Write a Python program to display the first three rows using loc[].

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha", "Rohit"],
    "Marks": [85, 90, 78, 88, 95]
}

df = pd.DataFrame(data)

print(df.loc[0:2])

Sample Output

     Name  Marks
0   Rahul     85
1    Aman     90
2   Priya     78

Explanation

Unlike Python slicing, loc[] includes both the starting and ending index labels.

Concepts Covered

• loc[]
• Row Range Selection
• Label-Based Slicing

5. Python Program to Select a Row Using iloc[]

Problem Statement

Write a Python program to retrieve the fourth row using iloc[].

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Age": [20, 21, 19, 22],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

print(df.iloc[3])

Sample Output

Name     Sneha
Age         22
Marks       88
Name: 3, dtype: object

Explanation

The iloc[] method selects rows using integer positions rather than index labels.

Concepts Covered

• iloc[]
• Integer Position
• Row Selection

6. Python Program to Select Multiple Rows Using iloc[]

Problem Statement

Write a Python program to display the first four rows of a DataFrame using iloc[].

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha", "Rohit", "Anjali"],
    "Age": [20, 21, 19, 22, 23, 20],
    "Marks": [85, 90, 78, 88, 95, 91]
}

df = pd.DataFrame(data)

print(df.iloc[0:4])

Sample Output

     Name  Age  Marks
0   Rahul   20     85
1    Aman   21     90
2   Priya   19     78
3   Sneha   22     88

Explanation

The iloc[] method uses integer positions for slicing. Like Python lists, the ending position is excluded.

Concepts Covered

• iloc[]
• Row Slicing
• Integer Indexing

7. Python Program to Select Specific Rows and Columns Using loc[]

Problem Statement

Write a Python program to display only the Name and Marks columns for the first three rows using loc[].

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Age": [20, 21, 19, 22],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

print(df.loc[0:2, ["Name", "Marks"]])

Sample Output

     Name  Marks
0   Rahul     85
1    Aman     90
2   Priya     78

Explanation

The loc[] method allows simultaneous selection of rows and columns using labels.

Concepts Covered

• loc[]
• Row and Column Selection
• Label Indexing

8. Python Program to Select Specific Rows and Columns Using iloc[]

Problem Statement

Write a Python program to display the first three rows and the first two columns using iloc[].

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Age": [20, 21, 19, 22],
    "Marks": [85, 90, 78, 88]
}

df = pd.DataFrame(data)

print(df.iloc[0:3, 0:2])

Sample Output

     Name  Age
0   Rahul   20
1    Aman   21
2   Priya   19

Explanation

The iloc[] method selects both rows and columns using integer positions.

Concepts Covered

• iloc[]
• Row and Column Selection
• Integer Position

9. Python Program to Select Rows Based on a Condition

Problem Statement

Write a Python program to display students whose marks are greater than 85.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha", "Rohit"],
    "Marks": [85, 90, 78, 88, 95]
}

df = pd.DataFrame(data)

print(df[df["Marks"] > 85])

Sample Output

     Name  Marks
1    Aman     90
3   Sneha     88
4   Rohit     95

Explanation

Boolean indexing filters rows that satisfy the specified condition.

Concepts Covered

• Boolean Indexing
• Conditional Selection
• Filtering Data

10. Python Program to Select Rows Using Multiple Conditions

Problem Statement

Write a Python program to display students whose Age is greater than 20 and Marks are greater than or equal to 90.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha", "Rohit"],
    "Age": [20, 21, 19, 22, 23],
    "Marks": [85, 90, 78, 88, 95]
}

df = pd.DataFrame(data)

result = df[(df["Age"] > 20) & (df["Marks"] >= 90)]

print(result)

Sample Output

    Name  Age  Marks
1   Aman   21     90
4  Rohit   23     95

Explanation

Multiple conditions can be combined using:

• & → AND
• | → OR

Each condition must be enclosed within parentheses.

Concepts Covered

• Boolean Operators
• AND Operator
• Conditional Filtering
• Multiple Conditions

11. Python Program to Select Rows Using the OR Operator

Problem Statement

Write a Python program to display students whose Marks are greater than or equal to 90 or Age is less than 20.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha", "Rohit"],
    "Age": [20, 21, 19, 22, 23],
    "Marks": [85, 90, 78, 88, 95]
}

df = pd.DataFrame(data)

result = df[(df["Marks"] >= 90) | (df["Age"] < 20)]

print(result)

Sample Output

    Name  Age  Marks
1   Aman   21     90
2  Priya   19     78
4  Rohit   23     95

Explanation

The | (OR) operator returns rows that satisfy at least one of the specified conditions.

Concepts Covered

• OR Operator
• Boolean Indexing
• Conditional Filtering

12. Python Program to Select Rows Using isin()

Problem Statement

Write a Python program to display students whose names are Rahul or Sneha.

Python Solution

import pandas as pd

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha", "Rohit"],
    "Marks": [85, 90, 78, 88, 95]
}

df = pd.DataFrame(data)

print(df[df["Name"].isin(["Rahul", "Sneha"])])

Sample Output

     Name  Marks
0   Rahul     85
3   Sneha     88

Explanation

The isin() function checks whether each value belongs to the specified list and returns matching rows.

Concepts Covered

• isin()
• Membership Filtering
• Boolean Selection

13. Python Program to Select Rows with Missing Values

Problem Statement

Write a Python program to display all rows containing missing values.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Marks": [85, np.nan, 78, np.nan]
}

df = pd.DataFrame(data)

print(df[df["Marks"].isnull()])

Sample Output

     Name  Marks
1    Aman    NaN
3   Sneha    NaN

Explanation

The isnull() function identifies missing values. It is commonly used during data cleaning.

Concepts Covered

• isnull()
• Missing Values
• Data Filtering

14. Python Program to Select Rows Without Missing Values

Problem Statement

Write a Python program to display only rows that do not contain missing values in the Marks column.

Python Solution

import pandas as pd
import numpy as np

data = {
    "Name": ["Rahul", "Aman", "Priya", "Sneha"],
    "Marks": [85, np.nan, 78, 92]
}

df = pd.DataFrame(data)

print(df[df["Marks"].notnull()])

Sample Output

     Name  Marks
0   Rahul   85.0
2   Priya   78.0
3   Sneha   92.0

Explanation

The notnull() function returns only those rows where the selected column contains valid (non-missing) values.

Concepts Covered

• notnull()
• Data Cleaning
• Missing Values

15. Python Program to Select Rows Using query()

Problem Statement

Write a Python program to select students whose marks are greater than 80 using the query() function.

Python Solution

import pandas as pd

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

df = pd.DataFrame(data)

result = df.query("Marks > 80")

print(result)

Sample Output

     Name  Marks
0   Rahul     85
1    Aman     90
3   Sneha     88

Explanation

The query() function filters rows using a readable string expression instead of Boolean indexing.

Concepts Covered

• query()
• Conditional Filtering
• Data Selection

16. Python Program to Select Unique Values from a Column

Problem Statement

Write a Python program to display all unique values present in the Course column of a DataFrame.

Python Solution

import pandas as pd

data = {
    "Student": ["Rahul", "Aman", "Priya", "Sneha", "Rohit"],
    "Course": [
        "Python",
        "Java",
        "Python",
        "Data Science",
        "Java"
    ]
}

df = pd.DataFrame(data)

print(df["Course"].unique())

Sample Output

['Python' 'Java' 'Data Science']

Explanation

The unique() function returns only the distinct values from a column. It is useful for identifying categories, removing duplicates, and exploring categorical data.

Concepts Covered

• unique()
• Distinct Values
• Data Exploration

17. Python Program to Count Unique Values in a Column

Problem Statement

Write a Python program to count how many unique values exist in the Course column.

Python Solution

import pandas as pd

data = {
    "Student": ["Rahul", "Aman", "Priya", "Sneha", "Rohit"],
    "Course": [
        "Python",
        "Java",
        "Python",
        "Data Science",
        "Java"
    ]
}

df = pd.DataFrame(data)

print("Total Unique Courses:", df["Course"].nunique())

Sample Output

Total Unique Courses: 3

Explanation

The nunique() function counts the number of distinct values present in a column without displaying them.

Concepts Covered

• nunique()
• Unique Count
• Data Analysis

Chapter Summary

In this chapter, you learned how to perform data selection and indexing in Pandas using different techniques. You explored selecting rows and columns with square brackets, loc[], and iloc[], filtering data with Boolean conditions, using logical operators (& and |), selecting values with isin(), identifying missing values using isnull() and notnull(), filtering data with query(), and retrieving unique values using unique() and nunique(). These operations are fundamental for efficient data analysis and real-world data manipulation.

Key Takeaways

• Use square brackets ([]) to select columns from a DataFrame.
• loc[] performs label-based indexing.
• iloc[] performs integer position-based indexing.
• Boolean indexing is useful for filtering rows based on conditions.
• Combine multiple conditions using & (AND) and | (OR).
• Use isin() to filter rows that match multiple values.
• isnull() and notnull() help identify missing data.
• query() provides a clean and readable way to filter data.
• unique() returns distinct values, while nunique() counts them.

Frequently Asked Questions (FAQs)

1. What is data selection in Pandas?

Data selection is the process of retrieving specific rows, columns, or subsets of data from a DataFrame for analysis.

2. What is the difference between loc[] and iloc[]?

• loc[] selects data using row or column labels.
• iloc[] selects data using integer positions.

3. How do you filter rows based on a condition?

Use Boolean indexing.

filtered_df = df[df["Marks"] > 80]

print(filtered_df)

4. What does the isin() function do?

The isin() function filters rows by checking whether values exist in a specified list.

df[df["Course"].isin(["Python", "Java"])]

5. How do you find missing values in Pandas?

Use the isnull() function.

df.isnull()

6. What is the purpose of the query() function?

The query() function filters DataFrame rows using a readable string expression instead of Boolean indexing.

df.query("Marks >= 85")

7. What is the difference between unique() and nunique()?

• unique() returns all distinct values.
• nunique() returns the total number of distinct values.

8. Why is indexing important in Pandas?

Indexing enables fast data retrieval, filtering, slicing, and analysis, making it one of the most essential features of the Pandas library for data manipulation.

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

Rishabh Kumar