Pandas Reading and Writing Files Practice Questions with Solutions

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Reading and writing files is one of the most important tasks in data analysis. Pandas provides built-in functions to import and export data from different file formats such as CSV, Excel, JSON, and text files. These functions help data analysts load datasets for analysis and save processed data efficiently. In this chapter, you’ll learn how to read, write, and manage files in Pandas using practical examples. Pandas Reading and Writing Files practice questions with solutions help to build concepts.

1. Python Program to Read a CSV File

Problem Statement

Write a Python program to read a CSV file using Pandas.

Python Solution

import pandas as pd

df = pd.read_csv("students.csv")

print(df)

Sample Output

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

Explanation

The read_csv() function imports data from a CSV file into a Pandas DataFrame.

Concepts Covered

  • read_csv()
  • CSV File
  • Data Import

2. Python Program to Display the First Five Rows of a CSV File

Problem Statement

Write a Python program to read a CSV file and display only the first five rows.

Python Solution

import pandas as pd

df = pd.read_csv("students.csv")

print(df.head())

Sample Output

     Name  Age  Marks
0   Rahul   20     85
1    Aman   21     90
2   Priya   19     78
3   Sneha   22     88
4   Rohit   23     95

Explanation

The head() function displays the first five rows after importing the dataset.

Concepts Covered

  • read_csv()
  • head()
  • Data Preview

3. Python Program to Read Selected Columns from a CSV File

Problem Statement

Write a Python program to import only the Name and Marks columns from a CSV file.

Python Solution

import pandas as pd

df = pd.read_csv(
    "students.csv",
    usecols=["Name", "Marks"]
)

print(df)

Sample Output

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

Explanation

The usecols parameter imports only the specified columns, reducing memory usage and improving performance.

Concepts Covered

  • read_csv()
  • usecols
  • Column Selection

4. Python Program to Read a CSV File with a Custom Index

Problem Statement

Write a Python program to use the Name column as the index while reading a CSV file.

Python Solution

import pandas as pd

df = pd.read_csv(
    "students.csv",
    index_col="Name"
)

print(df)

Sample Output

        Age  Marks
Name
Rahul    20     85
Aman     21     90
Priya    19     78
Sneha    22     88

Explanation

The index_col parameter assigns a column as the DataFrame index during file import.

Concepts Covered

  • index_col
  • Custom Index
  • CSV Import

5. Python Program to Save a DataFrame as a CSV File

Problem Statement

Write a Python program to save a DataFrame into a CSV file.

Python Solution

import pandas as pd

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

df = pd.DataFrame(data)

df.to_csv("students_output.csv", index=False)

print("CSV file saved successfully.")

Sample Output

CSV file saved successfully.

Explanation

The to_csv() function exports a DataFrame into a CSV file. Setting index=False prevents row indexes from being written to the file.

Concepts Covered

  • to_csv()
  • CSV Export
  • Data Saving

6. Python Program to Read an Excel File

Problem Statement

Write a Python program to read an Excel file using Pandas.

Python Solution

import pandas as pd

df = pd.read_excel("students.xlsx")

print(df)

Sample Output

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

Explanation

The read_excel() function imports data from an Excel file into a Pandas DataFrame.

Note: Install the openpyxl package if it is not already installed.

pip install openpyxl

Concepts Covered

  • read_excel()
  • Excel File
  • Data Import

7. Python Program to Read a Specific Sheet from an Excel File

Problem Statement

Write a Python program to read a specific worksheet from an Excel file.

Python Solution

import pandas as pd

df = pd.read_excel(
    "students.xlsx",
    sheet_name="Sheet1"
)

print(df)

Sample Output

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

Explanation

The sheet_name parameter specifies which worksheet should be imported.

Concepts Covered

  • sheet_name
  • Excel Worksheet
  • read_excel()

8. Python Program to Save a DataFrame as an Excel File

Problem Statement

Write a Python program to export a DataFrame to an Excel file.

Python Solution

import pandas as pd

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

df = pd.DataFrame(data)

df.to_excel("students_output.xlsx", index=False)

print("Excel file saved successfully.")

Sample Output

Excel file saved successfully.

Explanation

The to_excel() function exports the DataFrame into an Excel workbook. The index=False argument prevents row indexes from being stored.

Concepts Covered

  • to_excel()
  • Excel Export
  • Data Saving

9. Python Program to Read a JSON File

Problem Statement

Write a Python program to read a JSON file using Pandas.

Python Solution

import pandas as pd

df = pd.read_json("students.json")

print(df)

Sample Output

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

Explanation

The read_json() function imports structured JSON data into a DataFrame.

Concepts Covered

  • read_json()
  • JSON File
  • Data Import

10. Python Program to Save a DataFrame as a JSON File

Problem Statement

Write a Python program to export a DataFrame as a JSON file.

Python Solution

import pandas as pd

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

df = pd.DataFrame(data)

df.to_json("students_output.json", orient="records", indent=4)

print("JSON file saved successfully.")

Sample Output

JSON file saved successfully.

Explanation

The to_json() function exports a DataFrame into JSON format. The orient="records" parameter stores each row as a JSON object, while indent=4 makes the file more readable.

Concepts Covered

  • to_json()
  • JSON Export
  • orient
  • Data Serialization

11. Python Program to Read a Text File Using Pandas

Problem Statement

Write a Python program to read a text file using Pandas.

Python Solution

import pandas as pd

df = pd.read_table("students.txt")

print(df)

Sample Output

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

Explanation

The read_table() function reads tab-separated text files and loads the data into a Pandas DataFrame.

Concepts Covered

  • read_table()
  • Text File
  • Data Import

12. Python Program to Read a CSV File Without Header

Problem Statement

Write a Python program to read a CSV file that does not contain column headers.

Python Solution

import pandas as pd

df = pd.read_csv(
    "students.csv",
    header=None
)

print(df)

Sample Output

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

Explanation

Setting header=None tells Pandas that the file has no header row, so it automatically assigns numeric column labels.

Concepts Covered

  • header=None
  • CSV Import
  • Column Labels

13. Python Program to Rename Columns While Reading a CSV File

Problem Statement

Write a Python program to assign custom column names while importing a CSV file.

Python Solution

import pandas as pd

columns = ["Student", "Age", "Marks"]

df = pd.read_csv(
    "students.csv",
    names=columns,
    header=None
)

print(df)

Sample Output

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

Explanation

The names parameter assigns custom column names while importing the file.

Concepts Covered

  • names
  • Custom Column Names
  • CSV Import

14. Python Program to Read Only the First Three Rows of a CSV File

Problem Statement

Write a Python program to import only the first three rows of a CSV file.

Python Solution

import pandas as pd

df = pd.read_csv(
    "students.csv",
    nrows=3
)

print(df)

Sample Output

     Name  Age  Marks
0   Rahul   20     85
1    Aman   21     90
2   Priya   19     78

Explanation

The nrows parameter limits the number of rows read from the file, making it useful for previewing large datasets.

Concepts Covered

  • nrows
  • Partial Data Import
  • CSV Reading

15. Python Program to Read a CSV File While Skipping Rows

Problem Statement

Write a Python program to skip the first two rows while reading a CSV file.

Python Solution

import pandas as pd

df = pd.read_csv(
    "students.csv",
    skiprows=2
)

print(df)

Sample Output

    Aman   21   90
0  Priya   19   78
1 Sneha   22   88

Explanation

The skiprows parameter skips the specified number of rows before importing the remaining data.

Concepts Covered

  • skiprows
  • CSV Reading
  • Data Import Options

16. Python Program to Check Whether a File Exists Before Reading

Problem Statement

Write a Python program to check whether a CSV file exists before reading it using Pandas.

Python Solution

import os
import pandas as pd

file_name = "students.csv"

if os.path.exists(file_name):
    df = pd.read_csv(file_name)
    print(df)
else:
    print("File not found.")

Sample Output

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

Explanation

The os.path.exists() function checks whether the specified file exists before attempting to read it. This helps prevent runtime errors such as FileNotFoundError.

Concepts Covered

  • os.path.exists()
  • File Validation
  • read_csv()

17. Python Program to Save Selected Columns to a New CSV File

Problem Statement

Write a Python program to save only the Name and Marks columns from a DataFrame into a new CSV file.

Python Solution

import pandas as pd

df = pd.read_csv("students.csv")

selected_data = df[["Name", "Marks"]]

selected_data.to_csv(
    "selected_students.csv",
    index=False
)

print("Selected columns saved successfully.")

Sample Output

Selected columns saved successfully.

Explanation

You can select specific columns from a DataFrame and export them to a new CSV file using the to_csv() function.

Concepts Covered

  • Column Selection
  • to_csv()
  • CSV Export

Chapter Summary

In this chapter, you learned how to read and write files using Pandas. You explored importing data from CSV, Excel, JSON, and text files, selecting specific columns, assigning custom indexes, exporting DataFrames to different file formats, reading files with custom options such as header, names, nrows, and skiprows, checking whether files exist before reading them, and saving selected columns into new files. These file handling techniques are essential for working with real-world datasets in data analysis projects.

Key Takeaways

  • read_csv() imports CSV files into a DataFrame.
  • to_csv() exports a DataFrame to a CSV file.
  • read_excel() and to_excel() handle Excel files.
  • read_json() and to_json() work with JSON data.
  • read_table() imports tab-separated text files.
  • Parameters like usecols, header, names, nrows, and skiprows provide flexible file-reading options.
  • os.path.exists() helps verify that a file exists before reading it.
  • Pandas supports reading and writing multiple file formats for efficient data analysis.

Frequently Asked Questions (FAQs)

1. Which function is used to read a CSV file in Pandas?

Use the read_csv() function.

import pandas as pd

df = pd.read_csv("students.csv")

2. How do you save a DataFrame as a CSV file?

Use the to_csv() function.

df.to_csv("output.csv", index=False)

3. Which function is used to read Excel files?

Use the read_excel() function.

df = pd.read_excel("students.xlsx")

4. How do you read only selected columns from a CSV file?

Use the usecols parameter.

df = pd.read_csv(
    "students.csv",
    usecols=["Name", "Marks"]
)

5. How do you import only the first few rows of a file?

Use the nrows parameter.

df = pd.read_csv(
    "students.csv",
    nrows=5
)

6. How do you check whether a file exists before reading it?

Use the os.path.exists() function.

import os

print(os.path.exists("students.csv"))

7. Can Pandas read JSON files?

Yes. Use the read_json() function.

df = pd.read_json("students.json")

8. Why is file handling important in Pandas?

File handling allows you to import datasets from different sources, process and analyze the data, and export the results into various formats such as CSV, Excel, and JSON. It is one of the core skills required for data analysis, business intelligence, and machine learning projects.

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

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