Importing & Exporting CSV Files using Pandas

One of the most useful features of the Pandas library is its ability to read data from external files and save processed data back to files. The most commonly used file format is the CSV (Comma-Separated Values) file.

CSV files are widely used in schools, businesses, hospitals, banks, and government organizations because they are simple, lightweight, and supported by almost every spreadsheet and database application.

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What is a CSV File?

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Example of a CSV File

student.csv

Name,Marks,City
Amit,85,Jaipur
Neha,92,Delhi
Rohan,78,Mumbai
Priya,88,Jaipur

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Advantages of CSV Files

• Easy to create and edit.
• Supported by Microsoft Excel, Google Sheets, and databases.
• Small file size.
• Easy to exchange data between different software.
• Can be read directly using Pandas.

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Importing a CSV File

To import data from a CSV file into a DataFrame, use the read_csv() function.

Syntax

import pandas as pd

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

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Example 1

import pandas as pd

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

print(df)

Output

    Name  Marks     City
0   Amit     85   Jaipur
1   Neha     92    Delhi
2  Rohan     78   Mumbai
3  Priya     88   Jaipur

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Viewing the First Records

print(df.head())

Displays the first five rows by default.

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Viewing the Last Records

print(df.tail())

Displays the last five rows by default.

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Displaying Specific Columns

print(df["Marks"])

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Displaying Multiple Columns

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

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Exporting Data to a CSV File

After processing data, it can be saved back into a CSV file using the to_csv() function.

Syntax

DataFrame.to_csv("filename.csv")

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Example

import pandas as pd

data = {
    "Name":["Amit","Neha","Rohan"],
    "Marks":[85,92,78]
}

df = pd.DataFrame(data)

df.to_csv("result.csv")

The file result.csv is created in the current working directory.

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The index Parameter

By default, Pandas also saves the row index in the CSV file.

df.to_csv("result.csv")

Saved file:

,Name,Marks
0,Amit,85
1,Neha,92
2,Rohan,78

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Saving Without Index

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

Output File:

Name,Marks
Amit,85
Neha,92
Rohan,78

The index=False parameter prevents the row index from being saved.

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The header Parameter

The header parameter controls whether column names are written to the CSV file.

df.to_csv(
    "result.csv",
    header=False,
    index=False
)

Output:

Amit,85
Neha,92
Rohan,78

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Import → Modify → Export Workflow

CSV File
     │
     ▼
read_csv()
     │
     ▼
DataFrame
     │
Modify / Filter / Update
     │
     ▼
to_csv()
     │
     ▼
New CSV File

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Real-Life Applications

Field	Example
School	Student Result Records
Hospital	Patient Details
Bank	Customer Transactions
Business	Sales Reports
Sports	Player Statistics

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Common Errors

Error	Reason
FileNotFoundError	CSV file does not exist.
PermissionError	File is already open in another application.
ParserError	Incorrect CSV format.
UnicodeDecodeError	Wrong file encoding.

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Difference Between read_csv() and to_csv()

read_csv()	to_csv()
Imports data	Exports data
Creates a DataFrame	Saves a DataFrame
Reads from disk	Writes to disk

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Quick Revision

Function	Purpose
read_csv()	Read CSV file
to_csv()	Save CSV file
index=False	Do not save row index
header=False	Do not save column names
CSV	Comma-Separated Values

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CBSE Exam Tips

• Remember that read_csv() imports data into a DataFrame.
• Use to_csv() to save a DataFrame.
• Remember the purpose of index=False.
• Know the purpose of header=False.
• Be able to identify the output of CSV-related programs.
• Practice reading, modifying, and exporting CSV files for practical examinations.

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Summary

CSV files are one of the most common formats used for storing tabular data. Pandas provides the read_csv() function to import CSV files into a DataFrame and the to_csv() function to export processed data back to CSV files. Understanding these functions is essential for the CBSE practical examination and real-world data analysis tasks.