Reading and Writing with Pandas - ( Pandas Reading and Writing Files )

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Syntax

Reading CSV Files

import pandas as pd

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

Writing CSV Files

import pandas as pd

df.to_csv('filename.csv', index=False)

Example

Reading a CSV file

import pandas as pd

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

Writing a CSV file

import pandas as pd

df.to_csv('students_new.csv', index=False)

Output

The read_csv() method returns a pandas DataFrame object with the data from the CSV file. The to_csv() method writes a pandas DataFrame object to a CSV file.

Explanation

Pandas provides various methods to read and write different file formats. The most commonly used file format is the comma-separated values (CSV) file format. Pandas provides the read_csv() method to read data from CSV files, and the to_csv() method to write pandas DataFrames to CSV files.

In the above example, the read_csv() method is used to read the data from the 'students.csv' file and store it in a pandas DataFrame object. The to_csv() method is used to write the same DataFrame object to a new CSV file named 'students_new.csv'.

Use

Pandas provides a simple way to read and write various file formats, including CSV, Excel, JSON, etc. Reading and writing files using pandas can be used in various data science applications, such as data cleaning, data preprocessing, exploratory analysis, and more.

Important Points

• Pandas provides various methods to read and write different file formats.
• The read_csv() method is used to read data from CSV files.
• The to_csv() method is used to write pandas DataFrames to CSV files.

Summary

In conclusion, reading and writing files with pandas is an essential part of working with data in data science. The read_csv() and to_csv() methods in Pandas provide a simple way to read and write CSV files. These methods can be customized with various optional arguments to handle different CSV file formats and data types.