Python Pandas Table Save

Python Pandas is a widely used data analysis library in Python. One of its main functionalities is its ability to manipulate and analyze tabular data with ease. In this article, we will give an introduction to the various methods that can be used to save a Pandas table in multiple file formats.

Exporting DataFrames to Various File Formats

1. CSV File

The most common way to save a Pandas table is by exporting it to a CSV file. The to_csv() method of the Pandas DataFrame class is used for this purpose.

import pandas as pd

# Creating a sample DataFrame
df = pd.DataFrame({'Name': ['John', 'Marry', 'Alex'], 
                   'Age': [25, 26, 28],
                   'Salary': [50000, 60000, 45000]})

# Save DataFrame to CSV file
df.to_csv('data.csv')

2. Excel File

Pandas also provides support for exporting tables to Excel files. The to_excel() method can be used to save the DataFrame in an Excel file.

# Save DataFrame to Excel file
df.to_excel('data.xlsx', sheet_name='Sheet1', index=False)

• sheet_name: Specifies the name of the worksheet in the Excel file.
• index: Using False ensures that the DataFrame index is not included in the exported Excel file.

3. JSON File

JSON is another format in which Pandas can export the table data in. The to_json() method of the DataFrame class can be used to save the table data to a JSON file.

# Save the DataFrame to a JSON file
df.to_json('data.json')

4. SQL Database

Pandas is capable of connecting to and exporting tables to SQL databases as well. The to_sql() method can be used to save the DataFrame in a SQL database.

# Import necessary libraries for SQL connection
import sqlalchemy
from sqlalchemy import create_engine

# Connect to SQLite database
engine = create_engine('sqlite:///data.db')

# Save the DataFrame to existing SQLite table
df.to_sql('employees', engine, if_exists='replace', index=False)

• if_exists: By default the method chooses to append the data to an existing table but it can be overwritten by passing a value to this parameter. Here we replace any existing data in the table.

Conclusion

In this article, we have seen different ways in which a Pandas table can be saved in various file formats. Do keep in mind that there are plenty more formats available for saving data in Pandas including HTML, LaTeX, pickle, etc.