8.Pandas Input and Output: Read & Write CSV, Excel, JSON, SQL, and Handle Large Datasets

Introduction (200 words)

Input and output (I/O) operations are fundamental when working with data in Python. Whether you’re analyzing sales figures, sensor logs, or web traffic, the ability to seamlessly load and export data is crucial for effective data workflows. The Pandas library offers robust and flexible tools to handle various file formats like CSV, Excel, JSON, and SQL databases.

In this tutorial, you’ll learn how to:

• Read data from different file formats into DataFrames

• Write processed data back to those formats

• Work with large datasets using memory-efficient techniques

We’ll walk through two real-world examples for each format so you can confidently integrate these operations into your data pipeline. By the end, you’ll be able to handle input and output tasks with ease — even when working with large files that don’t fit into memory.

1. Reading Data

✅ Example 1: Read CSV

Output (example):

✅ Example 2: Read Excel

Output:

✅ Example 3: Read JSON

Output:

✅ Example 4: Read from SQL

Output:

2. Writing Data

✅ Example 1: Write to CSV

Creates: products_output.csv

✅ Example 2: Write to Excel

Creates: sales_output.xlsx

✅ Example 3: Write to JSON

Creates: users_output.json

✅ Example 4: Write to SQL

Effect: Table orders_backup added/replaced in shop.db

3. Handling Large Datasets

✅ Example 1: Read Large CSV in Chunks

Output: First row of each chunk printed (memory efficient)

✅ Example 2: Selective Column Loading

Output:

Summary

Input and output operations are the building blocks of every data science project. With Pandas, you can effortlessly read and write data across CSV, Excel, JSON, and SQL formats. We also explored strategies to manage large datasets using chunking and column filtering — techniques that are essential for working in memory-constrained environments.

Now that you’re equipped with these powerful I/O tools, you can integrate them into any data pipeline, automate reporting tasks, or scale your analytics workflows efficiently.