📂 Working with Different File Formats in Python

This guide provides a hands-on introduction to working with different file formats in Python, covering essential concepts such as reading, writing, and transforming various file types like CSV, JSON, XLSX, XML, and Binary formats.

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🎯 What You'll Learn

✅ Understand the Data Engineering process
✅ Read and write CSV files using Pandas
✅ Work with JSON files: Serialization and Deserialization
✅ Load and manipulate XLSX files using Pandas
✅ Parse and extract data from XML files
✅ Handle Binary file formats like images
✅ Perform basic data analysis and visualization using Pandas and Seaborn

Perfect for beginners and intermediate learners looking to enhance their file-handling and data manipulation skills with Python! 🚀

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📌 Estimated Time: 40–50 minutes

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📂 Topics Covered

📌 Data Engineering Overview
📌 Extract, Transform, and Load (ETL) Process
📌 Handling CSV Files in Pandas
📌 Reading and Writing JSON Files
📌 Working with XLSX Files in Pandas
📌 Parsing and Manipulating XML Files
📌 Managing Binary Files like Images
📌 Basic Data Analysis and Visualization

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📝 Table of Contents

1. Data Engineering
2. Data Engineering Process
3. Working with Different File Formats
4. Basic Data Analysis and Visualization

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📚 Data Engineering

Data engineering is a critical and foundational skill in any data scientist’s toolkit. It involves extracting, transforming, and loading data from multiple sources to ensure that the data is clean, structured, and ready for analysis.

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🔄 Data Engineering Process

The Data Engineering Process includes the following steps:

1. Extract – Extract data from various sources such as APIs, web scraping, and file formats like CSV, JSON, XLSX, etc.
2. Transform – Clean and modify the data to ensure consistency and convert it into a usable format.
3. Load – Load the transformed data into a data warehouse or analytical platform for further processing.

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📁 Working with Different File Formats

In real-world applications, data is stored in various file formats. It is essential for data engineers and data scientists to handle multiple formats efficiently.