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alias(), asc(), desc(), cast() & like() functions on Columns of dataframe in PySpark

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Unlock the power of PySpark! 🚀 In this video, we dive deep into essential functions for handling DataFrame columns in PySpark:

🔹 alias() – Create meaningful column aliases for better readability.
🔹 asc() & desc() – Sort your data in ascending or descending order.
🔹 cast() – Effortlessly change data types in columns.
🔹 like() – Filter rows using pattern matching.

You'll learn:
✅ Practical examples to implement these functions.
✅ How to optimize your PySpark DataFrame operations.
✅ Tips to enhance your data engineering skills.

Whether you're a beginner or an experienced data engineer, this tutorial will boost your PySpark expertise! 🛠️

💡 Chapters:
0:00 Introduction
0:45 alias() Function Explained
2:10 asc() & desc() for Sorting
4:30 cast() for Data Type Conversion
6:15 like() for Pattern Matching
8:00 Real-World Use Cases

🌟 Resources:
📚 Official Documentation: PySpark Column Functions
💻 Code Examples: Download from GitHub Repository

🔔 Subscribe for More: Don’t forget to like, comment, and subscribe for weekly PySpark and Data Engineering tutorials!

#PySpark #DataFrame #alias #cast #DataEngineering

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alias(), asc(), desc(), cast() & like() functions on Columns of dataframe in PySpark

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