دانلود کتاب پولارهای پایتون برای تحلیل داده‌ها – داده‌های بزرگ خود را با پولارهای پایتون به تحلیل‌های با کارایی بالا تبدیل کنید

Modern data systems demand speed, scalability, and efficiency. As datasets grow larger and workflows become more complex, traditional dataprocessing tools often struggle to keep up with performance and memory constraints. This gap has led to the emergence of new-generation data frameworks designed to handle large-scale data workloads more effectively. Polars is one such framework—built in Rust, designed for performance, and optimized for modern hardware. This book was written to provide a comprehensive, practical, and production-oriented guide to using Polars for data analysis and data engineering workflows. It is intended for readers who want to move beyond basic DataFrame operations and learn how to design high-performance, scalable, and maintainable data pipelines using Polars. The journey begins with a clear introduction to Polars, its core philosophy, and the features that distinguish it from traditional libraries such as Pandas. Early chapters focus on building a strong foundation—understanding Polars’ data structures, configuration options, and input/output capabilities —ensuring readers gain confidence in working with the framework from the ground up. As the book progresses, readers are guided through increasingly advanced data transformation techniques, including grouping, joining, reshaping, window functions, and working with nested data. Dedicated chapters explore data visualization, SQL integration, and extending Polars through user-defined functions and high-performance Rust plugins using PyO3. These topics bridge the gap between exploratory analysis and real-world, production-grade data processing. Special emphasis is placed on performance and scalability. Polars’ lazy execution model, query optimization engine, streaming mode, and GPU support are explained in detail, helping readers understand not just how to write code, but why certain approaches perform better. Profiling, optimization, and testing techniques are covered to ensure readers can confidently deploy Polars-based solutions in demanding environments. The later chapters focus on applied use cases, including market data analysis, machine learning workflows, and big data processing using cloud platforms such as Google Cloud. These chapters demonstrate how Polars integrates seamlessly with modern data ecosystems, enabling efficient ETL pipelines, analytics, and model development. This book is written for data analysts, data engineers, quantitative analysts, and Python developers who want to leverage Polars for high-performance data processing. While prior experience with Python and Data Frames is helpful, the book is structured to gradually build expertise—from fundamentals to advanced topics—making it accessible to both intermediate users and experienced professionals. By the end of this book, readers will have a deep understanding of Polars’ capabilities, best practices for performance optimization, and the confidence to apply Polars effectively in real-world data-intensive applications. My hope is that this book serves not only as a learning resource, but also as a long-term reference for building fast, scalable, and efficient data workflows with Polars.