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<strong>“I don’t even feel like I’ve scratched the surface of what I can do with Python”</strong>
</p><p>With <em>Python Tricks: The Book</em> you’ll discover Python’s best practices and the power of beautiful &amp; Pythonic code with simple examples and a step-by-step narrative.</p><p>You'll get one step closer to mastering Python, so you can write beautiful and idiomatic code that comes to you naturally.</p><p>Learning the ins and outs of Python is difficult—and with this book you'll be able to focus on the practical skills that really matter. Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today.</p><p>
<strong>Who Should Read This Book:</strong>
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<em>If you’re wondering which lesser known parts in Python you should know about</em>, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review.
<em>If you’ve got experience with legacy versions of Python</em>, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2.
<em>If you’ve worked with other programming languages and you want to get up to speed with Python</em>, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista.
<em>If you want to make Python your own and learn how to write clean and Pythonic code</em>, you’ll discover best practices and little-known tricks to round out your knowledge.
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<strong>What Python Developers Say About The Book:</strong>
</p><p><em>"I kept thinking that I wished I had access to a book like this when I started learning Python many years ago."</em> — Mariatta Wijaya, Python Core Developer</p><p><em>"This book makes you write better Python code!"</em> — Bob Belderbos, Software Developer at Oracle</p><p><em>"Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty."</em> — Ben Felder, Pythonista</p><p><em>"It's like having a seasoned tutor explaining, well, tricks!"</em> — Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.</p>

Finding great data analysts is difficult. Despite the explosive growth of data in industries ranging from manufacturing and retail to high technology, finance, and healthcare, learning and accessing data analysis tools has remained a challenge. This pragmatic guide will help train you in one of the most important tools in the field - Python. Filled with practical case studies, Python for Data Analysis demonstrates the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python. It also serves as a modern introduction to scientific computing in Python for data-intensive applications. Learn about the growing field of data analysis from an expert in the community. Learn everything you need to start doing real data analysis work with Python Get the most complete instruction on the basics of the "modern scientific Python platform" Learn from an insider who builds tools for the scientific stack Get an excellent introduction for novices and a wealth of advanced methods for experienced analysts

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming.