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If you want to develop complete Python web apps with Django, this Learning Path is for you. It will walk you through Python programming techniques and guide you in implementing them when creating 4 professional Django projects, teaching you how to solve common problems and develop RESTful web services with Django and Python. You will learn how to build a blog application, a social image bookmarking website, an online shop, and an e-learning platform.

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.

Discover everything you need to know about Python to turn your passion of programming into a job you'll love. Fueled by fun and practical examples, this book gives high schoolers who want to learn an easy programming language ideas for how to leverage them in the workforce. Start with the basics and before you know it, you'll be building your own web sites, doing white-hat hacking, finding code bugs and errors, and creating games, including using Python to roll characters for RPGs. Every chapter is relaxed and informal, like learning with a cool teacher all the time.

Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers’ time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools ( https://github.com/open2c/cooltools ), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets.

An introduction to the Python programming language.

SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

An introduction to programming for beginners using Python.

Learn to build and manage better software with clean, intuitive, scalable, maintainable, and high-performance Python code. Key Features ? Comparative analysis of regular and Pythonic coding constructs. ? Illustrates application design paradigms for Python projects. ? Detailed pointers on optimal data processing and application design. ? Highlights accepted conventions for testing and managing production code. Description 'The Pythonic Way' acquaints you with Python's capabilities beyond basic syntax. This book will help you understand widely accepted Pythonic constructs and procedures, thus enabling you to write reliable, optimized, and modular applications.You'll learn about Pythonic data structures, class and object creation, and more. The book then delves into some of Python's lesser-known but incredibly powerful functionalities such as meta-programming, decorators, context managers, generators, and iterators. Additionally, you'll learn how to accelerate computations by using Pandas Series and Dataframes. You will be introduced to various design patterns that work well with Python applications. Finally, we'll discuss testing frameworks and best practices for testing, packaging, launching, and publishing applications in production environments.This book will empower you as you transition from beginner or competitive Python coding to industry-standard Python software development. Intermediate Python developers will gain a deeper understanding of the language's nuances, enabling them to create better software. What you will learn ? Understand common practices for writing scalable and legible Python code. ? Create robust and maintainable production codebases for time and space performant applications. ? Master effective data processing practices and features like generators and decorators to improve complex computations on large datasets. ? Get familiar with Pythonic design patterns for secure, large-scale applications. Who this book is for This book is a valuable reference manual for novice and intermediate programmers and data scientists to learn about Pythonic standards and conventions. For beginners, this book will get you started with Pythonic thinking. This book will serve as a guide to fine-tune your skills beyond syntax and help build robust Python applications for intermediate Python coders. Table of Contents 1. Introduction to Pythonic Code 2. Pythonic Data Structures 3. Classes and OOP Conventions 4. Python Modules and Metaprogramming 5. Pythonic Décorators and Context Managers 6. Data Processing Done Right 7. Iterators, Generators, and Coroutines 8. Python Descriptors 9. Pythonic Application Design and Architecture 10. Effective Testing for Python Code 11. Production Code Management About the Authors Sonal Raj is an engineer, mathematician, data scientist, and Python evangelist from India, who has carved a niche in the financial services domain. He is a Goldman Sachs and D.E. Shaw alumnus who currently heads the data analytics and research efforts for a high-frequency trading firm.He holds a dual master's degree in Computer Science and Business Management and is a former research fellow of the Indian Institute of Science. His areas of research range from image processing, real-time graph computations to electronic trading algorithms and data science. He is a doctoral candidate at the Swiss School of Business Management, Geneva. Over the years, he has implemented low latency platforms, trading strategies, and market signal models. With more than a decade of hands-on experience, he is a community speaker and a Python and data science mentor to newcomers in the field. LinkedIn Profile: https://www.linkedin.com/in/sonalraj/ Blog Link: https://www.sonalraj.com/

\"Usually we think of coding as something only trained experts and scientists can handle, but not any more thanks to programs like Python. First developed in 1991, Python uses lines of code, letters, and symbols, to create computer programs. Python is easier to read and takes fewer lines of code to accomplish tasks than some programming languages. Pythons creator, Guido van Rossum, wanted to create open-source software that used easy-to-understand coding text. His software allows even novice programmers to see results in a short amount of time.\"--Provided by publisher.

The book focuses on using machine learning techniques to uncover cause-and-effect relationships in data, moving beyond mere correlations. From a Pearlian perspective, this involves utilizing causal diagrams (DAGs) and do-calculus to formalize causal reasoning.