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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.

This book – composed of two volumes – explores the syntactical constructs of the most common programming languages, and sheds a mathematical light on their semantics, providing also an accurate presentation of the material aspects that interfere with coding. Concepts and Semantics of Programming Languages 2 presents an original semantic model, collectively taking into account all of the constructs and operations of modules and classes: visibility, import, export, delayed definitions, parameterization by types and values, extensions, etc. The model serves for the study of Ada and OCaml modules, as well as C header files. It can be deployed to model object and class features, and is thus used to describe Java, C++, OCaml and Python classes. This book is intended not only for computer science students and teachers but also seasoned programmers, who will find a guide to reading reference manuals and the foundations of program verification.

Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems. Python and NumPy are included and make it easy for new learners of PySpark to understand and adopt the model. What You Will Learn: Understand the advanced features of PySpark and SparkSQL Optimize your code Program SparkSQL with Python Use Spark Streaming and Spark MLlib with Python Perform graph analysis with GraphFrames.

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share applications related to data analysis and visualization.

Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best 'swiss army knife' we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a 'sweet spot' where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses. Clear guidance on how to interpret results. Primary instruction in R but added sections for Python coders. Discussion exercises and data exercises for each of the main chapters. Final chapter of practice material and datasets ideal for class homework or project work.

Python is an easy-to-learn and extensible programming language that offers a number of functional programming features. This practical guide demonstrates the Python implementation of a number of functional programming techniques and design patterns. Through this book, you'll understand what functional programming is all about, its impact on.

\"A book of fun coding projects for readers who know a little Python already and want to expand their skills. Simulate volcanoes, map Mars, and more, while gaining experience using free modules like Tkinter, matplotlib, Cprofile, Pylint, Pygame, Pillow, and Python-Docx\"-- Provided by publisher.

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/