Built-in string methods and regular expressions can be used creatively to implement scalable workflows that process and analyze text data. Explore these tools and peripheral techniques within the context of a use case involving a Wikipedia data set.
Decorators deliver new forms of modularity and composability, helping you add features and functionalities in a quick, concise, and elegant way. Review their foundation (higher-order functions) and learn to use decorators in an illustrative use case.
Multiprocessing can be an effective way to speed up a time-consuming workflow via parallelization. This article illustrates how multiprocessing can be utilized in a concise way when implementing MapReduce-like workflows.
Iterators and generators are powerful and elegant abstractions that can help you work with data structures of an unknown or infinite size. Review how they are defined and conceptually related, and practice using them to manipulate data streams.
Built-in libraries that allow you to operate directly on abstract syntax trees can help scale certain software development workflows dramatically. Learn how to use these features to analyze and transform Python code programmatically.
Extensive support for operator overloading can help you reduce the conceptual complexity of your library or framework, making it possible for programmers who use it to leverage the extensive knowledge and skills they already possess.
Native syntactic support for type annotations makes it possible to document information about code in a structured way that is amenable to automated parsing. Explore the advantages of leveraging this feature within use cases such as unit testing.
Metaclasses are how classes are created, and by defining your own metaclasses you can impose guidelines and constraints on new contributions to a codebase. See how metaclasses can help implement static checking of user-defined derived classes.
A number of libraries and packages make it possible to invoke foreign functions written in another language (such as C/C++) and compiled into shared libraries. This article introduces some basic techniques for using shared libraries in your projects.
Comprehension syntax lets you define workflows on collections in a concise way that closely resembles widely recognized forms of mathematical notation. Build simple solutions in scenarios that require finding all combinations of items from a collection.
Both built-in and user-defined data structures can be either mutable or immutable. This article explains why this distinction exists for built-in data structures and examines a use case in which you must define an immutable data structure of your own.
This article is a step-by-step guide to assembling and publishing a small, open-source Python package; topics covered include directory structure, basic unit tests, basic continuous integration setup, and publication to a package repository.