python-articles
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mythmgn / awesome_py_traps
100 Awesome python traps examples, 1 new example per week
Language:Python 69python-supply / guide-to-publishing-packages
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 repository.
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Python's built-in libraries include powerful tools for retrieving and operating over abstract syntax trees. This article provides an overview of how to use these features to analyze and transform Python code programmatically.
Language:Jupyter Notebook 2Native syntactic support for type annotations was introduced in Python 3. This article provides an overview of this feature, reviews how it can be used to document information about expressions and functions in a structured way, and illustrates some of the advantages of leveraging it for applicable use cases.
Language:Jupyter Notebook 1Multiprocessing 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.
Language:Jupyter Notebook 1Python comprehensions are a powerful language feature that can greatly improve the productivity of a programmer and the readability of code. This article explores how comprehensions can be used to build concise solutions for problems that require generating various kinds of combinations of all the elements from a finite (or infinite) set.
Language:Jupyter Notebook 0This article covers some background on higher-order functions in Python, presents an overview of how Python decorators are defined and used, and illustrates their utility via a few use cases.
Language:Jupyter Notebook 0Iterators and generators are powerful abstractions within Python that have a variety of uses. This article reviews how they are defined, how they are related, and how they can help programmers work in an elegant and flexible way with data structures and data streams of an unknown or infinite size.
Language:Jupyter Notebook 0Landing/redirect page for python.supply, where you can use Python as a platform to learn foundational concepts and practical techniques in computer science, programming, and software engineering.
Language:HTML 0Python metaclasses are how classes are created, and by defining your own metaclasses you can guide and constrain code contributors in a complex codebase. This article reviews how metaclasses can be employed to implement static checking of user-defined derived classes.
Language:Jupyter Notebook 0While built-in string methods and regular expressions have limitations, they can be leveraged in creative ways to implement scalable workflows that process and analyze text data. This article explores these tools and introduces a few useful peripheral techniques within the context of a use case involving a large text data corpus.
Language:Jupyter Notebook 0Python offers a rich set of APIs that make it possible to build wrappers for foreign functions written in another language (such as C/C++) and compiled into shared libraries. This article introduces some basic techniques that will allow you to start using shared libraries in your projects.
Language:Jupyter Notebook 0nthparty / article-privacy-preserving-information-exchange
This article uses a simple use case involving a transaction between a vendor and a customer to illustrate the privacy-enhancing potential of oblivious transfer (OT) and to demonstrate how OT can be incorporated into a Python implementation of a web service by leveraging the otc library.
Language:Jupyter NotebookBoth built-in and user-defined data structures in Python can be either mutable or immutable. This article explains why Python makes this distinction for built-in data structures and reviews some use cases within which you may want to define an immutable data structure of your own.
Language:Jupyter NotebookPython's extensive support for operator overloading can help you greatly reduce the conceptual complexity of your library or framework, allowing programmers who must use it to leverage the extensive knowledge and skills they already possess.
Language:Jupyter Notebookreity / article-specifications-for-distinguishing-functions
This article presents a technique for assembling concise, lightweight specifications and unit tests for verifying the identity of a function; the technique sacrifices completeness to enable compact and portable specifications.
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