What are the most interesting Python modules?

In this article, we’ll learn about some of the most interesting Python modules. Here are some interesting libraries in Python.

HTTPX

HTTPX was developed by Tom Christine, a software developer specializing in API design and development.

High-performance applications are increasingly using asynchronous patterns, but the Requests library struggles to do its job efficiently.

HTTPX was created to overcome this problem. Building on the renowned usability of Requests, HTTPX is an asynchronous HTTP client that supports both HTTP/2 and HTTP/1.1. It has strict timeout controls and provides an API that is as similar as possible to that of the Requests library. HTTPX, fully type-annotated, can also be called directly from Python web applications via the ASGI protocol. All common Requests features, including support for international domain names and URLs, keepalives and connection pooling, sessions with cookie persistence, and more, are also included.

Alternatively, for high-performance asynchronous networking frameworks, you can use HTTPX with asyncio or trio. Because of this, HTTPX can handle a large number of queries.

Arrow

As many Python developers know, the rudimentary date, time, and time zone methods in the Python standard library and other low-level modules provide a way to interact with date and time objects. However, they suffer from performance and usability limitations; for example, they contain too many modules and classes, making them difficult to recall and distinguish. Conversions between time zones and timestamps are also time-consuming.

Fortunately, users can create, manipulate, format, and convert dates with the help of the logical and approachable Python module Arrow. Its primary purpose is to reduce the amount of code you import to handle dates and times.

Arrow works with ISO 8301 and Python 2.7, 3.5, 3.6, 3.7, and 3.8. Time zone conversion is simple because the timestamp itself is an attribute.

Arrow’s power can be gleaned from several aspects. Arrow’s DateTime replacement automatically takes the user’s time zone into account. Users gain quick and simple design alternatives for a range of common data entry situations. For relative offsets, such as week offsets, the shift method works well. Strings can be automatically formatted and parsed, which is a huge time saver. The ability to set the period, range, lower bound, and upper bound for any time interval from microseconds to years is another strength of Arrow.

FastAPI

As the name suggests, FastAPI is an extremely fast and powerful Python framework for developing application programming interfaces. It is a complete network application framework. Using FastAPI, you can potentially save 200% to 300% of your development time. With over 200 contributors and 28.6K starts, FastAPI is growing in popularity in the Python community.

FastAPI is an asynchronous web application framework based on the ASGI specification. Some of its features are listed below.

• OpenAPI, JSON formatting, and automatic client code generation – all based on open standards.

• A Swagger user interface that allows you to call and test your API from a browser.

• Interactive API documentation

• Reduce the number of errors caused by developer code errors by 40%.

• Minimize code duplication.

• Supports intuitive editors (VSCode/PyCharm) with autocompletion.

• It has integrated security and authentication.

• A dependency injection system that automatically manages all dependencies.

• Provides unlimited support for plugins.

• Fully compatible with Starlette and Pydantic.

• PyTest is used to test applications.

• Supports SQL databases, NoSQL databases, and GraphQL.

Python Fire

Python Fire has the ability to automatically generate a CLI for any given project. This library improves the ability to create a CLI. The required CLI can be obtained by first creating the necessary functionality from the CLI as a function, module, or class, and then using Fire.

When creating a Python library, testing should begin as soon as possible. Without Python Fire, you must define a main test method to obtain the required functionality. While this approach gets the job done, switching to a new main method each time you need to test new functionality is cumbersome and time-consuming. If you use Fire to test your CLI, you don’t need to constantly update your main test method.

If a function was created by someone else, it can be difficult to grasp just by looking at its code. Instead, call Fire on the module. By using this function, testing every method and function in the module becomes simple.

Starlette

Starlette is a lightweight ASGI framework or toolkit you can trust when it comes to developing powerful asyncio services.

This library is suitable for production use and includes many useful features, such as WebSocket and GraphQL integration. Starlette supports in-process background tasks, cross-origin resource sharing, GZip, static files, and streaming responses. All of these features are extensively tested and come with a fully type-annotated codebase and no hard dependencies.

Starlette can be used as a framework or as a standalone ASGI toolkit. Furthermore, components can be reused across all ASGI frameworks, fostering a community of compatible middleware and installable applications.

Mypy

Mypy is a static type checker written in Python. Its goal is to convert dynamic (or Mypy combines the best features of dynamic (“duck”) typing and static checking. It incorporates a powerful type system and compile-time type checking into Python’s already impressive expressiveness and user-friendliness.

When using Mypy, programmers have the freedom to create their own workflow. With Mypy, we aim to bring together the best features of dynamic and static typing for software development. Thus, users can rely on dynamic typing in situations where static typing fails, such as with legacy code. Mypy’s error reporting is designed after the structure of the compiler, making it ideal for use as a linter. With Mypy, developers have a reliable, consistent tool for testing their projects and their dependencies.

Immutables

Python’s Immutables module adds an immutable mapping type to the language. Hash Array Mapped Trie (HAMT), popular in functional languages like Clojure, Scala, and Haskell. On HAMT-based immutable mappings, operations like set() and get() are nearly O(1) efficient (O(log N)) for reasonably small mappings.

Expiring Dict

Python’s Expiring Dict is a powerful caching module. It provides a dictionary with sorted and automatically expired values for caching. Each dictionary lookup includes a check on the element’s TTL (maximum age) and maximum length.

Conclusion

In this article, we learned about eight different interesting Python modules that perform tasks in a simple, straightforward, and efficient way.