Top Python Frameworks and Libraries that will elevate your work

Introduction about Python

Python is a general-purpose programming language widely used in everything from creating banal web pages to controlling rovers on other planets.

The scripting language is universal and is the most popular programming language in the world (according to the Tiobe edition, which compiles the top of the most popular languages).

It’s easy to start with Python, it can build large and complex systems, and it has a wealth of libraries to do just about anything.

Any software product, regardless of complexity and specifics, can be created in Python and one of the hundreds of frameworks developed.

Third-party libraries like Pyromid, Flask, Django, and dozens of others are used to create web applications and sites to help replace PHP and JavaScript.

Even full-fledged data management systems are written in Python, which later becomes analogs of mastodons like WordPress and Drupal. Also, Python is used to create auxiliary tools for working with the web, such as scrappers that collect information from other people’s web pages.

Powerful applications and frameworks have been built in Python, such as the GIMP image editor, the Ubuntu app store, 3D graphics editors, BitTorrent networks, etc. The list can be continued for a long time.

Also, Python often becomes an integral part of such applications. For example, Sublime Text is written in C++, while its extensions and plugins are written in Python.

Apps for iOS and Android are built in hundreds of different languages, and Python is one of them. More often, of course, this concerns their server component because more common frameworks or some highly specialized technologies are responsible for the interface. Nevertheless, Python is often responsible for the backend (that is, the program’s internal logic).

Python is used in game development precisely because of its ability to create reliable and well-thought-out scripts using the language. They are responsible for the movement of characters, the performance of any actions, the transition from one event in the game to another, and so on.

Like many other languages, Python has a package manager called pip. It allows you to quickly, or instead in one command, download any of all available libraries and frameworks. There are more than 300 thousand in python, and the daily download is several tens of millions.

However, among the many, some are used more than others, are very popular, perform important functions for the programmer, and supply very cool tools that hundreds of thousands of programmers worldwide use daily.

Python Frameworks

There are many Python frameworks available. Which one you choose will depend on your specific needs and preferences. Some popular ones include:

Django

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. It is an open-source framework that allows developers to create web applications of almost any level. It is among the best Python frameworks and deservedly popular.

Django has a huge set of additional libraries and support from a huge community of developers, which significantly simplifies and speeds up development.

Key features of Django:

1. the presence of its ORM (from the English. Object-Relational Mapping, “object-relational mapping”);
2. the built-in administrative interface (in simple terms – admin panel);
3. template engine;
4. form library;
5. caching and internalization system;
6. authorization and authentication system.

Flask

Flask is a lightweight Python web framework that provides useful tools and features for creating web applications.

The Flask framework has a small source codebase, which is why it is called a microframework. But this does not mean that it has fewer features than the same Django. By default, it only includes a request handler and templating engine, and the simplest Flask application might be just a few lines long. The developers of this framework consciously wanted to keep the core simple but extensible.

Nevertheless, with the help of Flask, you can implement almost any task: from a simple one-page site to a serious project with authorization, authentication, and other features. Flask is suitable for tasks that require flexibility in the choice of components. The developer himself decides what is useful to him in his work.

Is Flask at the heart of such large services? like Reddit, Netflix, Pinterest, Twilio, and taxi aggregator Lyft.

Key features of Flask:

1. built-in development server and debugger;
2. request dispatching in RESTful style;
3. built-in support for unit testing;
4. using the Jinja2 template engine;
5. 100% WSGI 1.0 compatible;
6. Lots of community-provided extensions.

Pyramid

Pyramid is a flexible Python web framework suitable for small and large applications. The growing popularity of the Pyramid framework is universal. It can be used for projects of any level of complexity. It has earned high marks in the Python developer community for its transparency and proven reliability. Tech giants like Mozilla, Yelp, Dropbox, and SurveyMonkey use it.

Megaframeworks tend to make decisions for the developer regarding the architecture of the application or the choice of specific components, such as ORM. But if your needs don’t match their point of view, you will end up fighting their choice.

Microframeworks, on the contrary, do not impose any solutions, which sometimes play into your hands. However, when your application grows and needs new components, you are left with a choice in the matter of choice.

In both cases, the focus is on getting started on the project: either too much or too little. Pyramid is the golden mean. This framework can be a micro-framework at the very beginning of the project, and as it grows, it will provide everything you need at every stage to bring the project to a successful completion.

Pyramid key features:

1. the ability to write an application in one file;
2. serving requests for static files;
3. toolbar for debugging the application directly in the browser;
4. extensible interchangeable templates (template);
5. built-in internationalization;
6. HTTP caching; built-in support for sessions;
7. transaction management, including when working with several databases at once;
8. flexible, extensible authentication and authorization;
9. repetitive configuration automation.

Tornado

Tornado is a Python web framework and asynchronous networking library. It was developed at FriendFeed and then open-sourced by Facebook. It is designed to handle large numbers of simultaneous connections, making it well-suited for real-time web applications.

Some key features of Tornado include:

1. a non-blocking web server that can scale to thousands of open connections;
2. support for WebSockets and long-polling, allowing real-time communication between the server and clients;
3. a simple template engine for rendering HTML and other content;
4. support for third-party authentication and authorization (e.g., OAuth).

Web2py

Web2py is a full-stack Python web framework that includes a web server, a database, and a web-based IDE.

The Web2py framework was originally conceived as a learning tool focused on usability and ease of use. For example, it lacks project configuration files. The framework’s architecture was developed by analogy with solutions such as Ruby on Rails and Django.

The whole process of development, debugging, testing, and administration of a remote database, can be organized without any third-party tools through its web interface, which is also a web2py application.

In addition, each application is provided with an auto-generated Django-like administration interface.

Key Features of Web2py:

1. integrated web development environment;
2. built-in components for all primary functions;
3. mechanisms for authentication and access control by roles;
4. jQuery support for Ajax and UI effects;
5. support for internationalization;
6. automatic error logging along with context.

CherryPy

CherryPy is a Python web framework designed for speed, simplicity, and flexibility. This framework, which is over a decade old, has proven itself to be exceptionally fast and stable. One of the original goals of the founder of the project, Remy Delon, was to make CherryPy as consistent as possible with the “pythonic style” principle. This allowed the developers to use the framework like a regular Python module and not think about the specifics of web programming. They can create web applications in much the same way as any other object-oriented Python program.

CherryPy can act as a standalone web server or run by another server application.

Key Features of CherryPy:

1. the reliable multi-threaded web server that complies with the HTTP/1.1 standard;
2. the ability to run different HTTP servers (for example, on multiple ports) at the same time;
3. built-in tools for working with sessions, caching, authentication, static content;
4. powerful configuration system;
5. built-in profiling and testing support;
6. support for Python 2.7+, 3.5+, PyPy, Jython.

Bottle

Bottle is a fast, simple, and lightweight Python web framework. It is distributed as a single file module with no dependencies other than the Python standard library. It was originally developed solely for building web APIs.

Out-of-the-box functionality includes a built-in web server, templating, request routing, and proprietary tools and utilities. Bottle allows you to create simple applications for personal use. It’s also a good example of learning frameworks, as well as for quick prototyping. For example, Netflix used Bottle to create its web interfaces.

Bottle key features:

1. fast built-in pythonic-style template engine;
2. built-in development web server;
3. a simple mechanism for routing requests to functions with support for both static and dynamic addresses;
4. easy access to cookies, file uploads, request headers, and metadata.

Python Libraries

Many libraries are available for Python, providing a wide range of functionality and supporting many different use cases. Here are a few examples of popular Python libraries:

NumPy

NumPy is a library for scientific computing with Python, providing support for large, multi-dimensional arrays and matrices and an extensive collection of mathematical functions to operate on these arrays.

This library is widely used for machine learning. That is why other libraries also use it (TensorFlow). The most useful option provided by this tool is the array interface.

Now let’s look at the most useful Numpy options, which include:

1. use of the library to simplify the most complex mathematical operations;
2. the arrival of an impressive amount of materials with open source;
3. simplicity and intuitiveness.

As for the direct use of the library, Numpy can help transmit sound waves, images, and binary streams. Full-stack developers must know it to perform machine learning operations; usually, it is added to simplify the task.

Pandas

Pandas: a library for data manipulation and analysis, providing tools for reading and writing data, handling missing values, and working with time series and other data sets.

It is a machine-learning library that provides high-level data structures and a wide range of analysis tools. A distinctive feature of Pandas is the ability to translate the most complex operations with information using just one or two commands. This library contains a lot of ways to combine data, group it and filter it.

Pandas features include:

1. the ability to simplify data manipulation;
2. support for sorting, visualization, and other options.

Pandas provides a lot of flexibility and functionality when used with other libraries. In addition, users can use it in various operations, data sorting applications, etc.

SciPy

SciPy is a library for scientific computing in Python, providing functions for optimization, linear algebra, signal and image processing, and more.

This library is popular among engineers and developers due to the fact that there are various modules.

Here are some exciting features of SciPy:

1. the development of this machine-learning library was carried out using Numpy;
2. it supports various numerical programs, integration, and optimization;
3. SciPy submodules are well documented.

Using SciPy to solve various differential equations and linear algebra is possible.

Matplotlib

Matplotlib is a Python library for creating static, animated, and interactive visualizations.

By using this library, it is possible to create different graphics at the same time (including two-dimensional ones). It is also used for operations with figures. Engineers and developers often use Matplotlib to work and edit quality drawings. Learning this library is quite simple and understandable, which adds to its popularity.

Matplotlib features:

1. the ability to use different tools (graphical interfaces and Python scripts, Jupyter notebooks, Ipython shells);
2. integration with Seaborn;
3. the ability to develop high-quality images for publications;
4. printable image formats.

If the user needs help, a huge community is dedicated to this library. There you can get comprehensive information and training in Matplotlib.

The use of this library is quite extensive. These are object-oriented APIs for embeddable charts in a variety of applications. 

Seaborn

Seaborn is a Python library for creating statistical visualizations based on Matplotlib.

Seaborn uses other libraries (NumPy, Pandas) and is used for data visualization. It contains options for plotting graphs based on sets of information.

Features and benefits of Seaborn include:

1. aggregation and display – functions built into the library to help create graphs;
2. high-level interface for building statistical graphs (for easy understanding and study of data);
3. scatter and pie charts, histograms, error messages;
4. color selection tools – for finding patterns in data.

Seaborn is in demand among regression models. It is effective for creating various themes stylized as Matplotlib graphics and for plotting time series statistics. It is also used to visualize one and two-dimensional data.

Scikit-Learn

Scikit-Learn is a library for machine learning in Python, providing tools for data preprocessing, model selection, and evaluation.

This library is considered one of the best for working with complex data. It integrates with NumPy and SciPy. Scikit-Learn is frequently modified and improved. The cross-validation option, which allows more than one metric to be applied, is one such optimization.

Features of Scikit-Learn:

1. the ability to extract elements from texts and pictures;
2. cross-validation – many different methods for checking the accuracy of a controlled model on invisible information;
3. many machine learning algorithms and the ability to perform expensive tasks (clustering, factor analysis, etc.).

Scikit-Learn is used to implement common machine learning and data mining tasks.

TensorFlow

TensorFlow is an open-source library for machine learning and deep learning developed by Google. It is easy to learn and has several sets of useful tools. However, this library is not limited to machine learning; it is used for differentiable programming.

Features of TensorFlow include:

1. a simple architecture that allows you to quickly learn programming and create machine learning models;
2. it is possible to move the created learning models to any device, cloud storage;
3. the presence of an active execution option makes it possible to create models for machine learning, debug and manipulate the latter.

TensorFlow has all the solutions for debugging, running, and other tasks, problems regarding machine learning.

Conclusion

Python is one of the most popular programming languages today and is widely used to create a range of applications.

There are many useful popular Python frameworks to streamline the development process and speed up time to market. There are full-stack, non-full-stack, and micro-frameworks for Python development.

Python libraries are a collection of useful features that eliminate the need to write code from scratch. There are over 137,000 Python libraries in existence today. Python libraries play a vital role in developing machine learning applications, data processing, data visualization, image processing, data processing, and more.