Python is widely recognized as one of the world's most popular programming languages in software development companies to achieve business goals , powering startup backend stacks and integrations in rapidly emerging fields such as AI and data science.

Stack Overflow recently published its annual developer survey results, identifying Python as its fastest-growing programming language.

common libraries and frameworks for python developers in 2023

What Are Python Frameworks?

What Are Python Frameworks?

Python framework is a prewritten piece of code that gives developers tools and structures for quickly building applications with technical expertise, much like modules or packages in Java help developers make websites quickly in software companies.

Think of the Python framework as a collection of powerful tools for application creation and hire a python developers.

Python framework is an indispensable resource that developers can utilize to assist them with the following:

  • Structure standard for applications that facilitates development and maintenance.
  • The pre-built libraries, wide range of tools, and utilities simplify database management, form verification, and user authentication tasks.
  • Many frameworks are built with scalability and security in mind.

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Python Frameworks Types

Python Frameworks Types

Python frameworks can be divided into three categories, each describing its specifications. Below we outline these three Python frameworks in more detail.

Full Stack Frame

This framework meets all your web development needs with all-in-one solutions, offering everything from form generators and form validation tools, template layouts, and form generators to template engines and ORM solutions.

A full-stack web framework offers comprehensive features and tools needed for web app creation in one package, including examples of form generators, form validation services, and form generators themselves. Template layouts may also include all necessary template engines and ORM capabilities!

Full-Stack Python frameworks include Django (also referred to as Full Stack Python), Web2py (also referred to as Full Stack Python), CubicWeb, Giotto framework, FastAPI framework, TurboGears framework BlueBream framework, and Websauna.

Micro Framework

Lightweight web framework that offers the essentials needed to build minimalist applications, such as routing, request and reply handling, HTTP support, and routing support.

This framework gives developers only what is necessary to build fast web apps and mobile apps with minimal functionality project requirements.

Microframeworks do not include extra features, such as database abstraction or form verification, so senior developers must do more manual work to get to their desired functionality.

Manual code addition, requirements management, or third-party libraries might all be necessary to achieve the desired functionality. On the other hand, microframeworks offer greater customization and control over code, offering you greater freedom.

Flask is an increasingly popular Micro Python framework alongside Dash, Falcon MorePath Pycnic, and Hug.

Asynchronous Framework

Asynchronous Python frameworks enable developers to write code that can perform multiple tasks simultaneously without interfering with tasks or blocking other processes or requests.

They allow concurrent connections, processes, and requests. By writing concurrent code, this enables faster and more efficient processing of data.

Asyncio Python library writes asynchronous code using coroutines, event loops, and other methods. Sanic, AIOHTTP Growler, and Tornado are well-known Asynchronous Python Frameworks.

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Python Frameworks For Web Development By 2025

Python Frameworks For Web Development By 2025

Django

Django, a free and open-source Python framework, allows programmers to develop complex code and applications rapidly.

Python web developers can use Django to build high-quality apps quickly.

Django, one of the most widely used Python frameworks, is widely used for developing APIs and web apps, with over 12,000 long term projects having used its extensive library, reduced coding requirements, and reusability features.

Advantages

  • It helps you define URL patterns for your application.
  • Integrated authentication system
  • Simple and effective URL scheme
  • The best database language is available for object-oriented programs.
  • The intuitive admin interface allows for adding, deleting, and modifying customization.
  • A framework for caching supports several cache mechanisms.

Limitations

  • It could be better for small projects as it's a high-level framework.
  • The volume of requests can cause websites to load slowly
  • Lacks coding conventions like Rails
  • Use of the product requires in-depth knowledge

CherryPy

CherryPy is an open-source, lightweight, and fast web development framework written in Python that is open source and can run on any platform that supports Python.

CherryPy is a web framework that enables you to access any data access or templating technology, performing all the functions associated with web frames, such as sessions, file uploads, and static content display.

CherryPy allows developers to create web apps using Python object-oriented code rapidly. This significantly speeds up the production of source code.

Advantages

  • A thread-pooling WSGI server that is HTTP/1.1 compliant and reliable
  • Operation of multiple HTTP servers simultaneously (e.g., on different ports).
  • Python 2.7+ and Python 3.5+.

    PyPy, Jython support, Android, PyPy.

  • There are many integrated utilities to encode, cache, authenticate, and create static content.
  • Developers and administrators can configure their systems easily with this robust system
  • Support for integrated profiling, coverage, and testing

Limitations

It is difficult for beginners to use the framework due to its lack of documentation.

Pyramid

Mozilla, Yelp, and SurveyMonkey, among other IT titans, rely on the Pyramid Python platform for web development.

Due to its adaptability and user-friendliness, it has proven highly popular among IT teams worldwide; moreover, the Python 3 version of Pyramid is widely utilized today.

Pyramid provides developers with an effective platform for constructing complex online applications using Python with a level of expertise.

Python has gained widespread respect among even seasoned developers for its openness, measurable quality, and capacity for producing complex projects. previous projects. technical interview

Advantages

  • Ability to perform well in both small and large applications
  • WebHelpers HTML validation and generation and URL mapping according to Routes setup.
  • Template design with asset information
  • Tests, support, and extensive documentation
  • Flexible Authorization and Authentication

Limitations

  • The Pyramid framework is a complex and time-consuming process of tool preparation.
  • Only highly-skilled technicians can install the parts of the Pyramid correctly.
  • SQLAlchemy does not require or benefit small projects.

Grok

Grok is an open-source web framework built with Zope Toolkit (ZPK). It offers developers an agile development experience with two fundamental principles at its heart - Convention over Configuration (COC) and Doesn't Repeat Yourself (DRY).

Grok, an open-source framework for application development, seeks to accelerate this process.

Grok allows developers to choose from a selection of standalone and network libraries suited for each task so hire remote python developer for working similar to what would be found in full-stack Python Frameworks like Pylons or TurboGears.

Its interface also mirrors these full-stack Python frameworks.

Advantages

  • It provides a solid foundation for developing sophisticated and adaptable Web apps
  • This allows web developers to use the capabilities of Zope 3.
  • A robust object storage database
  • Integrate data security into your application to protect it and give authorized users access
  • Grok component architecture helps developers reduce development uncertainty.
  • This toolkit contains the essential building blocks to build custom web applications for app developer and corporate use.

Limitations

  • While hiding code in parent classes may make your code look more elegant, it can also affect readability and comprehension because the code might not be readily available.
  • If a view has unique functionality, CBVs are likely to be overkill.

    Use them whenever you plan to reuse a code section across multiple views.

Also Read: Python to Assembly: The Evolution of Programming Languages

TurboGears

TurboGears, a Python framework tailored explicitly for building full-stack data-driven web apps, was developed to address the shortcomings of other popular frameworks for developing mobile and online apps.

Software developers can start designing web apps with minimal configuration required.

TurboGears is a collection of JavaScript tools that enables web developers and Python web development companies to create Python websites faster and rapidly.

Web apps are much quicker to develop with SQLAlchemy, Repoze, and WebOb. And Genshi than existing frameworks; similarly to Pylons, it enables multiple databases and web servers.

TurboGears' framework uses a Model-View-Controller (MVC) design. Its powerful FORM and Ajax capabilities enable companies such as Bisque, ShowMeDo, and SourceForge to utilize it effectively.

Advantages

  • All of the characteristics can be implemented as function decorators.
  • Multi-database support.
  • Command-line utilities are available.
  • MochiKit Library Integration for JavaScript
  • PasteScript templates and MVC architecture.
  • ToscaWidgets facilitates front-end technologies and server collaboration.

Limitations

It can be challenging to expand the framework if necessary.

Web2py

Web2Py provides a debugger and code editor to facilitate testing and maintaining your web app, making this framework suitable for multiple platforms, including Windows, Unix/Linux, Mac OS X, Google App Engine, etc.

This framework simplifies Python app development by providing clients access to a web server, SQL-based database, and user interface online.

Users could develop, modify and deploy online apps through web browsers.

Web2Py is designed around a ticketing system, creating tickets whenever an error occurs and encouraging clients to track its status.

In addition, components exist to manage HTTP requests, responses, and sessions.

Advantages

  • Configuration over settlement allows for rapid web development.
  • MVC Architecture Support to Simplify Web Development
  • Developers can interact with relational and NoSQL database systems.
  • The web-based IDE can help you to speed up your web development tasks, such as deleting temp files, modifying applications files, running tests, and browsing previous tickets.
  • This includes the Helpful Batteries to build web applications without other tools or services.
  • By addressing security issues and significant vulnerabilities, you can maintain the security of your online applications.

Limitations

  • Inflexible plug-in system;
  • System management difficulties for loading data models
  • Python 3 is not supported.

Flask

Flask is a Python framework modeled after Sinatra Ruby's framework and available under a BSD license. Utilizing Werkzeug's Web Service Generator Interface Toolkit and Jinja2 Template Template, its primary objective is to provide a solid platform for building online applications.

Python allows developers to tailor its backend according to their specifications, while Flask was explicitly designed for creating flexible applications.

Many corporations like LinkedIn and Pinterest rely on these flexible applications. Flask is ideal for small projects compared to Django, including web server development, Google App Engine integration, and unit testing.

Advantages

  • Integrated development server with the debugger.
  • RESTful Request dispatching
  • Support for integrated unit testing (code and quality).
  • Uses Jinja2 Templating (tags and filters, macros, and more).
  • Compatible with WSGI 1.0.
  • Many extensions developed by the community allow for new functionality to be added.

Limitations

Flask is a framework that should only be utilized by experienced developers with exceptional technical skills and soft skills.

To be used effectively requires excellent attention to detail and in-depth knowledge of the relationships between components.

Bottle

The bottle web frame from Python fell under the small-scale frameworks category and was created to construct APIs.

Bottle tries executing all code on a single page without relying on external dependencies beyond the Python Standard Library.

The Bottle provides many features, such as templating, utilities, and direction. It also provides fundamental abstractions atop the WSGI standard; Flask lets you get much closer to source code than full-stack development environments.

The Bottle allows developers to interact better with hardware. This platform allows developers to learn Python frameworks, prototype programs, and create simple programs for themselves - as Netflix does using Bottle to develop its online interfaces.

Advantages

  • URL mapping can be made dynamic and flawless by using a simple syntax.
  • Swift and Python-based template engine with support.
  • The WSGI Framework is compatible with CGI, and its internals can be accessed.
  • This allows easy access to data and HTTP metadata, including cookies, file uploads, and other metadata.
  • Includes HTTP server that supports glue, fapws3, Flup, and other WSGI-capable HTTP servers.
  • Test and High-Performance Acceleration Enhancements

Limitations

  • The framework's small size means that it is best suited to small applications.

    It can only use the standard Python libraries.

  • Documentation and examples of usage are extremely limited within the framework community.

Tornado

Tornado is an unconventional Python library framework that uses a nonblocking I/O Framework and addresses the C10k problem (meaning, when appropriately configured, it can accommodate more than 10,000 simultaneous connections).

Apigee Fusion is an excellent platform for building applications with many concurrent users.

Advantages

  • Implementation of third-party authentication mechanisms and authorization mechanisms is possible.
  • HTTP clients with no blocking and better quality services.
  • It is a high-quality product.
  • Translation and localization assistance.
  • Support for User Authentication and Web Templates

Limitations

  • CBVs are overkill if a view has unique functionality.

    Use them whenever you expect to reuse a code section across multiple views.

BlueBream

BlueBream, an open-source framework, and server for creating online applications, is an open-source library, server, and framework created by the Zope team - previously referred to as Zope 3.

Specifically designed to accommodate large-scale operations that utilize reusable components, BlueBream makes building online apps simple and effective.

BlueBream is an experienced user of Zoop Toolkit who can meet the stringent requirements for long-lasting, consistent, and adaptable software development.

Advantages

  • Focus on Web Server Gateway Interface compatibility with Python.
  • Frameworks for functional and unit testing.
  • Security is a crucial concern for all businesses.
  • Templates can be developed using a language that is compatible with XHTML.
  • This tool allows you to generate forms automatically.
  • The Zope Component Architecture implements the separation of concerns to create robust and reusable components.

Limitations

  • BlueBream is a web framework with a steep learning curve for new developers unfamiliar with Python or Zope, making its use easier for those with prior experience in either of those languages.

    New developers may find BlueBream challenging.

  • BlueBeam can be difficult to use for small or simple tasks due to its component-based architecture and the wealth of capabilities available; some developers may find them overwhelming.
  • BlueBream may not be as well-known as web frameworks like Django or Flask, so fewer developers may be willing to support it.
  • BlueBream can be slower than other web frameworks, especially for larger projects with complex structures; the Zope framework could have contributed to this slowness.

The Seven Best Python Libraries You Should Use In 2025

The Seven Best Python Libraries You Should Use In 2025

Python's popularity among python developer hire can be attributed to several factors: its extensive library of tools available to users.

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Python's ease of use has inspired many developers to create new machine-learning libraries. Python's vast library is also one of the primary reasons machine learning experts have become increasingly interested in it.

TensorFlow

TensorFlow is an open-source library you may have encountered while working on machine learning projects with Python.

Google and the Brain dedicated Team jointly developed TensorFlow, which can be found in virtually all Google applications that employ machine learning technology.

TensorFlow is a library that allows you to write algorithms with complex tensor operations, such as neural network implementation.

Since neural networks can easily be represented as graphs, TensorFlow makes this possible with operations on Tensors representing data in N-dimensional matrices.

TensorFlow Features

TensorFlow uses techniques such as XLA to perform quick linear algebra operations.

  • Responsive Construction: TensorFlow allows us to easily visualize every part of a graph, which we cannot do with Numpy and SciKit.
  • Flexible: Tensorflow is a very flexible tool.

    It is modular, and you can use it however you like.

  • Easy to Train: The software can be easily trained on CPU and GPU for Distributed Computing.
  • Parallel Neural Network Training: TensorFlow allows you to train multiple neural networks and graphics cards, which is very efficient for large-scale systems.
  • Large Community: It is obvious that if Google developed the software, there are already a lot of software engineers working on improving stability.
  • Open Source: Anyone with internet access can use this machine-learning library.

Scikit-Learn

This Python library is linked with NumPy and SciPy and is considered one of the top libraries for dealing with complex data.

This library is currently experiencing significant modifications. One fundamental change involves its cross-validation function, which now supports multiple metrics; also, many training methods, including logistic regression and nearest neighbor, have been upgraded and modified accordingly.

Scikit-Learn Features

  1. Cross-validation is a method of checking the accuracy and reliability of supervised models using unseen data.
  2. Unsupervised learning algorithms are available in various options, from factor and principal component analysis to unsupervised neural networks.
  3. Bag of words): This tool helps extract specific features from text and images (e.g., Bag of Words)

Numpy

Numpy, a Python machine-learning library, is one of the most-utilized libraries. TensorFlow and other libraries rely heavily on Numpy for performing multiple operations on Tensors, including TensorFlow itself.

Numpy's array interface is its standout feature.

Numpy: Features and Benefits

  1. Interactive: Numpy has a very interactive interface and is easy to use.
  2. Mathematical Implementations: makes complex mathematical implementations simple.
  3. Intuitive: Makes understanding concepts and coding accessible.
  4. Lots of Interaction: Widely Used, hence many contributions to open source.

Keras

Keras has quickly earned the accolade of being one of the most excellent Python machine-learning libraries. Offering an easy way to express neural networks, it also stands as an invaluable resource for building models, processing large data sets, visualizing graphs, and much more.

Keras can be used with either TensorFlow or Theano as its backend infrastructure and some of the most popular networks, such as CNTK.

However, Keras tends to be slower compared to other machine learning libraries; it creates a computation graph from your backend infrastructure and uses that graph for operations - although all Keras models remain portable.

Features Of Keras

  • The software runs well on both CPUs and GPUs.
  • Keras can support almost any model of a neural net - convolutional, fully connected, pooling recurrent, embedded, etc.

    These models can also be combined to create more complex models.

  • Keras is modular and flexible.

    It's perfect for research that requires innovation.

  • Keras, a Python-based framework, makes it simple to explore and debug.

PyTorch

PyTorch, one of the leading machine learning libraries, allows developers to construct dynamic computation graphs and automatically calculate gradients quickly.

Furthermore, PyTorch features rich APIs designed to solve application-level neural network issues.

This machine learning library was developed on Torch, an open-source machine library written in C and wrapped with Lua.

Since the 2025 Python Machine Library debuted, it has quickly gained popularity among machine learning developers.

Features of PyTorch

  • Hybrid Front End: The new hybrid front end provides flexibility and ease of use in eager mode while seamlessly transitioning into graph mode for enhanced performance and functionality within C++ runtime environments for frontend engineers.
  • Distributed training: Utilize native Python and C++ support for asynchronous collective operations, peer-to-peer effective communications, and execution.
  • Python First: PyTorch does not integrate its Python-based framework into an unwieldy C++ one; instead, it is explicitly tailored for deep integration into Python to support use alongside popular libraries and packages like Cython or Numba.
  • Libraries and Tools: A community of researchers, developers, and educators has built an ecosystem of libraries and tools to extend PyTorch.

    This supports development in many areas, including computer vision and reinforcement learning.

LightGBM

Gradient Boosting, one of the top machine learning libraries, enables developers to construct algorithms using decision trees easily and redefine elementary models.

There are libraries specifically tailored for quick implementation of this technique. LightGBM, XGBoost, and CatBoost libraries each attempt to address similar problems differently.

Features LightGBM

  • High-speed calculations ensure high production efficiency.
  • It is intuitive, making it easy to use.
  • Training is faster than with many deep learning libraries.
  • There will be no errors when you use NaN and other canonical values.

Eli5

The Eli5 Python machine learning library can help overcome this difficulty. With its combination of visualization, debugging, and tracking tools, you can visually observe machine learning models as they evolve and observe the algorithm's working steps more closely.

Features and Benefits of Eli5

Eli5 also supports XGBoost libraries, lightning libraries, sci-kit-learn libraries, and sklearn libraries.

SciPy

SciPy is an application development and engineering machine learning library. To effectively use this resource, however, you must distinguish between its libraries and SciPy Stack modules for linear algebra, statistics, optimization, and integration.

Features of SciPy

  • SciPy is a library developed using NumPy.

    Its array is the prime example of NumPy at work.

  • SciPy provides an extensive set of numerical routines through its submodules, such as numerical integration, optimization, and more.
  • SciPy provides extensive documentation of its functions.

Theano

Theano, a Python machine learning library for processing multidimensional arrays, relies on a Python computational framework.

Similar to TensorFlow in its functionality but less efficient overall, Theano should not be considered suitable for production environments. Theano can also be deployed in a distributed or parallel environment, similar to TensorFlow.

Features of Theano

  • Tight Integration with NumPy: Ability to use NumPy arrays completely in Theano-compiled function.
  • Transparent Use of a GPU: Perform data-intensive calculations much faster than a CPU.
  • Efficient symbol differentiation: Theano calculates your derivatives of functions with one or multiple inputs.
  • Stability and speed optimizations: Get the correct answer to log(1+x), even if x is tiny.

    This is only one example to demonstrate the stability of Theano.

  • Dynamic code generation: Evaluate expressions quicker than ever before.

    This increases efficiency a great deal.

  • Self-verification and extensive unit testing: detect and diagnose multiple errors in the model.

Pandas

Pandas is a sophisticated Python machine-learning library offering analysis tools and high-level data structures.

One great advantage of using this library is being able to perform complex data operations using just one or two commands; furthermore, it includes built-in methods for filtering and grouping data and development time series functionality.

Features Of Pandas

Pandas make manipulating data easy. Pandas provides support for operations like Re-indexing and Iteration, as well as Aggregations, Concatenations, Sorting, and Visualizations.

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Conclusion

Python web frameworks help developers accelerate and simplify the development process by offering a collection of modules and packages that enable programmers to develop web apps and services quickly and efficiently.

Web frameworks relieve software developers of tedious tasks such as maintaining low-level aspects like protocols management, instead allowing them to focus on building the logic for their apps by reducing cost to hire a python developer.

Construction of websites relies heavily on user-friendly frameworks, but testing its functionality on real devices and browsers to ensure its highest quality is also crucial.

Developers should avoid relying on emulators as a testbed; simulation testing doesn't reflect the actual conditions of users accurately enough.To assess the quality of user experience, it's vital to conduct tests across various browsers, operating systems, and devices.

Paul
Full Stack Developer

Paul is a highly skilled Full Stack Developer with a solid educational background that includes a Bachelor's degree in Computer Science and a Master's degree in Software Engineering, as well as a decade of hands-on experience. Certifications such as AWS Certified Solutions Architect, and Agile Scrum Master bolster his knowledge. Paul's excellent contributions to the software development industry have garnered him a slew of prizes and accolades, cementing his status as a top-tier professional. Aside from coding, he finds relief in her interests, which include hiking through beautiful landscapes, finding creative outlets through painting, and giving back to the community by participating in local tech education programmer.

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