Meaning and types of Python framework and the 10 Python frameworks to try out
Python is a powerful, flexible, and easy-to-learn high-level programming language that has become increasingly popular in recent years. It is widely used for web development, artificial intelligence, data analysis, cyber security, etc. One of the reasons for its popularity is the wide range of frameworks available for Python, which makes it easy to build complex applications quickly and efficiently. Learn more about Python on their official website https://www.python.org/.
What is a Python Framework?
A Python framework is a collection of packages and modules that help developers with pre-fabricated functionality when building applications. Frameworks can help speed up development by providing commonly used functionality and a structure for building applications. Below are types of Python frameworks.
Types of Python Framework
Python frameworks can be grouped into three categories, full-stack, micro, and asynchronous.
Full-stack Framework:
Full-stack framework also referred to as enterprise framework, is the type of Python framework that contains all the web development requirements. These include front-end interfaces, back-end services, authentication functionality, authorization, database management, etc. These frameworks are configured to work great in any type of project.
Micro Framework:
Micro frameworks are minimal web application frameworks with limited functionalities and features. These frameworks offer only those components that are required for building an application. They lack much functionality that comes with a full-stack framework, like a database abstraction layer, authentication functionality, authorization, etc.
Asynchronous Framework
Asynchronous frameworks are microframeworks that allow a developer to run several processes concurrently with the help of the Python asyncio library. These can handle a large number of concurrent connections and are the latest type of framework in Python.
Now you understand what Python and Frameworks are all about, let’s continue with the top 10 Python frameworks.
Top 10 Python Frameworks
1. Django:
Django is a high-level web framework that is designed for fast and efficient development of web applications. It is built on the Model-View-Controller (MVC) architecture and provides a wide range of features, including an ORM (Object Relational Mapper), a templating engine, and built-in support for authentication and authorization.
Django is very powerful and secure, it to off many development burdens from developers. It can be used in any type of web application. Some big tech companies that use Django are: Instagram, Quora, Spotify, Pinterest, etc. Django is always the first choice of anyone who wants to start web development using Python.
2. Flask:
Flask is a Python lightweight and easy-to-use micro-web framework. It is suitable for small to medium-sized web applications and is known for its simplicity and flexibility. Flask is often used for building RESTful APIs, web services, and web applications. In some cases, it may be seen as the alternative to Django. It is a good choice for you if are interested in developing simple web apps with little effort.
3. Pyramid:
Pyramid (The Start Small, Finish Big Stay Finished Framework) is another Python web framework that is designed to be flexible and easy to use. It is similar to Flask in terms of its simplicity, but is more powerful and can be used for building both small and large web applications.
4. Tornado:
Tornado is a Python web framework designed for building high-performance web applications. This framework is known for its speed and scalability and is used for building real-time web applications, like chat apps and online games most of the time.
5. TensorFlow:
TensorFlow is an open-source library for machine learning (ML) and deep learning. Tensorflow is used for building and deploying machine learning models and can be used for other tasks, such as image classification, natural language processing, and time series analysis.
If you are interested in machine learning with Python, this framework is for you. It has tons of features and already-trained models to explore.
6. PyTorch:
PyTorch is a machine-learning library that is similar to TensorFlow but is known for its simplicity and ease of use. It is often used for building deep learning models, such as neural networks and convolutional neural networks. Many people prefer PyTorch over TensorFlow because they believe it’s more Pythonic as well as faster than TensorFlow.
7. NumPy:
NumPy is a Python library for scientific computing. NumPy provides a wide range of features, including support for mathematical functions, multi-dimensional arrays, and linear algebra. It is often used in scientific and engineering applications, such as machine learning, data analysis, signal processing, etc.
8. Pandas:
Pandas is a Python library for data analysis and manipulation. This framework provides a wide range of features, including support for data structures, such as Series and Data Frames, and can also be used for a wide range of tasks, such as data transformation, data cleaning, and data analysis.
9. SciPy:
SciPy is another Python scientific computing library, this framework is built on top of NumPy. It provides a wide range of features, including integration, optimization, and signal processing. SciPy is often used in scientific and engineering applications, such as data analysis, machine learning, and signal processing.
10. Matplotlib:
Matplotlib is a Python plotting library that is used for creating data visualizations and data analysis. This framework provides many features, including support for 2D and 3D plots, and can be used for a wide range of tasks, such as data visualization, data exploration, and data analysis.
Here is the end of our top 10 Python frameworks, also note that they are many other Python frameworks and the list keeps increasing every day. We will still bring you more articles about other frameworks and I hope you found a python framework for your project today.
Should I use a Framework?
In most cases, the answer to this question is yes, but it also depends on what application you are developing.
But overall a framework makes it easier to develop applications faster and more efficiently. It handles most of the tricky parts of the project and because most of the frameworks are open-source, it keeps expanding, and flaws and vulnerabilities can be detected so easily.
The framework allows developers to reuse code, and also makes it easier for a beginner to develop powerful applications.
A framework like Django handles all the database connections, leaving you with models to control how your database works.
Conclusion:
Python has a wide range of frameworks that are suitable for different types of applications and tasks. From web development to machine learning, these frameworks make it easy to build powerful and efficient applications with little effort.
We hope you enjoy this article, feel free to explore other interesting articles like this one.
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