Top 7 Python Web Development Libraries to choose from in 2021

Here are few more reasons to count on, when python comes into the picture.

  1. Python facilitates huge library collection.
  2. Regarded as the language any naive would be comfortable working with. Due to its ease and simplicity.
  3. Enjoy the ease to be productive with python libraries. Right from development up to deployment and management.
  4. Hits the goals of portability with ease like none other languages.
  5. Its high syntax is much enjoyable with a much low learning curve.
  6. Compare it with C, Java, and C++. You will know python libraries hit some higher level.

What are the Top 7 Python web development libraries to choose from in 2021?

1. Tensor flow

Why?

  • Responsive: Gives you an amazing ability to think and construct anything as you would like to.
  • Flexible: Want it to be brought in sync with some there features? or want it to work efficiently while being alone in the race? It offers you both options.
  • Easily trainable: Use CPU or GPU to train people well for distributed computing.
  • Neural parallel network training: Train multiple neural networks and GPUs. Thus bringing efficient large scale model systems.
  • Wide Community: Want some help or need something to be sorted out? The community is as large as the scope of the library!
  • Open Source: Got internet connectivity? Use the library then. For as long as you want it.

2. Requests

Why?

  • Larger compilation: This one is a much larger collection of libraries. Thus bringing more options, varieties and help.
  • Ease the process: Aims to simplify the task of creating and sending the HTTP requests to Python applications.
  • Intuitive: Its JSON method helps with adding query strings to URLs.This also helps with form-encoding your PUT and POST data.
  • Many things under one label: chunked HTTP requests, Connection timeouts, support for SOCKS proxy, connection pooling, keep-alive, and basic and digest authentication

3. Scikit-learn

Why?

  • Feature extraction: Want to extract data from images or texts? This one is a good catch for this need.
  • Cross-validation: Check the accuracy of supervised models on unseen data. This one provides many ways to do the same.
  • Unsupervised learning algorithms: Too many algorithms to play with. For example, starting from principal component analysis, clustering, factor analysis to unsupervised neural networks.

4. Bottle

Why?

  • Supportive: Routing support for clean and dynamic URLs
  • Amazing template solutions: Out-of-the-box support for templates like mako, jinja2, and cheetah. All thanks to its baked-in template engine.
  • Backed up with utilities: Form data, file uploads, cookies and headers are well taken care of.
  • Independent: Rely on only Python’s standard library. It offers every core feature a web developer would ever need.

5. Keras

Why?

  • Smooth: gives a smooth run on GPU and CPU.
  • Supportive for all: All neural network models are supported. Like fully embedding, connected, pooling, convolutional, recurrent etc.
  • Modular: expressive, flexible, and apt for innovative research.
  • Completely Python Based: Thus easy to debug and explore.

6. PyTorch

Why?

  • Flexible: seamless transition to graph mode for speed, optimization, and functionality in C++ runtime environments.
  • Popular with other libraries: Deeply integrated into Python. Can be used with popular libraries and packages. Like Cython and Numba.
  • Wide community: Tools and libraries extend PyTorch and supporting development. This extends areas from computer vision to reinforcement learning.

7. SciPy

Why?

  • Efficient numerical routines: helpful with optimization, numerical integration, and many others using its specific submodules.
  • Well Documented: All functions are well documented.

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