Best Python Frameworks

10 Best Python Frameworks to Learn for Data Science

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Python is also among the most popular programming languages nowadays besides JavaScript and Java. And the reason is quite simple. It is not only easy to read, but it is also easy to understand. In Python, you only require minimal lines of code to build complex web applications. Moreover, It is the preferred language choice for data science, machine learning, and artificial intelligence.

Python has almost the same syntax as the mathematical syntax. You can also use it in statistics, mathematics, and economics as a Python developer. 

Do you want to learn how to become a python developer? If you do, you need to ensure that you are familiar with the various python frameworks available for data science.

This article will explain 10 of the best Python frameworks to learn data science. Please go through it for more information on becoming a python developer. 

Python Frameworks for Data Science 

The future of data science, machine learning, and artificial intelligence depends highly on python frameworks. Most scholars and researchers have used academic MATLAB for years.

However, Python has proved to be the best nowadays. Python offers various benefits in data science. It has a vast community as compared to its rival R.

Automating and building websites can also be done with Python. Let’s now look at some of the best python frameworks for learning data science. 

NumPy

NumPy is an essential framework for Python which works very well in scientific computing. What makes it an excellent python framework is its support for large and multi-dimensional arrays.

High-level math functions can easily operate on these arrays. This framework can also handle linear algebra and integrate Fortran code and C/C++ code. 

Pattern

It is a web mining module for Python. This particular framework has tools for visualization, machine learning, scraping, network analysis, and natural language processing (NLP).

It is a free module with 350+ unit tests and 50+ examples. 

Also Read: Kotlin vs Java

Scikit-Learn

Scikit-Learn offers Python a high-speed machine learning library. It has tools that allow efficient and straightforward data mining and analysis.

Scikit-Learn’s main features include preprocessing, regression, model, clustering, model selection, and classification.

Keras

Keras is a neural network library that runs on top of TensorFlow, PlaidML, Microsoft Cognitive Toolkit, or PlaidML. This work makes it easy to work with Machine Learning. This is because it is easier to understand than other ML libraries.

The four main principles of Keras are extensibility, user-friendliness, modularity, and computability.

Shogun

Shogun is an ML framework written in C++. It Offers interfaces of various programming languages, such as Python. Shotgun has several unified methods of machine learning.

They are all based on easy to understand algorithms. Support Vector Machines (SVM) is emphasized more in this Python framework.

Cython

This framework was developed to provide C-like performance for Python code. As a Python developer, you will benefit from its Pyrex-based translators. Pyrex-based translators allow you to write C extensions for Python.

While Cython offers integration support with Jupyter and IPython. It is also considered one of the best options for mathematical code. 

SciPy

Open-source Python framework SciPy is widely used in science, math, and engineering. It comes with modules for linear algebra, optimization, interpolation, ODE solvers, integration, and more.

As a python developer, you can use it with packages such as Pandas, NumPy, and IPython. If you are looking for a python framework that manipulates numbers on a computer while displaying or publishing results, SciPy is what you need.

Dask

You will probably want to use the Dask Python framework if you want a framework that offers advanced parallelism for analytics.

This is one of the best python frameworks. It is because it allows a Python developer to perform at scale while integrating with Scikit-Learn, Pandas, and NumPy.

This framework has schedulers that enable a python developer to scale to thousand-node clusters. The most powerful supercomputer uses its algorithm.

Numba

As a Python developer, you may need this optimizing compiler for compiling Python. It compiles to CPU and GPU machine code. That is because it uses LLVM compiler infrastructure.

The Python framework is often used for scientific research that uses NumPy arrays. You need to use it with data science applications or Ml-specific hardware to do an excellent job. 

Conclusion (Best Python Frameworks)

To become an excellent Python developer, these are some of the best Python frameworks and libraries.

We hope that this article on how to become a python developer was helpful to you. And you are now ready to explore these frameworks and libraries for Python.

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