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Python for data science

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One of the main reasons why Python is widely used in scientific and research communities is its ease of use and simple syntax that makes it easy for people with no engineering background to adapt. It’s also more suitable for rapid prototyping.

According to engineers from academia and industry, the deep learning frameworks available with Python APIs, in addition to scientific packages, have made Python incredibly productive and versatile. Deep learning about Python frameworks has made a lot of progress and is being updated quickly.

When it comes to applications, ML scientists prefer Python as well. When it comes to areas such as creating fraud detection algorithms and network security, developers are leaning towards Java, while for applications such as natural language processing (NLP) and sentiment analysis, the developers have chosen Python because it provides a large set of libraries. that help solve complex problems. business problems are easy, build strong data systems and applications.

Here are some useful features of the Python language:

  • It uses an elegant syntax, so programs are easier to read.
  • This is easy access to a language that makes the program easier to run.
  • Large standard library and community support.
  • Python’s interactive mode makes it easy to testing your codes.
  • In Python it is also easy to extend your code by adding new modules that are implemented in another compiled language such as C++ or C.
  • Python — it is an expressive language that can be embedded into applications, offering a programmable interface.
  • Allows a developer to run code anywhere, including Windows, Mac OS X, UNIX, and Linux.
  • It’s free software security in several categories. It doesn’t cost anything to use or download Pythons or add to your application.

Most commonly used libraries for data science:

  • NumPy .
  • Rows — it is Processing and storing data in one-dimensional data. 
    Matplotlib
    : Matplolib — another useful Python library for data visualization. Descriptive analysis and data visualization are essential for any organization. Matplotlib provides various techniques to visualize data in a more efficient way. Matplotlib allows you to quickly create line graphs, pie charts, bar charts, and other professional-grade metrics. Using Matplotlib, every aspect of the figure can be customized. Matplotlib has interactive features such as scaling, scheduling and saving the graph in graphical format.
  • Scipy : Scipy — another popular Python library for data science and scientific computing. Scipy provides great functionality for scientific mathematics and computer programming. SciPy contains submodules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, Statmodel, and other tasks common in science and technology.
  • Scikit — learn : Sklearn — it is a Python library for machine learning. Sklearn provides various algorithms and functions that are used in machine learning. Sklearn is built with NumPy, SciPy and matplotlib. Sklearn provides simple and simple tools for data analysis and data analysis. It provides a set of common machine learning algorithms for users through a consistent interface. Scikit-Learn helps you quickly implement popular dataset algorithms and solve real-world problems.

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