This Python tutorial explains both basic and advanced Python concepts. This Python tutorial is intended for both beginners and experts. You'll have a strong foundation in Python after this lesson, giving you the expertise you need to advance your education and become a top-tier software engineer.

What is Python?

Python is a popular general-purpose, interactive, object-oriented, and high-level programming language. Python is a dynamically typed, garbage-collected programming language. It was created between 1985 and 1990 by Guido van Rossum. Python source code is also accessible under the GNU General Public License (GPL), just like Perl.
Python supports a variety of programming paradigms, including procedural, object-oriented, and functional programming. Python's design philosophy places a strong emphasis on code readability through the use of substantial indentation.
Beginning with fundamental ideas and progressing to more complex ones, this tutorial provides a comprehensive understanding of the Python programming language. You will be guided through straightforward and useful methods as you learn the Python programming language in this tutorial.

Where is Python used?

Python is a well-liked, all-purpose programming language that is employed in almost every technical field. Given below are some examples of where Python is used.

  • Data Science
  • Date Mining
  • Desktop Applications
  • Console-based Applications
  • Mobile Applications
  • Software Development
  • Artificial Intelligence
  • Web Applications
  • Enterprise Applications
  • 3D CAD Applications
  • Machine Learning
  • Computer Vision or Image Processing Applications.
  • Speech Recognitions

Python Popular Frameworks and Libraries

Widely used in many different fields, including machine learning, artificial intelligence, web applications, etc., Python has a large selection of libraries and frameworks. Following are some common frameworks and libraries in Python:

  • Web development (Server-side) - Django Flask, Pyramid, CherryPy
  • GUIs based applications - Tk, PyGTK, PyQt, PyJs, etc.
  • Machine Learning - TensorFlow, PyTorch, Scikit-learn, Matplotlib, Scipy, etc.
  • Mathematics - Numpy, Pandas, etc.

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