The Python programming language was seen as a filler, a way of writing scripts that “automate boring things” (as the popular Python tutorial says), or for quickly prototyping applications for implementation in other languages.
Python is already installed with popular Linux distros like Ubuntu and Fedora. To find out which version of Python you are running, type Python in the terminal emulator. The interpreter must start and print the version number.
However, over the last few years, Python has become a leading player in modern software development, infrastructure management, and data analysis. It is no longer the service language for office space, but a major force in shaping the management of web applications and systems, and a major driver of the explosive growth of big data analytics and machine intelligence.
What is Python used for?
The easiest use case for Python is scripting and automation. Python is not just a replacement for shell scripts or batch files; it is also used to automate interaction with web browsers or application GUIs, or to provision and configure a system using tools like Ansible and Salt. But scripting and automation are just the tips of the Python iceberg.
General Python application programming
You can create command-line GUI and cross-platform applications using Python and deploy them as stand-alone executables. Python has no built-in ability to generate a stand-alone binary from a script, but third-party packages, such as cx_Freeze and PyInstaller, can be used to do this.
Data science and machine learning with Python
Complex data analytics has become one of the busiest areas of IT and an important use case for Python. The vast majority of libraries used for data science or machine learning have Python interfaces, making it the most popular high-level command interface for machine learning libraries and other numerical Algorithms Classes in Bangalore.
Python web services and RESTful APIs
Native Python libraries and third-party web frameworks provide fast and affordable ways to build anything from simple REST APIs in a few lines of code to comprehensive data-based sites. The latest versions of Python have strong support for asynchronous operations, which allows sites to handle tens of thousands of requests per second with the right libraries.
Python Metaprogramming and Code Generation
In Python, everything in the language is an object, including the Python modules and libraries themselves. This allows Python to function as a highly efficient code generator, making it possible to write applications that handle their own functions and have the kind of extensibility that would be difficult or impossible to obtain in other languages. Python can be used to code efficiently in other languages such as code generating System design online Course such as LLVM.
Glue the Code in Python
Python is often referred to as a “glue language”, which means that it can interoperate different code (usual libraries with C language interfaces). Its use in data science and machine learning follows this line, but it is only the embodiment of the general idea. If you have applications or software domains that you would like to use but cannot communicate directly, you can use Data structure training in Python to connect them.
Where Python fails
It is also worth noting some activities for which Python is not suitable.
Python is a high-level language and therefore not suitable for system-level programming – no device drivers or operating system cores are used.
It is also not ideal for situations where cross-platform standalone binaries are required. You can build a standalone Python application for Windows, macOS, and Linux, but it’s not simple and elegant.
Finally, Python is not the best choice when speed is the top priority in all aspects of the application. You’d better use C / C ++ or another language of this caliber for that.