Python is a versatile, high-level language known for its simplicity and readability, making it a popular choice for a wide range of development projects.
Python's layered architecture and its rich ecosystem of libraries and tools make it a powerful programming language that is widely used in various fields, including web development, data science, scientific research, and more.

Features of Python

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Python Architecture

Python's layered architecture and its rich ecosystem of libraries and tools make it a versatile and powerful programming language that is widely used in various fields, including web development, data science, scientific research, and more. Python architecture includes following components.

Source Code
Python development begins with writing source code, which is a collection of instructions and statements in the Python programming language. These source code files typically have a .py extension.
Interpreter
The Python source code is processed and executed by the Python interpreter, which converts the human-readable code into low-level machine instructions. The interpreter handles the execution of code and manages the runtime environment.
CPython, Jython
Python has multiple implementations, with CPython being the most commonly used. CPython is the reference implementation and is written in C. Other implementations like Jython (Python for Java) and IronPython (Python for .NET) are adapted for specific use cases.
Python Virtual Machine (PVM)
The Python Virtual Machine (PVM) is a key component that executes intermediate code generated by the Python interpreter. PVM acts as an abstraction layer between the Python code and the underlying hardware, making Python code portable across different platforms.
Development Tools
Developers use various integrated development environments (IDEs) and code editors to write and manage Python code. Popular Python development tools include IDLE, PyCharm, Visual Studio Code, and Jupyter notebooks.
Third-Party Libraries and Frameworks
Python's architecture allows for the integration of third-party libraries and frameworks, which expand Python's capabilities for specific domains. For example, web developers often use Django and Flask, while data scientists rely on libraries like NumPy, pandas, and Matplotlib.

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