Why Python is Called Interpreted Language - GeeksforGeeks (2024)

Python is frequently categorized as an interpreted language, but What does that suggest exactly? To apprehend why Python is called an interpreted language, it’s essential to discover the concepts of interpretation and compilation, in addition to the execution model of Python code.

Python is called an interpreted language because it executes code logic directly, line by line, without the need for a separate compilation step. In methods to compiled languages like C or C++, where the source code is translated into machine code before execution, Python code is translated into intermediate code by the Python interpreter.

Python is an Interpreted as well as Compiled language

Python is Interpreted as well as compilation language here, we will understand what does means by interpreted and compilation.

Compilation

In a compiled language, the supply code is translated into gadget code or executable code with the aid of a compiler before execution. The compilation system typically includes more than one ranges, which include lexical analysis, syntax analysis, optimization, and code era. Once the code is compiled, the resulting machine code may be achieved immediately with the aid of the computer’s processor.

Compiled languages offer numerous advantages, which include quicker execution pace and higher overall performance because the code has already been translated into gadget instructions. However, compilation introduces a further step within the development system, as developers need to assemble their code earlier than running it. Examples of compiled languages include C and Rust.

Interpretation

In an interpreted language, the supply code is performed line by using line through an interpreter at runtime. The interpreter reads each line of code, interprets it into machine instructions, and executes it straight away. There isn’t any separate compilation step, and the supply code stays in its unique form.

Interpreted languages offer benefits inclusive of portability, because the equal source code may be done on unique platforms with out change. Additionally, interpreted languages frequently offer functions together with dynamic typing and runtime introspection, that may make development and debugging easier. Examples of interpreted languages consist of Python, JavaScript, and Ruby.

Why Python is called Interpreted Language?

Python is mostly an interpreted language, even though it consists of elements of each interpretation and compilation. Let’s explore Python’s execution model to understand why it is called an interpreted language:

Source Code

In Python, the supply code is written in undeniable textual content documents with a .Py extension. These documents incorporate human-readable code this is written the use of Python’s syntax and language features.

Lexical Analysis

When a Python script is finished, step one is lexical evaluation, also called tokenization. In this step, the interpreter reads the source code and breaks it down into character tokens, together with keywords, identifiers, operators, and literals. This procedure creates a chain of tokens that represent the shape of the code.

Parsing

After lexical evaluation, the interpreter performs parsing, which involves studying the series of tokens to determine the syntactic structure of the code. The parser exams whether the code conforms to the guidelines of the Python language grammar and constructs a parse tree or summary syntax tree (AST) representing the code’s structure.

Compilation to Bytecode

Once the parse tree is generated, the interpreter interprets it into an intermediate representation called bytecode. Bytecode is a low-degree, platform-unbiased representation of the source code this is executed by using the Python Virtual Machine (PVM). The compilation to bytecode is accomplished by the Python compiler, also known as the Python bytecode compiler.

Execution with the aid of the Python Virtual Machine (PVM):

The bytecode generated via the compiler is done with the aid of the Python Virtual Machine (PVM). The PVM is a runtime surroundings that interprets and executes Python bytecode. It consists of a bytecode interpreter, memory manager, garbage collector, and other additives chargeable for dealing with the execution of Python code.

Just-In-Time (JIT) Compilation:

In addition to interpretation, Python also carries elements of compilation thru Just-In-Time (JIT) compilation. JIT compilation is a way in which the interpreter dynamically compiles bytecode into local system code at runtime, optimizing overall performance for regularly done code paths. This allows Python to obtain a balance among the power of interpretation and the overall performance of compilation.

Advantages of Interpretation

Interpreted languages like Python offer numerous advantages:

• Portability: Interpreted languages are regularly platform-independent, permitting the same source code to run on unique running systems and architectures without change.
• Rapid Development: Interpreted languages normally have shorter improvement cycles, as there may be no separate compilation step. Developers can write, test, and execute code more fast, facilitating fast prototyping and generation.
• Dynamic Typing: Interpreted languages frequently help dynamic typing, permitting variables to exchange their type dynamically at runtime. This flexibility can simplify improvement and make code greater adaptable to converting necessities.

Disadvantages of Interpretation

Interpreted languages like Python offer numerous disadvantages:

• Slower Execution : Interpreted languages like Python typically run slower compared to compiled languages because the interpreter translates the source code into intermediate code during runtime. This overhead can result in slower execution speeds, especially for performance-critical applications.
• Dependency on Interpreter : Python code requires the presence of the Python interpreter to run, which adds an extra layer of dependency. Users need to have the appropriate version of the interpreter installed on their systems to execute Python programs.
• Difficulty in Hiding Source Code : Since Python code is distributed as source files, it can be easier for others to access and view the source code. While tools like obfuscation can be used to make the code less readable, it’s inherently more difficult to protect Python code compared to compiled languages.

However, Interpreted languages Also have Some risks

• Performance Overhead: Interpretation can introduce a overall performance overhead compared to compiled languages, as the interpreter must parse, translate, and execute each line of code at runtime. This overhead can result in slower execution speeds for sure sorts of packages.
• Lack of Optimization: Interpreted languages may additionally lack a number of the optimization opportunities to be had to compiled languages, inclusive of static analysis and code optimization. This can bring about suboptimal overall performance for overall performance-important applications.

Conclusion

In end, Python is known as an interpreted language due to the fact its source code is done line through line by way of an interpreter at runtime. However, Python additionally contains factors of compilation, consisting of lexical analysis, parsing, and bytecode compilation. This hybrid method lets in Python to reap a stability between the ability of interpretation and the overall performance of compilation. Despite the overhead of interpretation, Python’s simplicity, versatility, and massive atmosphere make it a famous choice for a wide variety of packages, from internet improvement to clinical computing.