Python is one of the most popular interpreted languages, but have you ever wondered why Python is known as an interpreted language whereas other programming languages such as C, C++, Java, and so on generate results after compilation? Let us discuss why python is interpreted language.
What is an Interpreted Language?
Any programming language that isn’t already in "machine code" before runtime is considered an interpreted language. An interpreted language is a computer programming language in which the instructions are executed without first being compiled into machine instructions.
In other words, unlike compiled languages, an interpreted language does not require prior translation. The translation occurs concurrently with the program’s execution.
Why Python is Interpreted Language?
The most common saying, which is also published in various books, is that Python is an interpreted language, however, the hidden fact is that Python is both compiled and interpreted. This might seem contradictory at first, but it’s important to understand how Python’s compilation and interpretation work together.
When you write a Python program, it is initially in source code form. This source code needs to be transformed into an executable form that can be executed by the computer. There are two ways to do this:
Compilation: In this process, the source code is translated into machine code or bytecode ahead of time. The resulting compiled code can be executed directly without any further processing. Python uses a compiler to transform the source code into bytecode, which can be executed by the Python virtual machine. This is how Python code is executed in environments like PyCharm, Visual Studio Code, or other integrated development environments (IDEs) that use Python’s static analysis tools.
Interpretation: In this process, the source code is interpreted and executed directly by the interpreter at runtime. When you run a Python script from the command line or an interactive shell, the interpreter reads the source code and executes it line by line. This is how Python code is executed in interactive environments like Jupyter notebooks, or in web applications where Python code is executed on the server side.
Python’s compilation and interpretation work together to provide a flexible and dynamic programming environment. When Python code is compiled, the resulting bytecode can be cached and reused for faster execution in the future. This also allows for some level of static analysis and optimization of the code. When Python code is interpreted, it allows for rapid development and debugging, and provides a more interactive programming experience.
Advantages of Interpreted Language
Here are some advantages of interpreted languages:
- Portability: Interpreted languages are typically platform-independent, which means that they can run on different operating systems without modification. This is because the interpreter itself is usually written in a low-level language that is compiled for each platform.
- Easy to learn and use: Interpreted languages typically have simple syntax and a smaller set of language constructs, making them easier to learn and use than compiled languages. Additionally, interpreted languages often have interactive shells or REPLs (Read-Eval-Print Loops) that allow for quick prototyping and experimentation.
- Rapid development: Because there is no need to compile or link the code before running it, interpreted languages offer a faster development cycle than compiled languages. Changes to the code can be tested and seen immediately, which is especially useful in a development environment.
- Flexibility: Interpreted languages are often more flexible than compiled languages, as they allow for dynamic typing and more dynamic programming constructs such as reflection and introspection. This can make it easier to write code that adapts to changing requirements or situations.
- Debugging: Debugging can be easier in an interpreted language because error messages often include detailed information about the line of code where the error occurred. Additionally, many interpreted languages include debugging tools that allow developers to step through code and inspect variables at runtime.
Disadvantages of Interpreted Language
Here are some disadvantages of interpreted languages:
- Slower execution speed: Interpreted languages are generally slower than compiled languages because the code is translated into machine instructions on-the-fly at runtime, rather than being compiled ahead of time into optimized machine code. This can make them less suitable for applications that require high performance, such as video games or scientific simulations.
- Higher memory consumption: Interpreted languages typically use more memory than compiled languages, since they need to keep both the source code and the interpreter in memory at runtime. This can become a problem when working with large datasets or when running on memory-constrained devices.
- Lack of static type checking: Interpreted languages often use dynamic typing, which means that variable types are determined at runtime rather than being declared in advance. This can make it harder to catch errors related to type mismatches or incorrect function calls until runtime.
- Security concerns: Interpreted languages can be more vulnerable to security issues such as cross-site scripting (XSS) or SQL injection attacks. This is because the code is executed in the context of the interpreter, which can make it easier for attackers to exploit vulnerabilities in the interpreter itself.
- Dependency on the interpreter: Interpreted languages require the interpreter to be installed on the target system, which can be a barrier to adoption if the interpreter is not widely available or if there are compatibility issues with other software on the system.
- Python is both a compiler and an interpreted language, which means that when we run a python program, it is first compiled and then interpreted line by line.
- Because the line of code passes through an interpretation run-time, which increases the run-time complexity, an interpreted language program executes slower than a language that is directly compiled.
- Python programs are less in size and easier to debug.
Here are some frequently asked questions
Q1 – How does the Python interpreter work?
A – The Python interpreter reads the source code line by line and then executes each line of code one at a time. It translates the source code into bytecode, which is then executed by the Python virtual machine.
Q2 – Can Python be compiled?
A – Yes, Python can be compiled into bytecode or into native machine code.
Q3 – Is Python slower than compiled languages like C or C++?
A – Yes, Python is generally slower than compiled languages like C or C++. This is because the interpreter has to translate the source code into bytecode at runtime, which takes more time than compiling the code ahead of time into machine code.