Last Updated on January 17, 2023
Python may be an interpreted language, but it has a huge library of pre-written code that can be easily ran to process data. Spending time learning how to read Python code is the best way to start.
Python is an interpreted language. This means that the runtime system can interpret Python programs directly. In fact, there’s no need to compile the program first- Python “interpreters” (which can also be called “compilers”, but not with the same meaning) work by reading Python source code, parsing the text into the appropriate internal representation, and then executing it in a run-time execution environment. Usually the interpreter executes each statement in order until it reaches a full stop, such as a return statement or the end of program. When this happens, control returns back to the interpreter again.
Interpreting language means translating it directly into a machine-readable form. A program written in an interpreted language is compiled at runtime, which can run slower than compiled languages. The advantage is that it can be run on machines with less powerful processors designed for general use.
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Python is an interpreted language. Here we explain the meaning of interpreted language, the formats of Python and what it has to offer.
The lesson description calls Python an “interpreted” language. What does that mean?
An interpreter takes the code you write an executes (runs) whatever actions you specified, creates the variables you created, and does a lot of behind-the-scenes work to ensure it runs smoothly or tells you about errors.
Python is called an interpreted language because it goes through an interpreter, which turns code you write into the language understood by your computer’s processor. Later on when you work on a project on your own computer, you will download and use the Python interpreter to be able to write Python code and execute it on your own!
- createdMar ’18
- last replyJun ’18
21 DAYS LATER
An interpreter is basically very different from a compiler.
The interpreter is used mostly for scripting languages like python etc, which directly executes the code (no separate stages for compiling the code and then executing it) by converting it into an intermediate code usually called the byte code. This definitely increases the speed of execution.
This byte code is platform independent which can later be run on different platforms so the code is portable.1 Reply1524 DAYS LATER
Except platform independent like Java, the compiled code is more efficient
with optimization even recently Java made a lot of improvement for performance.
Besides, in real-time operations, the interpreted language is normally not fast enough
to handle interrupts or real-time response in the sub-micron seconds.
In terms of sizing, the compiled language can take advantage of machine/hardware
architecture to use directly assembly codes for specific operations like copy, move
just as under x86 or ARM.42 MONTHS LATER
Python is an “interpreted” language.
This means it uses an interpreter. An interpreter is very different from the compiler.
An interpreter executes the statements of code “one-by-one” whereas the compiler executes the code entirely and lists all possible errors at a time.
That’s why python shows only one error message even though your code has multiple errors. This will help you to clear errors easily and it definitely will increase the execution speed.
Python is an interpreted language, which means that when you write a program in Python and run it, the computer reads the program (just like you!) and carries out the instructions one by one.
Let’s see the difference between Compiled and Interpreted Language:
|S.NO.||COMPILED LANGUAGE||INTERPRETED LANGUAGE|
|1||A compiled language is a programming language whose implementations are typically compilers and not interpreters.||An interpreted language is a programming language whose implementations execute instructions directly and freely, without previously compiling a program into machine-language instructions.|
|2||In this language, once the program is compiled it is expressed in the instructions of the target machine.||While in this language, the instructions are not directly executed by the target machine.|
|3||There are at least two steps to get from source code to execution.||There is only one step to get from source code to execution.|
|4||In this language, compiled programs run faster than interpreted programs.||While in this language, interpreted programs can be modified while the program is running.|
|5||In this language, compilation errors prevent the code from compiling.||In this languages, all the debugging occurs at run-time.|
|6||The code of compiled language can be executed directly by the computer’s CPU.||A program written in an interpreted language is not compiled, it is interpreted.|
|7||This language delivers better performance.||This languages delivers relatively slower performance.|
As a Machine Learning Engineer, I have been using Python for more than a year. Recently, I have also started learning C++, for fun. It made me realize how easy and intuitive Python is. I got more curious about how Python is different from other languages and its working. In this blog, I try to scratch Python’s inner working.
Python started as a hobby project by Guido Van Rossum and was first released in 1991. A general purpose language, Python is powering large chunk of many companies like Netflix and Instagram. In an interview, Guido compares Python to languages like Java or Swift and says that while the latter two are a great choice for software developers — people whose day job is programming, but Python was made for people whose day job has nothing to do with software development but they code mainly to handle data.
When you read about Python, quite often you come across words like — compiled vs interpreted, bytecode vs machine code, dynamic typing vs static typing, garbage collectors, etc. Wikipedia describes Python as
Python is an interpreted, high-level, general-purpose programming language. It is is dynamically typed and garbage-collected.
When you write a program in C/C++, you have to compile it. Compilation involves translating your human understandable code to machine understandable code, or Machine Code. Machine code is the base level form of instructions that can be directly executed by the CPU. Upon successful compilation, your code generates an executable file. Executing this file runs the operations in your code step by step.
For the most part, Python is an interpreted language and not a compiled one, although compilation is a step. Python code, written in .py file is first compiled to what is called bytecode (discussed in detail further) which is stored with a .pyc or .pyo format.
Instead of translating source code to machine code like C++, Python code it translated to bytecode. This bytecode is a low-level set of instructions that can be executed by an interpreter. In most PCs, Python interpreter is installed at /usr/local/bin/python3.8. Instead of executing the instructions on CPU, bytecode instructions are executed on a Virtual Machine.
One popular advantage of interpreted languages is that they are platform-independent. As long as the Python bytecode and the Virtual Machine have the same version, Python bytecode can be executed on any platform (Windows, MacOS, etc).
Dynamic typing is another advantage. In static-typed languages like C++, you have to declare the variable type and any discrepancy like adding a string and an integer is checked during compile time. In strongly typed languages like Python, it is the job of the interpreter to check the validity of the variable types and operations performed.
Disadvantages of Interpreted languages
Dynamic typing provides a lot of freedom, but simultaneously it makes your code risky and sometimes difficult to debug.
Python is often accused of being ‘slow’. Now while the term is relative and argued a lot, the reason for being slow is because the interpreter has to do extra work to have the bytecode instruction translated into a form that can be executed on the machine. A StackOverflow post makes it easier to understand using an analogy —
If you can talk in your native language to someone, that would generally work faster than having an interpreter having to translate your language into some other language for the listener to understand.
What exactly is Garbage Collection?
In older programming languages, memory allocation was quite manual. Many times when you use variables that are no longer in use or referenced anywhere else in the program, they need to be cleaned from the memory. Garbage Collector does that for you. It automatically frees up space without you doing anything. The memory management works in two ways —
In a simplified way, it keeps track of the number of references to an object. When that number goes down to zero, it deletes that object. This is called reference counting. This cannot be disabled in Python.
In cases where object references itself or two objects refer each other, a process called generation garbage collection helps. This is something traditional reference counting cannot take care of.
What is __pycache__ ?
Many times in your personal project or on GitHub, you might have seen a folder named __pycache__ being created automatically.
/folder - __pycache__ - preprocess.cpython-36.pyc - preprocess.py
As you can see, the filename is the same as the one outside __pycache__ folder. The .pyc extension tells us that the file contains bytecode for preprocess.py. The names cpython denotes the type of interpreter. CPython means that the interpreter was implemented in C language. Similarly, JPython is a Python interpreter implemented in Java.
But why is the folder created in the first place? Well, it slightly increases the speed of the Python program. Unless you change your Python code, recompilation to bytecode is avoided, thereby saving time.