Python vs C++

Both Python and C++ are among the most popular programming languages. Both of them have their advantages and disadvantages. In this chapter, we shall take a look at their characteristic features.

Compiled vs Interpreted

Like C, C++ is also a compiler-based language. A compiler translates the entire code in a machine language code specific to the operating system in use and processor architecture.

Python is interpreter-based language. The interpreter executes the source code line by line.

Cross platform

When a C++ source code such as hello.cpp is compiled on Linux, it can be only run on any other computer with Linux operating system. If required to run on other OS, it needs to be compiled.

Python interpreter doesn’t produce compiled code. Source code is converted to byte code every time it is run on any operating system without any changes or additional steps.


Python code is easily portable from one OS to other. C++ code is not portable as it must be recompiled if the OS changes.

Speed of Development

C++ program is compiled to the machine code. Hence, its execution is faster than interpreter based language.

Python interpreter doesn’t generate the machine code. Conversion of intermediate byte code to machine language is done on each execution of program.

If a program is to be used frequently, C++ is more efficient than Python.

Easy to Learn

Compared to C++, Python has a simpler syntax. Its code is more readable. Writing C++ code seems daunting in the beginning because of complicated syntax rule such as use of curly braces and semicolon for sentence termination.

Python doesn’t use curly brackets for marking a block of statements. Instead, it uses indents. Statements of similar indent level mark a block. This makes a Python program more readable.

Static vs Dynamic Typing

C++ is a statically typed language. The type of variables for storing data need to be declared in the beginning. Undeclared variables can’t be used. Once a variable is declared to be of a certain type, value of only that type can be stored in it.

Python is a dynamically typed language. It doesn’t require a variable to be declared before assigning it a value. Since, a variable may store any type of data, it is called dynamically typed.

OOP Concepts

Both C++ and Python implement object oriented programming concepts. C++ is closer to the theory of OOP than Python. C++ supports the concept of data encapsulation as the visibility of the variables can be defined as public, private and protected.

Python doesn’t have the provision of defining the visibility. Unlike C++, Python doesn’t support method overloading. Because it is dynamically typed, all the methods are polymorphic in nature by default.

C++ is in fact an extension of C. One can say that additional keywords are added in C so that it supports OOP. Hence, we can write a C type procedure oriented program in C++.

Python is completely object oriented language. Python’s data model is such that, even if you can adapt a procedure oriented approach, Python internally uses object-oriented methodology.

Garbage Collection

C++ uses the concept of pointers. Unused memory in a C++ program is not cleared automatically. In C++, the process of garbage collection is manual. Hence, a C++ program is likely to face memory related exceptional behavior.

Python has a mechanism of automatic garbage collection. Hence, Python program is more robust and less prone to memory related issues.

Application Areas

Because C++ program compiles directly to machine code, it is more suitable for systems programming, writing device drivers, embedded systems and operating system utilities.

Python program is suitable for application programming. Its main area of application today is data science, machine learning, API development etc.

The following table summarizes the comparison between C++ and Python −

Criteria C++ Python
Execution Compiler based Interpreter based
Typing Static typing Dynamic typing
Portability Not portable Highly portable
Garbage collection Manual Automatic
Syntax Tedious Simple
Performance Faster execution Slower execution
Application areas Embedded systems, device drivers Machine learning, web applications
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