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Python and C++ are both widely used programming languages. Both are used for creating software, applications, etc. However, they differ in various characteristics, such as object-oriented paradigm, performance, design, etc. They are both very powerful languages that are used nowadays. C++ is used for heavy development, including operating systems, embedded systems, and game development, while Python is used for machine learning, data science, automation, scripting, etc. In this article, we will see a thorough analysis of Python and C++ languages.
What is Python?
Python is a high-level, easy-to-learn programming language. It is a powerful programming language that works as a general-purpose language and provides different features to be combined with other libraries. It has efficient data structures with an effective object-oriented approach to programming. Python’s interpreted nature, dynamic typing, and beautiful syntax make it a perfect language for scripting and quick application development across various platforms.
Python was discovered in the 1980s and was implemented in 1989. Python has evolved since its first implementation by introducing various great features like garbage collection, list comprehension, etc. As Python is an interpreted language, therefore it consists of an interpreter that is extended with various data types and functions like C++. Python is extensible in C or C++ and offers interfaces to numerous system calls, libraries, and window systems. It can also be used as an extension language for programs that require an interface that can be programmed.
Features
Python is a language that helps you work more quickly and integrate the systems effectively. To work effectively, it provides different features that you can use to build applications. Below are some of the key features of Python:
- Python is an open-source language, meaning the code is available to everyone online and can also contribute to making it a better programming language with great features.
- Python is an easy-to-read language as its syntax is very simple like English. Being a straightforward language, it makes it simple to learn a programming language.
- Python does not use curly braces; rather, it uses indentation for defining the code blocks.
- It is a dynamically typed language, meaning the type of variables declared in the program are determined at the runtime.
- The biggest feature of Python is garbage collection, which is automatically invoked. It also supports interactive debugging and testing modes.
- Python is used in different applications including Machine Learning, Artificial Intelligence, data science, Data analysis, Web application development, etc.
- Python is an object-oriented language, meaning you can work like a real-world object in a program.
- Less code writing and more dependence on third-party libraries. Python has a very vast ecosystem to choose from various third-party libraries to integrate into your application.
- A vast standard library that supports a wide range of typical programming activities is included with Python.
- Python has interoperability, meaning you can easily integrate it with other systems or languages like C++, etc.
Syntax
The given below is a simple syntax to write a Python program:
print(‘Hello World!’)
In this example, the print statement is used to print a “Hello World!” text. The syntax is very simple and straightforward, like English. We will see more in-depth examples later in this article.
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What is C++?
C++ is a general-purpose and high-level programming language. It was designed by scientist Bjarne Stroustrup in 1979. C++ was built on top of the C programming language with the inclusion of classes. C++’s design highlights include performance, efficiency, and ease of use. It was created with systems programming, embedded, resource-constrained applications, and big systems in mind. But it has now evolved to include the features of object-oriented programming, functional programming, etc.
C++ is intended to be a compiled language. It is typically transformed into machine language that the system can understand directly, resulting in a very efficient program. C++ uses the compiler to compile the program and show the output.
Features
C++ or CPP programming language provides various features like OOP, functional programming, compilation, etc. Below are some of the key features of C++:
- C++ is a compiled programming language. When the program is compiled into machine code, it results in high efficiency and proper resource usage.
- C++ is used for high performance and security.
- Unlike Python, C++ is a statically typed language, meaning you have to define the variable types, and they are known at the compile time.
- C++ allows object-oriented paradigms like polymorphism, inheritance, encapsulation, etc for making the code reusable.
- C++ provides the flexibility that developers can utilise the Procedural, object-oriented, and generic programming paradigms that best fit their needs.
- C++ is known for the use of pointers and structure definition.
- C++ is case-sensitive, meaning you have to be careful with capital and small letters.
- C++ is used in applications like game development, embedded systems, and various other fields.
- STL or standard template library is provided by C++ that contains the template classes and functions.
- C++ also supports interoperability.
Syntax
The given below is a simple syntax to write a C++ program:
#include <iostream>
int main() {
cout << “Hello World!” << endl;
return 0;
}
In this example, we are printing a “Hello World!” text using the “cout” statement.
Design Philosophy
Python
According to Guido van Rossum, grouping using indentation is quite elegant and makes an average Python program far more readable. Most users eventually grow to adore this function. Python was built with the aim of making the language more readable, simple, and much more usable. The key principles include:
- Simple is better than complex.
- Explicit is better than implicit.
- Readability counts
Python was designed with a focus on increasing the programmer’s productivity, readability, etc. It was also prioritised for clean and concise code that was useful for scripting and rapid prototyping.
C++
C++ was built to build embedded systems or to control hardware that requires high performance, efficiency, etc. C++ emphasises more on system control and performance with low-level machines. The key principles include:
- Give control to programmers for manual memory management so that allocation and deallocation can be done easily.
- Fine-tuning options due to its complex code syntax.
- C language compatibility for the use of low-level features.
Performance
Python
Python’s performance, when compared to C++ is slower than a general C++ program. Due to its interpreted or runtime nature, it makes it slower as here it requires the runtime code translation, and that too line-by-line. This performance issue creates an overhead for Python.
But it is also faster in case of rapid development and when using third-party libraries including NumPy, Cython, etc.
C++
Because C++ is compiled straight into machine code, it improves performance. The C++ compiler can perform more precise optimisation. As a result, extremely effective executables are produced, which are excellent for jobs requiring a lot of performance, such as systems programming, game development, etc.
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Standard Libraries
Python
Python has a very vast and extensive standard library. It supports numerous tasks, including data science, data analysis, artificial intelligence, machine learning, etc. The library includes modules written in Python that offer standardised solutions for many common problems encountered in everyday programming, as well as built-in (originally written in C) modules that give programmers access to system capabilities like file I/O and more.
Python libraries can be installed and used using the famous package manager pip. Python internal library contains various components that can be used for individual programs, building complete applications using frameworks, and more. These include:
- Built-in-Types (Comparisons, Numeric Types — int, float, complex, Boolean Type – bool Iterator, Types, etc.)
- Text Processing Services (string, re, difflib, etc.)
- Data Types (datetime, zoneinfo, calendar)
- File and Directory Access (pathlib, os.path, etc.)
- File formats (csv, tomlib, etc)
- Generic Operating System Services (os, io, time, etc.)
- Internet Protocols and Support (smtplib, urllib, poplib, etc.)
These are some of the common packages and modules that Python gives you to use for building various applications and programs. Apart from this, there are various other third-party popular libraries also like:
- NumPy and SciPy (scientific computing)
- Pandas (data manipulation)
- TensorFlow and PyTorch (machine learning)
- Django and Flask (web development)
C++
C++ has a very famous Standard Template Library (STL), which offers data structures, algorithms, and iterators. A variety of data-storing and data-manipulating containers, including vector, list, map, set, and stack, are provided by the STL. Third-party libraries are also widely accessible for a variety of uses. The third-party libraries include:
- Qt (GUI development)
- OpenCV (computer vision)
- Unreal Engine and Unity (game development)
Use Cases and Examples
Python
Python is used for various tasks, including machine learning, artificial intelligence, data science, data analysis, and much more. Apart from this, Python is used in automation and scripting, and web application development using Django and Flask frameworks.
C++
C++ is used for developing various software infrastructures including operating systems, compilers, and libraries. C++ is a programming language that can manipulate hardware systems and increase their performance and efficiency. Software like Windows (OS), Google Chrome, Mozilla, TensorFlow, and many more are built using C++. C++ is also used for game development, especially simulation games. There are also other use cases of C++, such as medical technology, telecommunications, research, space technology, financial tools, etc.
Let’s see some examples of using Python and C++ through code and their output to compare their performance, STLs, object-oriented paradigms, and other fields.
Example 1: The below example demonstrates the performance of Python using a simple program.
# import the time package
import time
# function to calculate the sum of cubes of a number
def cube_sum(number):
sum = 0
for i in range(1, number + 1):
sum += i * i * i
return sum #return the sum of cubes
# define the number as 5_000_000
number = 5_000_000
start_time_p = time.time() # starting time of program
calc_sum = cube_sum(number) #find the cube sum
end_time_p = time.time() # ending time of program
# print the sum and time taken for performance
print(f"The sum of cubes is: {calc_sum}")
print(f"Performance (Time taken): {end_time_p - start_time_p:.4f} seconds")
Output:
The sum of cubes is: 156250062500006250000000000
Performance (Time taken): 0.6051 seconds
Explanation:
In this example, we are calculating the sum of the cube of a number given by the user as input. We are taking the starting time and the ending time to find the difference between both to find out the performance. Here, we have calculated the cube of 5 million (5000000) and we have got the sum as 156250062500006250000000000. The time taken by the program to find the sum of cubes of 5 million is 0.605 seconds and denotes the performance of the program.
Example 2: The below example demonstrates the performance of C++ using a simple program.
#include <iostream> // for io stream
#include <chrono> // for timings
// function to calculate the sum of cubes of a number
long long cube_sum(int number) {
long long sum = 0;
for (int i = 1; i <= number; ++i) {
sum += i * i * i;
}
return sum; // returning the cube sum
}
// Main function
int main() {
// input number of 5 million
int number = 5'000'000;
auto start = std::chrono::high_resolution_clock::now();
long long res = cube_sum(number);
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration el = end - start;
std::cout << "The sum of cubes is: " << res << std::endl;
std::cout << "Performance (Time taken): " << el.count() << " seconds" << std::endl;
return 0;
}
Output:
The sum of cubes is: 1104128615424
Performance (Time taken): 0.00991887 seconds
Explanation:
In this example, we are calculating the sum of the cube of a number given by the user as input in C++. We are taking the starting time and the ending time to find the difference between both to find out the performance. Here also, we have calculated the cube of 5 million (5000000) and we have got the sum as 1104128615424. The time taken by the program to find the sum of cubes of 5 million is 0.00991887 seconds and denotes the performance of the program.
Example 3: The below example demonstrates the performance using sorting of numbers in Python.
# import the time and random packages
import time
import random
# generate a list of 5 million numbers using random
number = 5_000_000
my_data = [random.randint(0, 5000000) for _ in range(number)]
start_time_p = time.time() # starting time of program
my_data.sort() # sort the data in place
end_time_p = time.time() # ending time of program
# print the time taken for sorting using STL of python
print(f"Performance (Time taken): {end_time_p - start_time_p:.4f} seconds")
Output:
Performance (Time taken): 2.3477 seconds
Explanation:
In this example, we are sorting the input number of 5 million using Python standard library sort(). We sorted the list of 5 million numbers that were randomly generated using the random() and calculated the time taken by the program to sort the list of integers. The time taken by sorting the 5 million list of integers came out to be 2.34 seconds which denotes the performance of the program.
Example 4: The below example demonstrates the performance using sorting of numbers in C++.
// import statements
#include <iostream>
#include <vector>
#include <algorithm>
#include <chrono>
#include <cstdlib>
// Main function
int main() {
int number = 5'000'000;
std::vector my_data(number);
// Generate a vector of 5 million random integers
for (int i = 0; i < number; ++i) {
my_data[i] = rand() % 5000000;
}
auto start = std::chrono::high_resolution_clock::now();
std::sort(my_data.begin(), my_data.end());
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration el = end - start;
std::cout << "Performance (Time taken to sort vector integers): " << el.count() << " seconds" << std::endl;
return 0;
}
Output:
Performance (Time taken to sort vector integers): 2.71222 seconds
Explanation:
In this example, we are sorting the input number of 5 million using the vector and sort() from the standard template library. Here also, we sorted the list of 5 million numbers that were randomly generated using the rand() and for loop and calculated the time taken by the program to sort the list of integers. The time taken for sorting the 5 million list of integers came out to be 2.712 seconds which denotes the performance of the program.
Example 5: The below example demonstrates the performance while calculating the vehicle mileage in Python.
import time # for timing
import math # for math operations
# defining a parent class
class Vehicle:
def mileage(self):
raise NotImplementedError("It must be overridden by child classes!")
class Car(Vehicle):
def __init__(self, distance, fuel):
self.distance = distance
self.fuel = fuel
def mileage(self):
return self.distance / self.fuel
class Bike(Vehicle):
def __init__(self, distance, fuel):
self.distance = distance
self.fuel = fuel
def mileage(self):
return self.distance / self.fuel
# Creating different mileages
mileages = [Car(300, 15), Bike(200, 40), Car(4050, 18), Bike(250, 45)]
start_time = time.time()
# Calculating and printing the area of each shape
for avg in mileages:
print(f"The mileage of vehicles is: {avg.mileage()}")
end_time = time.time()
print(f"Performance (Time taken): {end_time - start_time:.4f} seconds")
Output:
The mileage of vehicles is: 20.0
The mileage of vehicles is: 5.0
The mileage of vehicles is: 225.0
The mileage of vehicles is: 5.555555555555555
Performance (Time taken): 0.0001 seconds
Explanation:
In this example, we created a parent class of “Vehicle” with an abstract method mileage() in Python. The Car and Bike classes are derived as sub-classes of the Vehicle class to calculate the mileage of each vehicle. We have created 4 different instances of classes and calculated their execution time to find the performance. The performance of the program came out to be 0.0001 seconds.
Example 6: The below example demonstrates the performance while calculating the vehicle mileage in C++.
// import statements
#include <iostream>
#include <vector>
#include <memory>
#include <cmath>
#include <chrono>
using namespace std;
using namespace chrono;
// Parent vehicle class
class Vehicle {
public:
virtual double mileage() const = 0; // Pure virtual function
virtual ~Vehicle() = default;
};
// Inherited Car class using Vehicle class
class Car : public Vehicle {
private:
double distance, fuel;
public:
Car(double d, double f) : distance(d), fuel(f) {}
double mileage() const override {
return distance / fuel;
}
};
// Inherited Bike class using Vehicle class
class Bike : public Vehicle {
private:
double distance, fuel;
public:
Bike(double d, double f) : distance(d), fuel(f) {}
double mileage() const override {
return distance / fuel;
}
};
// Main function
int main() {
// Vector of unique pointers to vehicles
vector<unique_ptr> vehicles;
vehicles.push_back(make_unique(300, 15));
vehicles.push_back(make_unique(200, 40));
vehicles.push_back(make_unique(4050, 18));
vehicles.push_back(make_unique(250, 45));
auto start = high_resolution_clock::now();
// Calculating and printing the mileages of each vehicle object
for (const auto& avg : vehicles) {
cout << "The mileage of vehicles is: " << avg->mileage() << endl;
}
auto end = high_resolution_clock::now();
duration el = end - start;
cout << "Performance (Time taken): " << el.count() << " seconds" << endl;
return 0;
}
Output:
The mileage of vehicles is: 20
The mileage of vehicles is: 5
The mileage of vehicles is: 225
The mileage of vehicles is: 5.55556
Performance (Time taken): 6.211e-05 seconds
Explanation:
In this example, we created a parent class of “Vehicle” with an abstract method mileage() using C++. The Car and Bike classes are derived as sub-classes of the Vehicle class to calculate the mileage of each vehicle. We have created 4 different instances of classes and calculated their execution time to find the performance. The performance of the program came out to be 6.211e-05 seconds.
Community Support
Python
Python has great community support for programmers and developers. Being an open-source language, new programmers are regularly contributing to it to make it a better, more efficient, and optimised language. There is a thriving Python community with a wealth of tutorials, forums, and documentation. Python’s community can help support beginners and experts and adds to the ever-increasing open-source knowledge base. Community support has been further reinforced by the language’s growing use in cutting-edge sectors like AI/ML, data science, application development, etc.. Because of its community-driven development approach, Python offers developers a bunch of tools and ongoing enhancements.
C++
The C++ community is very extensive and active, especially among developers working on high-performance applications. Community support is provided as Discord servers, Reddit, blogs, YouTube channels, etc. C++ has a long history, and its community consists of many experienced programmers and collaborators. The language’s standardisation group works hard to evolve the language by including new features and improvements.
Interoperability
Python
Python is quite good at integrating with other languages and systems, as it provides several methods for doing so. C/C++ libraries can be called by Python programs thanks to tools like Cython, ctypes, etc The Python API (Application Programmers Interface) defines a set of variables, macros, and functions that give users access to the majority of the Python run-time system in order to facilitate extensions.
Python is perfect for integrating different parts of a software stack because of its ability to function as a glue language.
C++
C++ is also interoperable like Python. Here, other languages can also work with C++, however, it may require more work to do so. The language can readily interact with C libraries because of its close affinity to C. The compiler-research has also proposed a C++ language interoperability layer (LIL) which bridges C++ to another language using a set of reusable utilities. Performance-critical modules can also be written in C++ and invoked from higher-level languages like Python.
Key Differences Between Python and C++
The given below are some of the key differences between Python and C++ programming languages:
| Criteria | Python | C++ |
| Developed by | Python was developed by Guido van Rossum. | C++ was designed by Bjarne Stroustrup. |
| Language type | Python is a high-level language. | C++ is an intermediate language that is a combination of both high-level and low-level language. |
| Typing | Python supports dynamic (at run-time) typing. | C++ supports static (at compile time) typing. |
| Syntax | The indentation is used for syntax, and no use of curly braces. | The curly braces are used to define the code block syntaxes. |
| Compilation | Python allows the interpreted compilation. | C++ allows the compiled compilation. |
| Performance | Because Python is an interpreted language, the performance is slower. | C++ is a compiled language; therefore, the performance is faster as compared to Python |
| Memory Management & Garbage collection | There is automatic memory management and garbage collection. | There is manual memory management (allocation and deallocation) and no garbage collection. |
| Paradigms | Python supports both the procedural and OOP. | C++ supports both functional and OOP. |
| Error handling | Only exceptions can be handled in Python. | Exceptions and error codes can be seen in C++. |
| Speed | Due to straightforward syntax and dynamic typing, the development speed is faster. | The development speed is slower compared to Python. |
| Readability | Due to its English-like syntax, the code is more easily readable. | The code may look less readable for non-programmers. |
| Function overloading | Python doesn’t support function overloading. | C++ supports function overloading. |
| Multithreading | Using the GIL (Global Interpreter Lock) limits true parallelism. | By default, multithreading can be seen in C++. |
| Community | It has a very large community support of experienced developers. | It also has a very large community support of experienced developers. |
Conclusion
Python is characterised by its ease of learning, reading, and supporting quick development; hence, it is typically preferred for machine learning, artificial intelligence, web development, data science, and scripting. On the other hand, C++ is considered an exceptional choice for those who prefer a powerful programming language as it is highly performant, allows low-level memory manipulation, and can be used in system programming or game development where high efficiency is desired. In this article, we have covered a thorough analysis between Python and C++. The Python and C++ design philosophy, performance, standard libraries, community support, and interoperability are also discussed. We have also compared both language’s performance using code examples in various aspects.
Developers should understand the distinctive features and capabilities of each language. This empowers them to select an appropriate tool that serves their specific needs— allowing them to exploit the individual strengths of each language to develop high-performing software efficiently and effectively.
FAQs
What is the main difference between Python and C++ languages?
The main difference between Python and C++ is that Python is an interpreted programming language that needs an interpreter for compilation, whereas C++ is a precompiled programming language where an interpreter is not required during compilation.
Is C++ more efficient than the Python language?
Yes, we can call C++ more efficient as C++ code runs more quickly than Python code. It can be used in applications that need high-performance computing. This is so that the computer's processor may directly run C++ code after it has been compiled or converted into machine code.
Is there an object-oriented paradigm in Python and C++?
Yes, both Python and C++ support the object-oriented paradigm for supporting real-world use cases. Although the OOP concept in Python is simpler, complex OOP designs are more efficiently and controllably handled in C++.
Is C++ more memory efficient than Python?
Python is said to be more memory efficient because it automatically manages memory, so we don't need to manually allocate or deallocate memory. Whereas in C++, the memory is manually allocated and released using calloc(), malloc(), and free() methods.
Which one is faster: Python or C++?
C++ is a faster language than Python since it is based on a compiler. The same code, when run using Python and C++, will first be completed in C++ because C++ transforms the high-level code into machine code that can be executed directly by the computer's processor.
Updated on October 10, 2024
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