Is Python ready to overcome its competitors?

A soufflé is a baked egg dish that originated in France in the 18th century. The process of making an elegant and delicious French souffle is complex, and in the past, only French pastry professionals were usually prepared. However, with the mixtures of prefabricated souffle now widely available in supermarkets, this classic French dish has found its way in the kitchens of innumerable households.

Python is like prefabricated souffle mixtures in programming. Many studies have consistently demonstrated that Python is the most popular programming language among developers, and this advantage will continue to expand in 2025. Python stands out compared to languages ​​such as C, C ++, Java and Julia because it is highly legible and expressive, flexible and dynamic, always powerful. These characteristics make Python the most appropriate programming language for people, even without basic programming concepts. The following characteristics distinguish Python from other programming languages:

• Dynamic typification
• List the understandings
• Generators
• Paso of argument and mutability

These characteristics reveal the intrinsic nature of Python as a programming language. Without this knowledge, you will never really understand Python. In today's article, I will elaborate how Python stands out on other programming languages ​​through these characteristics.

Dynamic typification

For most programming languages ​​such as Java or C ++, explicit data type statements are required. But when it comes to Python, you don't have to declare the type of variable when you create one. This characteristic in Python is called dynamic typification, which makes Python flexible and easy to use.

List the understandings

The understandings of the list are used to generate lists of other lists applying functions to each element in the list. They provide a concise way to apply loops and optional conditions on a list.

For example, if you want to create a list of squares for even numbers between 0 and 9, you can use JavaScript, a regular loop in the understanding of the Python and Python list to achieve the same goal.

JavaScript

let squares = Array.from({ length: 10 }, (_, x) => x)  // Create array (0, 1, 2, ..., 9)
   .filter(x => x % 2 === 0)                          // Filter even numbers
   .map(x => x ** 2);                                 // Square each number
console.log(squares);  // Output: (0, 4, 16, 36, 64)

Regular loop in python

squares = ()
for x in range(10):
   if x % 2 == 0:
       squares.append(x**2)
print(squares) 

Understanding Python's list

squares = (x**2 for x in range(10) if x % 2 == 0)print(squares) 

The previous three sections of code generate the same list (0, 4, 16, 36, 64), but the understanding of the Python list is the most elegant because the syntax is clearly concise and expresses the intention, while the Python function is more detailed and requires an initialization and explicit appendage. JavaScript syntax is the least elegant and readable because it requires chain methods to use the matrix. From the filter and the map. Both the Python function and the JavaScript function are not intuitive and cannot be read as a natural language as the understanding of the Python list does.

Generator

Python generators are a special type of iterator that allows developers to iterate about a sequence of values ​​without storing them all in memory at the same time. They are created with the key performance. Other programming languages ​​such as C ++ and Java, although they offer similar functionality, do not have a keyword of incorporated performance in the same simple and integrated way. Here are several key advantages that make Python generators unique:

• Memory efficiency: Generators produce a value at the same time so that they only calculate and maintain an element in memory at any given time. This contrasts with, for example, a list in Python, which stores all elements of memory.
• Lazy evaluation: The generators allow Python to calculate the values ​​only as necessary. This “lazy” calculation results in significant performance improvements when it comes to large or potentially infinite sequences.
• Simple syntax: This could be the most important reason why developers choose to use generators because they can easily convert a regular function into a generator without having to administer the state explicitly.

def fibonacci():
   a, b = 0, 1
   while True:
       yield a
       a, b = b, a + b

fib = fibonacci()
for _ in range(100):
   print(next(fib))

The previous example shows how to use the key performance by creating a sequence. For the use of memory and the time difference between the code with and without generators, the generation of 100 fibonacci numbers can hardly see differences. But when it comes to 100 million numbers in practice, it is better to use generators because a list of 100 million numbers could easily force many system resources.

Paso of argument and mutability

In Python, we don't really assign values ​​to variables; Instead, we join variables to objects. The result of such action depends on whether the object is mutable or immutable. If an object is mutable, the changes made within the function will affect the original object.

def modify_list(lst):
   lst.append(4)

my_list = (1, 2, 3)
modify_list(my_list)
print(my_list)  # Output: (1, 2, 3, 4)

In the previous example, we would like to add '4' to the My_list list that is (1,2,3). Because the lists are mutable, the operation of adding behavior changes the original my_list list without creating a copy.

However, immutable objects, such as integers, floats, ropes, tuples and frozen, cannot be changed after creation. Therefore, any modification results in a new object. In the example, because the integers are immutable, the function creates a new integer instead of modifying the original variable.

def modify_number(n):
   n += 10
   return n

a = 5
new_a = modify_number(a)
print(a)      # Output: 5
print(new_a)  # Output: 15

Python's argument is sometimes described as “objective reference” or “assignment pass”. This makes Python unique because Python passes references uniformly (transfer of approval by object), while other languages ​​must be explicitly differentiated between the passage by value and the passage by reference. Python's uniform approach is simple but powerful. Avoid the need for explicit pointers or reference parameters, but requires that developers take into account mutable objects.

With Python's argument passing and mutability, we can enjoy the following benefits in coding:

• Memory efficiency: Save memory by passing references instead of making complete copies of objects. This especially benefits the development of the code with large data structures.
• Performance: Avoid unnecessary copies and, therefore, improves the general coding performance.
• Flexibility: This feature provides convenience to update the data structure because developers do not need to explicitly choose between the passage value and the passage by reference.

However, this Python feature forces developers to carefully choose between mutable and immutable data types and also brings a more complex purification.

So is Python really simple?

Python's popularity results from its simplicity, memory efficiency, high performance and friends for beginners. It is also a programming language that most resembles the natural language of a human, so even people who have not received systematic and holistic programming training can still understand it. These characteristics make Python a better option between companies, academic institutes and government organizations.

For example, when we would like to filter the “completed” orders with amounts greater than 200, and update a mutable summary report (a dictionary) with the total count and sum of the amounts for an electronic commerce company, we can use List understanding To create a list of orders that meet our criteria, omits the declaration of variable types and make changes in the original dictionary with Assignment pass.

import random
import time

def order_stream(num_orders):
   """
   A generator that yields a stream of orders.
   Each order is a dictionary with dynamic types:
     - 'order_id': str
     - 'amount': float
     - 'status': str (randomly chosen among 'completed', 'pending', 'cancelled')
   """
   for i in range(num_orders):
       order = {
           "order_id": f"ORD{i+1}",
           "amount": round(random.uniform(10.0, 500.0), 2),
           "status": random.choice(("completed", "pending", "cancelled"))
       }
       yield order
       time.sleep(0.001)  # simulate delay

def update_summary(report, orders):
   """
   Updates the mutable summary report dictionary in-place.
   For each order in the list, it increments the count and adds the order's amount.
   """
   for order in orders:
       report("count") += 1
       report("total_amount") += order("amount")

# Create a mutable summary report dictionary.
summary_report = {"count": 0, "total_amount": 0.0}

# Use a generator to stream 10,000 orders.
orders_gen = order_stream(10000)

# Use a list comprehension to filter orders that are 'completed' and have amount > 200.
high_value_completed_orders = (order for order in orders_gen
                              if order("status") == "completed" and order("amount") > 200)

# Update the summary report using our mutable dictionary.
update_summary(summary_report, high_value_completed_orders)

print("Summary Report for High-Value Completed Orders:")
print(summary_report)

If we would like to achieve the same objective with Java, since Java lacks incorporated generators and lists understandings, we have to generate a list of orders, then filter and update a summary using explicit loops and, therefore, make the code more complex, less legible and more difficult to maintain.

import java.util.*;
import java.util.concurrent.ThreadLocalRandom;

class Order {
   public String orderId;
   public double amount;
   public String status;
  
   public Order(String orderId, double amount, String status) {
       this.orderId = orderId;
       this.amount = amount;
       this.status = status;
   }
  
   @Override
   public String toString() {
       return String.format("{orderId:%s, amount:%.2f, status:%s}", orderId, amount, status);
   }
}

public class OrderProcessor {
   // Generates a list of orders.
   public static List generateOrders(int numOrders) {
       List orders = new ArrayList<>();
       String() statuses = {"completed", "pending", "cancelled"};
       Random rand = new Random();
       for (int i = 0; i < numOrders; i++) {
           String orderId = "ORD" + (i + 1);
           double amount = Math.round(ThreadLocalRandom.current().nextDouble(10.0, 500.0) * 100.0) / 100.0;
           String status = statuses(rand.nextInt(statuses.length));
           orders.add(new Order(orderId, amount, status));
       }
       return orders;
   }
  
   // Filters orders based on criteria.
   public static List filterHighValueCompletedOrders(List orders) {
       List filtered = new ArrayList<>();
       for (Order order : orders) {
           if ("completed".equals(order.status) && order.amount > 200) {
               filtered.add(order);
           }
       }
       return filtered;
   }
  
   // Updates a mutable summary Map with the count and total amount.
   public static void updateSummary(Map summary, List orders) {
       int count = 0;
       double totalAmount = 0.0;
       for (Order order : orders) {
           count++;
           totalAmount += order.amount;
       }
       summary.put("count", count);
       summary.put("total_amount", totalAmount);
   }
  
   public static void main(String() args) {
       // Generate orders.
       List orders = generateOrders(10000);
      
       // Filter orders.
       List highValueCompletedOrders = filterHighValueCompletedOrders(orders);
      
       // Create a mutable summary map.
       Map summaryReport = new HashMap<>();
       summaryReport.put("count", 0);
       summaryReport.put("total_amount", 0.0);
      
       // Update the summary report.
       updateSummary(summaryReport, highValueCompletedOrders);
      
       System.out.println("Summary Report for High-Value Completed Orders:");
       System.out.println(summaryReport);
   }
}

Conclusion

Equipped with dynamic typing characteristics, list comprehensions, generators and their approach to the passage of arguments and mutability, Python makes a simplified codification while improving the efficiency and performance of memory. As a result, Python has become the ideal programming language for self -predicating.

Thanks for reading!