Why should Python not be your first language?
Tags: python, datastructures, java, coding
Published: 2024-02-14 06:49:32


While Python is the most hot topic these days, either in web development (not that much), data science, or AI/ML,. Python provides rich libraries for such applications and is kind of cool.There are certain aspects where Python may not be the best choice as a first language when compared to Java or C++.I have discusssed 10 points regarding that.

 

1. **Data Type Declarations:**
Variables can be declared without specifying their data type in Python. Dynamic typing offers flexibility and convenience, but it can sometimes lead to challenges related to understanding data types and their limitations.Dynamically typed languages provide less type safety compared to statically typed languages like Java. In Python, you can assign values of different types to the same variable without explicit declarations or checks.
In Python, variable x need not be defined explicitly but in a language like Java or C++.
```
int x ;
float x;
x=10.2 //correct
x=10  // will be wrong declaration

```
While in Python 3.It fails in some constraints,might give errors or not suitable  to set up datatype constraints.
```
x =10 #be treated as int
x=10.2 #treated as float
```
 Beginners learning programming with dynamically typed languages should pay attention to understanding data types and their limitations to write robust and maintainable code. Additionally, exploring statically typed languages can provide valuable insights into type systems and their role in software development.

2.**Basic understanding of data structure** Python's built-in data structures, such as lists, dictionaries, tuples, and sets, are powerful and versatile. 
However, Python's built-in data structures may be perceived as limited compared to other programming languages like Java or C++, which offer a wider range of built-in data structures such as arrays, linked lists, stacks, queues, and trees.
Beginners who want to learn and understand these advanced data structures may need to implement them from scratch or use third-party libraries, which can add complexity to the learning process.The availability of comprehensive educational resources for understanding more advanced data structures and algorithms in Python may vary compared to other languages.

3.**Less Exposure to Low-Level Concepts:**

Python abstracts many low-level details, making it easy for beginners to start coding without understanding some fundamental concepts such as memory management or pointers. While this abstraction is beneficial for beginners initially, it may limit their understanding of how computers work at a lower level.
In languages like C and C++, pointers are fundamental concepts that allow programmers to directly manipulate memory addresses. While Python does not have direct support for pointers, understanding them in languages like C or C++ can provide valuable insights into memory management and data storage.
Pointers facilitate efficient data storage by enabling dynamic memory allocation and manipulation. For example, pointers can be used to create dynamic data structures like linked lists, trees, and graphs, where memory allocation is not predetermined at compile time. Understanding pointers helps beginners grasp how data is stored and accessed in memory, enhancing their comprehension of data structure implementations.

```
#include <stdio.h>

int main() {
    int x = 10;   // integer variable
    int *ptr;     // pointer to an integer

    ptr = &x;     // pointer points to the address of x

    printf("Value of x: %d\n", x);     // output: Value of x: 10
    printf("Address of x: %p\n", &x);  // output: Address of x: <memory address>
    printf("Value of x using pointer: %d\n", *ptr);  // output: Value of x using pointer: 10

    return 0;
}

```
In this example, ptr is a pointer variable that stores the address of the integer variable x. By dereferencing ptr with *ptr, we can access the value stored in x. This example demonstrates how pointers are used to manipulate memory addresses and access data stored in memory.

4.**Transition to Other Languages:**


If beginners plan to transition to languages with different syntax or paradigms later on, starting with Python may lead to challenges in adapting to those languages. Learning a language with a more rigid syntax upfront may provide a stronger foundation for learning other languages in the future.This is the  most common problem faced by people.


 5.**Indentation Sensitivity:**

Python's use of indentation for block structure can be confusing for beginners who are used to languages with explicit block delimiters like braces. While indentation can improve code readability, it may also lead to errors if not used consistently.Other languages use brackets to define blocks,sometime makes less complex to understand.
In Python

```
for i in range(0,100):
print(i) #this will give indentation error
  print(i) #corect output.

```
In Java

```
for(int i =0 ;i<100;i++){
//in this block we can write anywhere related to for statement without caring indentation.
}
```

 6.**Performance Limitations:**

Python is an interpreted language, which generally means it is slower than compiled languages like C++ or Java. While Python's performance is usually sufficient for most applications, it may not be the best choice for performance-critical tasks or projects where speed is crucial.
As you can see from the figure, Java and Python behave differently with the same logic.
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/n1bhcc17ekjhjhewnbbc.png)
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/hc76rcwxjn4hiy1ianhy.png)

7.**Reliance on Built-in Functions:**


Python's extensive library of built-in functions and methods for data structures can lead to an overreliance on these functions without understanding the underlying principles.
Beginners may use built-in functions without fully understanding how they operate, hindering their comprehension of data structure algorithms and implementations.
Built-in functions like sort() simplify tasks but understanding their workings is crucial for beginners.

Certainly! Here's a more concise version:

Built-in functions like sort() simplify tasks, but understanding their workings is crucial for beginners.

Example: Using sort() to sort a list:

```
numbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3]
numbers.sort()
print("Sorted list:", numbers)
```
Understanding sorting algorithms enhances problem-solving skills.Learning sorting algorithms exposes beginners to key algorithmic concepts.The hands-on practice with sorting algorithms reinforces learning and practical experience.

Python boasts an abundance of built-in functions, unlike other languages such as C++ and Java, which also offer their own sets of built-in functions.

8.**Influence of Python's Ecosystem:**

Python's ecosystem, including popular libraries and frameworks, may influence beginners to prioritise application development and higher-level programming paradigms over understanding low-level data structure concepts.
Beginners may gravitate towards using libraries and frameworks that abstract away data structure implementations, limiting their exposure to underlying data structure principle

In Python, beginners may rely on libraries like Pandas,numpy etc for data manipulation, abstracting away data structure details. In contrast, in Java, beginners are more likely to directly engage with data structure implementations, fostering a deeper understanding of underlying principles.


9.**Limited Exposure to Algorithmic Complexity:**


While Python provides implementations of basic data structures, it may not expose beginners to the complexities of data structure algorithms and their computational efficiency.
Beginners may not fully appreciate concepts like time complexity and space complexity without delving into more advanced data structure and algorithm topics.
In Java, beginners are more likely to encounter explicit implementations of data structures and algorithms compared to Python. Let's illustrate this with a comparison:

```
# Python code for linear search
def linear_search(arr, target):
    for i in range(len(arr)):
        if arr[i] == target:
            return i
    return -1

# Example usage
my_list = [4, 7, 2, 1, 9, 6]
target_element = 9
result = linear_search(my_list, target_element)
if result != -1:
    print(f"Element {target_element} found at index {result}.")
else:
    print(f"Element {target_element} not found in the list.")

```
In Java

```
// Java code for linear search
public class LinearSearch {
    public static int linearSearch(int[] arr, int target) {
        for (int i = 0; i < arr.length; i++) {
            if (arr[i] == target) {
                return i;
            }
        }
        return -1;
    }

    public static void main(String[] args) {
        int[] myArray = {4, 7, 2, 1, 9, 6};
        int targetElement = 9;
        int result = linearSearch(myArray, targetElement);
        if (result != -1) {
            System.out.println("Element " + targetElement + " found at index " + result + ".");
        } else {
            System.out.println("Element " + targetElement + " not found in the array.");
        }
    }
}

```
In Python, the linear search algorithm is implemented using a function, abstracting away some details, making it easier for beginners to start coding without worrying about lower-level concepts.

In Java, the linear search is implemented in a class with a main method, requiring beginners to understand classes and methods from the start. Java's explicit approach provides a deeper understanding of core concepts like data structures and algorithms.
10. **Less Emphasis on Performance Optimisation:**


Python's focus on simplicity and readability may lead beginners to prioritize writing clear and concise code over optimizing performance.
Beginners may overlook opportunities for performance optimization in data structure implementations, such as using more efficient algorithms or data structures tailored to specific use cases.

In Python, the focus on simplicity and readability may lead beginners to prioritize writing clear and concise code over optimizing performance. This is exemplified by the following Python code snippet for finding the factorial of a number:

```
def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)

result = factorial(5)
print(result)  # Output: 120

```
While this Python code is concise and easy to understand, it may not be the most efficient implementation for calculating factorials, especially for large values of n.

On the other hand, in languages like Java, beginners are often introduced to concepts like performance optimization early on. Here's an example of a Java code snippet for calculating the factorial of a number using iteration instead of recursion:

```
public class Factorial {
    public static int factorial(int n) {
        int result = 1;
        for (int i = 1; i <= n; i++) {
            result *= i;
        }
        return result;
    }

    public static void main(String[] args) {
        int result = factorial(5);
        System.out.println(result);  // Output: 120
    }
}

```
n this Java code, the factorial is calculated using a simple loop, which can be more efficient for larger values of n compared to the recursive approach used in the Python code.

## Summary

As a newcomer, I initially felt confident in my decision to start with Python, but over time, I encountered challenges that made me reconsider. While Python's simplicity and readability are beneficial, its limited exposure to low-level concepts and performance optimisation may hinder a beginner's understanding. Comparing Python with Java or C++ highlights these drawbacks for beginners.I gave comparision with Java but all these concepts are applicable for C++too.
Although for experience, Python's extensive ecosystem, dynamic typing, and automatic memory management make it versatile and accessible, supporting various applications from web development to data science and machine learning. Despite its limitations, Python will be my favourite language.