Getting Started with Python: Key Concepts and Beginner-Friendly Examples
Introduction
Python is an interpreted (i.e it does not need to be compiled before execution),object-oriented , high-level programming language known for its simplicity, readability, and versatility or flexibility.Python is also a dynamic language which means that the type of variable is not determined until it is assigned a value.
Python has a large number of libraries(Libraries are like a toolbox, adding features to existing code) and frameworks(frameworks provide a structure for building new applications) .
Python is used in web development, data science, artificial intelligence ,machine learning , game development, CAD.
In this blog, we will explore key Python concepts, differences between versions, and hands-on code examples,
These examples can be practiced using platforms like Google Colab, Spyder, or even simple editors like VB (for those transitioning from other languages).
Table of Contents
Why is Python considered a high-level programming language?
Difference between Python 2 and Python 3 .
How to check the Python version in a script?
Significance of PEP 8 in Python programming .
How does Python handle memory management?
What are Python libraries ?
A Python code snippet to swap the values of two variables without using a temporary variable .
A Python function to generate Fibonacci series up to n terms.
Conclusion
Why is Python considered a high-level programming language?
Python is a high-level programming language, meaning that it abstracts away low-level details of computer architecture and provides a more user-friendly syntax. Tasks such as memory management and hardware-specific optimizations are handled by the Python interpreter, freeing developers from such concerns.
Python is dynamically typed, Python comes with a comprehensive standard library that provides modules and packages for a wide range of tasks, from file I/O and networking to data manipulation and GUI development.
Difference between Python 2 and Python 3 .
Python 2 and Python 3 are two major versions of the Python programming language. key difference between them are:
1. Print Keyword:
The print statement in Python 2 was replaced by the print() function in Python 3.
2.Division of Integers: In Python 2, dividing two integers would perform integer division (truncating the result to an integer),
while in Python 3, it performs true division (returning a float if necessary).
3.Storage of Strings: Unicode support was improved in Python 3, making it the default string type i.e strings are stored as UNICODE by default, whereas in Python 2, strings were ASCII by default.
4.Iteration: Python 2 had both the range() function and the xrange() function for generating ranges of numbers.
In Python 3, the xrange() function was removed, and the range() function behaves like xrange() from Python 2.
5.Iterators and Generators: Python 3 introduced improvements to iterators and generators, making them more efficient and powerful compared to Python 2.
6.Exceptions: In Python 2, exceptions are enclosed in notations.
In Python 3, exceptions are enclosed in parentheses.
How to check the Python version in a script ?
We can check the Python version within a script using the sys module, which provides access to some variables used or maintained by the Python interpreter, including the version information.
CODE TO WRITE IS:
import sys
print("Python version:", sys.version) # Prints the Python version
Significance of PEP 8 in Python programming .
PEP 8 is the official style guide for Python code. PEP stands for Python Enhancement Proposal, and PEP 8 specifically focuses on how to write Python code that is readable, maintainable, and consistent. It provides guidelines and best practices for formatting code, naming conventions, and code layout.
How does Python handle memory management ?
Python uses a combination of strategies for memory management, primarily relying on garbage collection and automatic memory allocation.
Automatic Memory Allocation:
Python manages memory allocation automatically for objects or variables created during program execution.
Reference Counting:
Python uses a simple technique called reference counting to keep track of the number of references to an object. Every object in Python has a reference count, and when an object's reference count drops to zero, meaning no variables or objects are referring to it, Python's garbage collector deallocates the memory associated with that object.
Garbage Collection:
Python also employs garbage collection to deallocate memory that is no longer in use. The garbage collector periodically runs in the background to identify and free up memory occupied by objects that are no longer needed.
Memory Pools:
Python uses memory pools to manage memory allocation efficiently. Memory pools are pre-allocated blocks of memory used for small objects of uniform size. Python allocates memory for small objects from these memory pools, reducing the overhead of memory allocation and deallocation.
What are Python libraries ?
In Python, a library is a collection of pre-written code and functions that can be imported and used in our programs to perform specific tasks without having to implement the functionality from scratch.
few commonly used Python libraries are:
NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow , PyTorch, Django , Flask.
A Python code snippet to swap the values of two variables without using a temporary variable .
SCRIPT / CODE :
# Original values
x = 5
y = 10
# Swapping without using a temporary variable
x, y = y, x
# Updated values after swapping
print("x after swapping:", x)
print("y after swapping:", y)
output is:
x after swapping: 10
y after swapping: 5
EXPLATION :
Using tuple unpacking, the values of x and y are swapped without the need for a temporary variable. When the expression y, x = x, y is executed, Python first creates a tuple (y, x) with the current values of y and x, then unpacks this tuple and assigns its elements to x and y, effectively swapping their values.
A Python function to generate Fibonacci series up to n terms.
def fibonacci(n):
fseries = [] # Initialize an empty list
# Handle special case for n = 0
if n == 0:
return fseries
# For n > 0, start the series with the first Fibonacci number
fseries.append(0)
# If n > 1, add the second Fibonacci number
if n > 1:
fseries.append(1)
# Generate Fibonacci numbers until number of terms (n) is reached
while len(fseries) < n:
# Calculate the next Fibonacci number
next_fibonacci = fseries[-1] + fseries[-2]
fseries.append(next_fibonacci)
return fseries
#EXAMPLE INPUT
n = 11
fib_series = fibonacci(n)
print(f"Fibonacci series up to {n} terms:", fib_series)
OUTPUT IS :
Fibonacci series up to 11 terms: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
Conclusion
Python remains one of the most versatile and beginner-friendly programming languages today. In this blog, we explored its high-level nature, learned how to differentiate between Python 2 and 3, understood memory management, and used real code examples to build logic and solve problems. Platforms like Google Colab, Spyder, and even traditional programming tools like VB (for comparative learning) make Python accessible and powerful for everyone.
Whether you’re just starting out or brushing up your skills, practicing these concepts and using the right tools will put you on the path to becoming a confident Python programmer.
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