Python Intermediate: Enhance your Python Programming Concept

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Introduction to Python Intermediate

Python intermediate refers to the advanced programming concepts and features in Python, such as object-oriented programming, regular expressions, decorators, generators, and iterators, among others. These concepts are used to build more complex programs and solve more difficult programming problems.


The necessity of these concepts lies in the fact that they enable developers to write more efficient, maintainable, and scalable code. With object-oriented programming, for example, developers can organize their code into reusable and modular components, reducing code complexity and improving code readability. Regular expressions help developers manipulate and process text data, while decorators, generators, and iterators offer a more concise and efficient way to handle repetitive code structures.

The articles in this Python Intermediate series are listed below.


By mastering these intermediate concepts in Python, developers can create more powerful and sophisticated applications, improve their problem-solving skills, and enhance their career prospects in the software development industry.

Python intermediate topics

  1. Functions: Creating, calling, and understanding parameters and return values.
  1. Modules and Packages: Importing, writing, and reusing code across multiple projects.
  1. File Handling: Reading, writing, and manipulating files in Python.
  1. Exception Handling: Understanding and using try-except blocks, raising and handling exceptions.
  1. Regular Expressions: Using regular expressions to search, match, and manipulate strings.
  1. Object-Oriented Programming: Understanding classes, objects, inheritance, polymorphism, and encapsulation.
  1. Advanced Data-Structures: Using data structures such as lists, dictionaries, sets, and tuples more effectively.
  1. Decorators: Using and writing decorators to add additional functionality to functions and classes.
  1. Iterators and Generators: Understanding iterators, generators, and the difference between them.
  1. Debugging and Testing: Debugging Python code, writing and running tests, and using test frameworks.

Example of Modules and Packages:

First, let’s create a module named my_module.py with a function that calculates the square of a number:

def square(num):
    return num ** 2

Now, let’s create a package named my_package that contains two modules: module1.py and module2.py. In module1.py, we will import my_module and use its square function:

from my_module import square

def double_square(num):
    return 2 * square(num)

In module2.py, we will define a function that generates a list of even numbers using a list comprehension:

def get_evens(num_list):
    return [num for num in num_list if num % 2 == 0]

Finally, let’s create a __init.py__ file in the my_package directory, which tells Python that this directory is a package:

# empty __init__.py file

Now, we can use our package and its modules in our main program. Here’s an example:

Example:

from my_package.module1 import double_square
from my_package.module2 import get_evens

my_num = 5

# use functions from module1 and module2
double_squared = double_square(my_num)
even_nums = get_evens([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

print(f"The double of {my_num} squared is {double_squared}")
print(f"The even numbers in the list are {even_nums}")

Output:

The double of 5 squared is 50
The even numbers in the list are [2, 4, 6, 8, 10]

In this example, we’ve created a module named my_module that contains a function, and a package named my_package that contains two modules, module1, and module2. We imported functions from both modules in our main program and used them to perform calculations on a number and a list of numbers.

The above topics are just a rough outline, and intermediate-level tutorials can go into more depth or cover additional topics as well.

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