**Advanced Use of Python Tutorial**

This comprehensive tutorial will guide you through the advanced features and applications of Python programming language.

Table of Contents

1. Working with Modules
2. Object-Oriented Programming (OOP) Concepts
3. File Input/Output and Persistence
4. Regular Expressions and Text Processing
5. Data Structures: Lists, Tuples, Dictionaries, Sets
6. Advanced Data Types: Numpy, Pandas
7. Multithreading and Concurrency
8. Decorators and Metaclasses

Working with Modules

Python modules are reusable code that can be imported into your Python program to extend its functionality.

Example: Importing the math Module

import math

print(math.pi)  # Output: 3.141592653589793

Modules can also be imported as specific functions or classes, rather than the entire module.

Example: Importing the sqrt Function from the math Module

from math import sqrt

print(sqrt(16))  # Output: 4.0

Object-Oriented Programming (OOP) Concepts

Python supports object-oriented programming through its classes and instances.

Example: Defining a Simple Class

class Car:
    def __init__(self, brand, model):
        self.brand = brand
        self.model = model

my_car = Car("Toyota", "Camry")
print(my_car.brand)  # Output: Toyota
print(my_car.model)  # Output: Camry

File Input/Output and Persistence

Python provides various ways to read and write data from files.

Example: Reading a Text File

with open("example.txt", "r") as file:
    contents = file.read()
print(contents)

Example: Writing Data to a JSON File

import json

data = {"name": "John", "age": 30}
with open("person.json", "w") as file:
    json.dump(data, file)

Regular Expressions and Text Processing

Python's re module provides support for regular expressions.

Example: Finding All Occurrences of a Pattern

import re

text = "Hello world! Hello again!"
pattern = r"Hello"
matches = re.findall(pattern, text)
print(matches)  # Output: ['Hello', 'Hello']

Data Structures: Lists, Tuples, Dictionaries, Sets

Python has various built-in data structures that can be used to store and manipulate data.

Example: Creating a List

fruits = ["apple", "banana", "cherry"]
print(fruits[0])  # Output: apple

Example: Using the dict Class for a Dictionary

person = {"name": "John", "age": 30}
print(person["name"])  # Output: John

Advanced Data Types: Numpy, Pandas

Python's NumPy and Pandas libraries provide support for numerical and data analysis tasks.

Example: Using the numpy Library to Perform Basic Arithmetic Operations

import numpy as np

numbers = np.array([1, 2, 3, 4, 5])
result = numbers * 2
print(result)  # Output: [ 2  4  6  8 10]

Example: Using the pandas Library to Create and Manipulate DataFrames

import pandas as pd

data = {"Name": ["John", "Mary", "David"], "Age": [30, 25, 40]}
df = pd.DataFrame(data)
print(df)  # Output:
            #     Name  Age
            # 0    John   30
            # 1    Mary   25
            # 2   David   40

Multithreading and Concurrency

Python's threading module provides support for multithreading.

Example: Using the threading Module to Create Multiple Threads

import threading

def print_numbers(n):
    for i in range(1, n + 1):
        print(i)

threads = []
for i in range(5):
    thread = threading.Thread(target=print_numbers, args=(10,))
    threads.append(thread)
    thread.start()

# Wait for all threads to finish
for thread in threads:
    thread.join()

Decorators and Metaclasses

Python's decorators and metaclasses provide a way to extend the functionality of classes and functions.

Example: Using a Decorator to Log Function Calls

import functools

def log_calls(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__} with args: {args}, kwargs: {kwargs}")
        return func(*args, **kwargs)
    return wrapper

@log_calls
def add(a, b):
    return a + b

print(add(2, 3))  # Output:
# Calling add with args: (2, 3), kwargs: {}
# Output: 5

This comprehensive tutorial has covered various advanced topics in Python programming. We have explored working with modules, object-oriented programming concepts, file input/output and persistence, regular expressions and text processing, data structures, advanced data types, multithreading and concurrency, and decorators and metaclasses.

I hope this tutorial has provided you with a deeper understanding of the capabilities and applications of the Python programming language. Happy coding!