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Conclusion and where to go after this

  Conclusion and Where to Go After This Congratulations on completing your Python learning journey! You've covered a wide array of topics, from the basics of syntax and data types to advanced concepts like multithreading, multiprocessing, and decorators. But learning doesn't stop here. Python is a versatile language with many specialized fields where you can apply your skills. Here are some potential paths you can explore next: Machine Learning Machine Learning (ML) is one of the most exciting fields you can dive into. Python's libraries like TensorFlow, Keras, scikit-learn, and PyTorch make it an ideal language for building ML models. You'll learn about supervised and unsupervised learning, deep learning, neural networks, and more. Start with the basics of linear regression and classification, then move on to more complex models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Data Structures and Algorithms (DSA)...
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MultiProcessing in Python

  Learning Sections          show MultiProcessing in Python Multiprocessing in Python involves using the multiprocessing module to run multiple processes concurrently, taking advantage of multiple CPU cores. This module provides a higher level of concurrency than threading and is especially useful for CPU-bound tasks. Creating Processes You can create and start a new process by using the multiprocessing module: import multiprocessing def print_numbers (): for i in range ( 10 ): print ( i ) p1 = multiprocessing.Process ( target = print_numbers ) p1 . start () p1 . join () # Wait for the process to complete Using Process Pools The multiprocessing module provides a Pool class, which allows you to manage a pool of worker processes: from multiprocessing import Pool def square ( n ): return n * n with Pool ( 4 ) as pool : result = pool.map ( square , range (...
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Multithreading in Python

  Learning Sections          show Multithreading in Python Multithreading is a programming technique used to run multiple threads (smaller units of process) concurrently within a single process. It allows for parallel execution of tasks and can significantly improve the performance of applications, especially those involving I/O-bound operations. Creating Threads You can create and start a new thread by using the threading module in Python: import threading def print_numbers (): for i in range ( 10 ): print ( i ) t1 = threading.Thread ( target = print_numbers ) t1 . start () t1 . join () # Wait for the thread to complete Using Thread Pools The concurrent.futures module provides a high-level interface for asynchronously executing callables. The ThreadPoolExecutor is particularly useful for managing a pool of threads: from concurrent.futures import ThreadPoolExecutor def ...
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AsyncIO in Python

  Learning Sections          show AsyncIO in Python AsyncIO is a library to write concurrent code using the async/await syntax. It allows you to manage asynchronous tasks and I/O operations in Python. Basics of AsyncIO AsyncIO provides an event loop, coroutines, and tasks: Event loop: The core of every asyncio application. It runs asynchronous tasks and callbacks. Coroutines: Special functions defined with async def . They use await to pause their execution and wait for other coroutines. Tasks: Used to schedule coroutines to run concurrently in the event loop. Creating a Simple Coroutine import asyncio # Define a coroutine async def say_hello (): print ( "Hello" ) await asyncio.sleep ( 1 ) print ( "World" ) # Create an event loop and run the coroutine asyncio.run ( say_hello ()) Running Multiple Coroutines import asyncio async de...
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Regular Expressions in Python

  Learning Sections          show Regular Expressions in Python Regular expressions (regex) are a powerful tool for matching patterns in text. Python provides the re module to work with regular expressions. Basic Functions in the re Module re.search(pattern, string): Searches for the first occurrence of the pattern within the string. Returns a match object if found, else None. re.match(pattern, string): Checks for a match only at the beginning of the string. Returns a match object if found, else None. re.findall(pattern, string): Returns a list of all non-overlapping matches of the pattern in the string. re.finditer(pattern, string): Returns an iterator yielding match objects over all non-overlapping matches. re.sub(pattern, repl, string): Replaces the matches with the specified replacement string. Using re.search import re # Search for a pattern wit...
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Function Caching in Python

  Learning Sections          show Function Caching in Python Function caching is a technique used to store the results of expensive function calls and reuse those results when the same inputs occur again. Python's functools module provides a built-in way to cache function results using the lru_cache decorator. Using lru_cache from functools The lru_cache decorator caches the results of a function based on its inputs. It uses a Least Recently Used (LRU) caching strategy to manage the cache size: from functools import lru_cache # Apply the lru_cache decorator @lru_cache (maxsize= 128 ) def fib ( n ): if n == 0 : return 0 elif n == 1 : return 1 else : return fib ( n - 1 ) + fib ( n - 2 ) # Call the cached function print ( fib ( 10 )) In this example, the Fibonacci function results are cached, so repeated calls with the same input are faster. ...
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Generators in Python

  Learning Sections          show Generators in Python Generators are a special type of iterator in Python that allow you to iterate over a sequence of items without storing them all in memory at once. They are useful for generating large sequences of data on-the-fly, or for processing data in a memory-efficient manner. Creating Generators In Python, generators are created using generator functions or generator expressions: # Generator function def my_generator ( n ): for i in range ( n ): yield i # Generator expression my_generator = ( i for i in range ( 10 )) A generator function uses the yield keyword to yield values one at a time, while a generator expression creates an anonymous generator. Iterating Over Generators You can iterate over the values produced by a generator using a for loop: for value in my_generator ( 5 ): print ( value ) This w...
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