pythontrainingbangalore

  Some of the techniques for improving Python code performance include concatenating strings with join, applying multiple assignments, using generators as keys for sorting, interning strings, and using the built-in timeit module . Optimizing Python code for performance involves several strategies to improve efficiency. Start by profiling your code to identify bottlenecks using tools like cProfile or line_profiler . Use efficient data structures such as tuples, sets, and dictionaries. Optimize loops by avoiding unnecessary calculations and using list comprehensions. Leverage built-in functions and libraries like NumPy for performance-critical tasks. Minimize the use of global variables, and prefer local variables for faster access. Use string join() for concatenation. Implement caching with functools.lru_cache and consider JIT compilation with Numba. For I/O-bound tasks, use asynchronous programming with asyncio . Avoid unnecessary object creation and consider using C exten...

  What is Manipulating Strings? Text is one of the most common forms of data your programs will handle. You already know how to concatenate two string values together with the + operator, but you can do much more than that. You can extract partial strings from string values, add or remove spacing, convert letters to lower-case or uppercase, and check that strings are formatted correctly. You can even write Python code to access the clipboard for copying and pasting text. Manipulating strings involves various operations to modify and process strings, including altering case, concatenating, slicing, searching and formatting   performing various operations on string data to transform, extract, or analyze the text. Strings are sequences of characters and are a fundamental data type in most programming languages, including Python.   Working with Strings In Python, sequences of characters are referred to as  Strings . It used in Python to record text information,...

  Some of the techniques for improving Python code performance include concatenating strings with join, applying multiple assignments, using generators as keys for sorting, interning strings, and using the built-in timeit module . Optimizing Python code for performance involves several strategies to improve efficiency. Start by profiling your code to identify bottlenecks using tools like cProfile or line_profiler . Use efficient data structures such as tuples, sets, and dictionaries. Optimize loops by avoiding unnecessary calculations and using list comprehensions. Leverage built-in functions and libraries like NumPy for performance-critical tasks. Minimize the use of global variables, and prefer local variables for faster access. Use string join() for concatenation. Implement caching with functools.lru_cache and consider JIT compilation with Numba. For I/O-bound tasks, use asynchronous programming with asyncio . Avoid unnecessary object creation and consider using C exten...

  Decorators are a powerful feature in Python that allow you to modify the behavior of a function that take another function as an argument and have wrapped inner function or a class method. They are often used to add "wrapping" functionality, such as logging, access control, memoization, and more. In more details, decorators are just functions that can use arguments of passed function and our decorator return this wrapper function In this deep dive, we'll explore what decorators are, how they work, and provide examples of their usage. What is a Decorator? A decorator is a function that takes another function and extends or alters its behavior. Decorators, functions are taken as the argument into another function and then called inside the wrapper function. Decorators are often used to abstract away repetitive tasks from the core logic of functions. Basic Structure of a Decorator A decorator is structural design pattern essentially a function that lets you att...