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Jul 26, 2019 · x, y = y, x. This approach is much quicker and cleaner than: temp = x x = y y = temp. 9. Avoid global variables. Using few global variables is an effective design pattern because it helps you keep track of scope and unnecessary memory usage. Also, Python is faster retrieving a local variable than a global one.
- Learning Python
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- Learning Python
- Tips For Optimizing Code Performance and Speed
- Using Advanced Features Such as Decorators, Generators, and Metaclasses
- Techniques For Debugging and Error Handling
- Best Practices For Writing Clean and Readable Code
- Using Advanced Data Structures Such as Sets, Dictionaries, and Tuples
- Using Built-In Libraries For Data Analysis and Manipulation
- Tips For Working with Large Datasets and Memory Management
- Techniques For Creating and Using Modules and Packages
- Using Object-Oriented Programming Concepts in Python
- Advanced Techniques For Working with Strings, Numbers, and Other Data Types
Use built-in functions and libraries: Python has a lot of built-in functions and libraries that are highly optimized and can save you a lot of time and resources.Avoid using global variables: Global variables can slow down your code, as they can be accessed from anywhere in the program. Instead, use local variables whenever possible.Use list comprehensions instead of for loops: List comprehensions are faster than for loops because they are more concise and perform the same operations in fewer lines of code.Avoid using recursion:Recursive functions can slow down your code because they take up a lot of memory. Instead, use iteration.Decorators:Decorators are a way to modify the behavior of a function or class. They are typically used to add functionality, such as logging or memoization, without changing the underlying code.Generators:Generators are a way to create iterators in Python. They allow you to iterate over large data sets without loading the entire data set into memory. This can be useful for tasks like read...Metaclasses:Metaclasses are a way to create classes that can be used to create other classes. They can be used to define custom behavior for classes, such as adding methods or properties. They can...Coroutines:Coroutines are a way to create concurrent and asynchronous code in Python. They allow you to perform multiple tasks simultaneously, and they can be used to create simple, lightweight thr...Use the built-in Python debugger (pdb): The built-in Python debugger is a powerful tool that allows you to step through your code line by line, examine variables, and set breakpoints.Use print statements: Adding print statements to your code can help you identify the source of the problem by providing a clear picture of the program’s execution flow and variable values.Use a linter: A linter is a tool that checks your code for syntax errors and potential bugs. It can help you catch errors before you run your code.Use a unit testing framework: Unit testing allows you to test small pieces of your code individually, making it easier to pinpoint the source of any errors.Use meaningful variable and function names: Use clear, descriptive names for variables and functions that accurately reflect their purpose and usage.Use whitespace and indentation: Use whitespace and indentation consistently to separate code blocks and make the structure of your code clear.Use comments: Use comments to explain the purpose of your code and any non-obvious parts of it.Keep lines short: Limit the length of your lines of code to around 80 characters, this makes it easier to read the code on different devices and screens.Python provides several advanced data structures that can be used to store and manipulate data in powerful and efficient ways. These data structures include sets, dictionaries, and tuples. 1. Sets: A set is an unordered collection of unique elements. Sets are commonly used for membership testing, removing duplicates from a list, and mathematical op...
Python has a vast ecosystem of built-in libraries that can be used for data analysis and manipulation. These libraries include: 1. NumPy: NumPy is a library for working with large arrays and matrices of numerical data. It provides functions for performing mathematical operations on these arrays, such as linear algebra, Fourier transforms, and stati...
Working with large datasets can be a challenging task, and it requires proper memory management to avoid running out of memory and to ensure the code runs efficiently. Here are some tips for working with large datasets and managing memory: 1. Use memory-efficient data structures:When working with large datasets, it’s important to use memory-efficie...
Modules and packages are a way to organize and reuse code in Python. They can be used to group related functions, classes, and variables together, and to make them available for use in other parts of the program. Here are some techniques for creating and using modules and packages: 1. Create modules: A module is a single Python file that contains P...
Object-oriented programming (OOP)is a programming paradigm that is based on the concept of objects, which are instances of classes. OOP allows you to model real-world concepts in your code, making it more organized, reusable, and maintainable. Here are some techniques for using object-oriented programming concepts in Python: 1. Create classes: In P...
Python provides a wide range of built-in functions and methods for working with strings, numbers, and other data types. Here are some advanced techniques for working with these data types: 1. String formatting: Python provides advanced string formatting techniques using the format() method and f-strings. These techniques allow you to insert dynamic...
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- Choose wisely between "Ask for Forgiveness" and "Look before you leap" Ask for forgiveness: You run your code like normal, then wrap them in the try/catch block.
- How to properly filter a list: There are 3 popular ways to filter a list in Python: For Loop: This is the basic way to get your job done, it's easy to read and understand.
- Checking for True or False. We have 3 ways to achieve this: if var == True: This is bad, ~120% slower than the winner. if var is True: This is also bad, ~60% slower than the winner.
- Use in to check if item exists in a collections of item. in in a powerful tool to check if an item is in a list/set/tuple or a dictionary (use in would only check if a Key exists in a dictionary).
Oct 28, 2022 · Python’s built-in functions are one of the best ways to speed up your code. You must use built-in python functions whenever needed. These built-in functions are well tested and optimized. The reason these built-in functions are fast is that python’s built-in functions, such as min, max, all, map, etc., are implemented in the C language.
Feb 17, 2024 · Applying Optimization Algorithms in Python Code. Optimization algorithms can significantly improve the performance of Python code by systematically searching for the most efficient solutions. This section provides an introduction to using optimization algorithms in Python and walks through an example implementation.
Mar 31, 2024 · Python’s built-in functions and libraries are designed for high performance. For data-heavy tasks, leveraging libraries like NumPy and Pandas can lead to significant speed improvements. Built-in ...
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Oct 4, 2023 · There are other commonly suggested tips and tricks to improve Python performance, such as the following: Avoid dot notation, including Math.sqrt () or myObj.foo (). Use string manipulation, such as the join () method. Employ multiple assignments, for example a,b = 1,2. Use the @functools.lru_cache decorator.
