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1. Tips for optimizing code performance and speed

Use built-in functions and libraries : Python has many highly optimized built-in functions and libraries, which can save a lot of time and resources;
Avoid using global variables : Global variables can be accessed from anywhere in the program and may slow down the code, so use local variables whenever possible;
Use list comprehensions : This is also often said in my previous tutorials, list comprehensions are faster and more concise than for loops, and perform the same operation with fewer lines of code;
Avoid using recursion : recursive functions can slow down your code because they take up a lot of memory;
Use NumPy and SciPy : these can greatly help optimize code for scientific and mathematical computing;
Use Cython to speed up key parts of the code, which can be compiled into C to make it faster;
Use "vectorized operations" and "broadcasting" when performing calculations ;
Use multi-process, multi-thread or asynchronous to utilize multiple CPUs to run multiple tasks at the same time ;

2. Use advanced features like decorators, generators, and metaclasses

Decorator: A decorator is a way to modify the behavior of a function or class. Usually used to add functionality without changing the underlying code , such as logging or memoization (old stereotype);
Generators: Generators are a way of creating iterators in Python. Allows iterating over large datasets without loading the entire dataset into memory , useful for tasks like reading large files or processing large amounts of data
Metaclass: A metaclass is a way of creating classes that can be used to create other classes. Can be used to define custom behavior for a class, such as adding methods or properties. It can also be used to create metaprogramming, which allows writing code that generates other code;
Coroutines: Coroutines are a way to create concurrent and asynchronous code in Python. Allows multiple tasks to be executed simultaneously, and they can be used to create simple, lightweight threads;
Function annotations: Function annotations are a way to add metadata to functions, which can be used to provide more information about function parameters, return values, and types, and can also be used to specify the types of function parameters and return values ;
Context Manager: A context manager is a way to handle resources (commonly such as files) in a safe and efficient manner. Allows defining the context in which resources are used, and automatically handles opening and closing of resources ;
Enumeration: An enumeration is a way to define a set of named values that can be used as a replacement for integers and strings. Created using the Enum class;
Namedtuples: Namedtuples are a subclass of tuples with named fields that can be accessed by name rather than by index. Created using the namedtuple function

3. Some tips for debugging and handling errors

Use the built-in Python debugger (pdb) : The built-in Python debugger is a powerful tool that allows stepping through code, inspecting variables and setting breakpoints;
Use the print statement (hahaha I like to use in many scenarios): add the print statement to the code to help determine the root cause of the problem by providing the program execution process and printing of variable values;
Use a linter: A linter is a tool that checks your code for syntax errors and potential errors. Errors can be caught before running the code;
Use a unit testing framework : unit testing allows a small piece of code to be tested in isolation, making it easier to pinpoint the source of errors;
Using a logging library : A logging library records information about program execution, such as variable values and execution flow. Useful for tracking down infrequent bugs or understanding a program's behavior over time;
Use try-except blocks : Try-except blocks can help write robust and fault-tolerant code by catching errors and providing an alternative execution flow to handle errors gracefully;
Use assert statement : An assert statement checks if a given condition is true and throws an exception if it is false. Used to check the integrity of input and can be used as a debugging aid;
Use the log module : the log module records messages of different levels, which can be used to record debug, information, warning, error and critical messages;
Use the traceback module : the traceback module extracts the stack trace of the exception, which is useful for understanding the cause of the error and locating the fault point in the code;
Use the bug tracking system: the bug tracking system logs, tracks and manages bugs, and tracks the progress of bug fixes;

4. Tips for writing clean and readable code

Use meaningful variable and function names : Use clear, descriptive names for variables and functions that accurately reflect their purpose and purpose (this is so important I used to scribble in school)
Use whitespace and indentation : use whitespace and indentation consistently to separate code blocks and to make code structure clear;
Use comments : Use comments to explain the purpose of the code and any unclear parts of the code;
Keep single lines short : limit the length of lines of code to around 80 characters
Use a unified naming convention : for example, use snake_case for variable names and CamelCase for class names.
Keep the function body small and focus on a certain task : make it easier to reuse and easier to understand, the main function is famously written in hundreds of lines
Use docstrings (docstrings) : Document purpose, parameters, return function and class value;
Follow the PEP-8 style guide: This guideline provides guidelines for writing clean and readable Python code. Covers indentation, naming conventions, whitespace, and more. Following these guidelines will make the code more consistent and easier for others to read;

5. Use advanced data structures such as sets, dictionaries, and tuples

Python provides several high-level data structures that can be used to store and manipulate data in a powerful and efficient manner. These data structures include sets, dictionaries, and tuples.

Set: A set is an unordered collection of unique elements. Sets are commonly used for membership tests, removing duplicates from lists, and mathematical operations like intersection and union . Use curly braces {} or the set() constructor definition. For example my_set = {1, 2, 3, 4}
Dictionary: A dictionary is an unordered collection of key-value pairs. Dictionaries are commonly used for lookups, counting, and sorting . Defined using curly braces {}, keys and values are separated by colons. For example my_dict = {'geeks': 1, 'for': 2, 'geeks': 3}
Tuple: A tuple is an ordered collection of elements. Tuples are similar to lists, but are immutable, meaning their elements cannot be modified once created. Use parentheses () or tuple() constructor definitions. For example my_tuple = (1, 2, 3, 4)

These data structures can be used in a variety of ways to solve different problems. For example, you can use sets to quickly check whether an element already exists in a dataset, use dictionaries to efficiently store and retrieve data, and use tuples to combine multiple values and use them as a single entity.
The important thing is that each data structure has its own advantages and disadvantages. Choosing the correct data structure for a specific task can greatly improve the performance and readability of the code , which are summarized in the previous articles.

6. Tips for working with large data sets and memory management

Working with large datasets is a challenging task that requires proper memory management to avoid running out of memory and ensure that the code runs efficiently. Here are some tips for working with large datasets and managing memory:

Use memory-efficient data structures : for example, use the more memory-efficient NumPy instead of Python's built-in list data structure;
Use data sampling: When working with large datasets, it is often useful to first process smaller subsets of the data. done using techniques such as random sampling, which help reduce the amount of memory required to load and process data;
Use Lazy Loading: Lazy loading is a technique that loads data into memory only when needed, rather than loading the entire dataset at once;
Using Iterators and Generators : Iterators and Generators are a way to process large datasets without loading the entire dataset into memory at once, allowing data to be processed one at a time;
Use disk-based storage : When working with large datasets that cannot fit in memory, it is often useful to store data on disk, popular libraries such as HDF5 and Parquet allow large datasets to be stored on disk and accessed in a memory-efficient manner ;
Monitor memory usage: Regularly monitoring your program's memory usage can help identify and fix memory leaks and optimize your program's memory usage. Python provides libraries such as memory_profiler and psutil to monitor memory usage;

7. Techniques for working with strings, numbers, and other data types

String formatting : Python provides advanced string formatting techniques using the format() method and f-strings. Allows dynamic values to be inserted into strings and makes them more readable. For example, use "My name is {}".format("John") to insert the value "John" into the string;
Regular Expressions: Python provides a module called re for working with regular expressions. Regular expressions are powerful tools for searching, matching and manipulating strings;
String Methods : Python provides various string methods that can be used to manipulate strings. Including but not limited to .strip() to remove spaces from the beginning and end of a string, .split() to split a string into a list of substrings, and .replace() to replace a specific substring;
Number formatting : Python also provides advanced number formatting techniques using the format() method and f-strings, similar to what is done on strings, allowing control over the number of decimal places, the presence of thousands separators, and other formatting options;
Type conversion: Python provides functions such as int(), float(), and str() that can be used to convert one data type to another. This is useful when working with different types of data;
Decimal precision : Python's decimal module provides a Decimal class that can be used for high-precision decimal operations. Helpful for financial and monetary calculations where floating point precision may not be sufficient;
Advanced Mathematical Operations: Python provides a math module that provides advanced mathematical functions such as trigonometric functions, logarithms, exponentials, etc. Also mention NumPy, the library also provides efficient implementation of these operations, such as matrix operations.

The above are the current skills to improve the level of Python programming. If you have any mistakes, please point them out and look forward to growing up with you~

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These seven tips to improve the performance of Python code by 100% must be known

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