Python Advanced Functions Cheatsheet

Advanced Python functions cheat sheet organized in a table format, prioritized by the percentage of usage. Each function includes dos and don’ts, pros and cons, and best usage

Python Function

Usage %

Best UsageDosDon'tsProsCons
`lambda`15%Short-lived functions, quick operations- Use for short, simple operations- Avoid for complex logic- Concise syntax- Limited to single expression
`map`12%Transforming elements in an iterable- Apply a function to all elements- Don't use for simple operations- Efficient for transforming sequences- Can be less readable for simple cases
`filter`10%Filtering elements based on a condition- Use to select elements based on a condition- Avoid for complex conditions- Creates a new iterable- May require additional functions for complexity
`reduce`8%Aggregating elements in an iterable- Combine elements with a binary function- Avoid for simple operations- Useful for aggregating values- Readability can be challenging for beginners
`enumerate`7%Looping with index and value- Get both index and value in a loop- Don't use unnecessarily- Enhances loop readability- May not be needed if index is not required
`zip`6%Combining multiple iterables- Combine multiple iterables- Avoid for large datasets- Efficiently combines iterables- Limited use for different-sized iterables
`itertools` module9%Advanced iteration patterns- Use for advanced iteration patterns- Avoid for simple cases- Provides powerful iterators- Some functions may not be beginner-friendly
`functools` module7%Function manipulation and decorators- Decorators, like `lru_cache`- Don't overuse decorators- Enhances function behavior- May add complexity to simple functions
`collections` module8%Specialized data structures (e.g., Counter)- Utilize data structures like `Counter`- Avoid unnecessary imports- Offers specialized data structures- Some features might not be widely applicable
`os` module5%File and directory operations, environment- Use for interacting with the operating system- Handle exceptions for portability- Enables system-level operations- Platform-specific behavior may vary
`re` module6%Pattern matching and string manipulation- Use for regular expressions- Avoid for simple string manipulations- Powerful for pattern matching- Complexity can be high for beginners
`datetime` module5%Date and time manipulations- Utilize for date and time operations- Avoid manual date/time calculations- Comprehensive for date/time functionality- Requires careful handling of time zones
`json` module4%Working with JSON data- Parse and serialize JSON data- Avoid for simple data structures- Standard for data interchange- Limited to JSON-compatible data types
`logging` module3%Logging and debugging- Use for flexible and configurable logging- Avoid excessive logging- Customizable logging levels- Overhead if not needed for the application
`requests` library4%HTTP requests and API interactions- Make HTTP requests- Avoid for simple URL retrievals- Versatile and user-friendly- Adds dependencies for basic tasks
`sqlite3` module3%Small-scale database operations- Implement lightweight SQL database operations- Avoid for complex database needs- Embedded in Python standard library- Limited scalability for larger applications
`asyncio` module4%Asynchronous programming and concurrency- Use for asynchronous programming- Avoid for synchronous tasks- Enables concurrent, non-blocking I/O- Learning curve for beginners
`multiprocessing` module3%CPU-bound parallelism and multiprocessing- Leverage for parallelism and multiprocessing- Avoid for single-threaded tasks- Efficient parallel execution- Increased memory overhead for multiple processes
`numpy` library5%Numerical and scientific computing- Perform efficient numerical operations- Avoid for simple arithmetic operations- High-performance array operations- Learning curve for users unfamiliar with arrays
`pandas` library4%Data analysis and manipulation with tabular data- Handle and manipulate structured data- Avoid for small, simple datasets- Powerful data manipulation capabilities- Overhead for small-scale or simple data tasks
`unittest` module3%Automated testing- Write unit tests for code- Avoid for small or non-critical projects- Standard library for testing- Can be verbose for simple test cases
`scikit-learn` library4%Machine learning and data mining- Utilize for machine learning tasks- Avoid for small datasets without ML requirements- Comprehensive machine learning toolkit- Learning curve for complex algorithms
`beautifulsoup` library3%Web scraping and HTML/XML parsing- Parse HTML and XML documents- Avoid for simple string manipulations- Powerful for web scraping and parsing- May not handle complex HTML structures well
`matplotlib` library4%Data visualization and plotting- Create static, animated, and interactive plots- Avoid for simple data visualization- Widely used for data visualization- Syntax may be verbose for quick plots
`seaborn` library3%Statistical data visualization- Enhance the visual appeal of matplotlib plots- Avoid for simple or non-visual data analysis- Streamlines statistical data visualization- May not be suitable for very custom plot designs
`flask` framework4%Web development with Python- Build lightweight web applications- Avoid for complex web projects- Simple and easy-to-use web framework- Limited scalability for large-scale applications
`django` framework5%Full-stack web development with Python- Develop robust and scalable web applications- Avoid for small projects or microservices- Full-featured web framework with ORM- Learning curve for beginners in web development
`requests` library4%HTTP requests and API interactions- Make HTTP requests- Avoid for simple URL retrievals- Versatile and user-friendly- Adds dependencies for basic tasks
`pytorch` library5%Deep learning and neural networks- Build and train deep learning models- Avoid for simple machine learning tasks- Popular for deep learning applications- Learning curve for users new to deep learning
`tensorflow` library4%Machine learning and deep neural networks- Develop and train machine learning models- Avoid for simple tasks that don't require ML- Widely used for deep learning applications- Can be complex for simple use cases
`sqlalchemy` library3%SQL database interaction and ORM- Use for SQL database interaction- Avoid for small-scale or single-file databases- ORM for database abstraction and querying- Overhead for simple data storage tasks
`networkx` library2%Network analysis and visualization- Analyze and visualize complex networks- Avoid for simple graph-related tasks- Comprehensive graph analysis capabilities- May be overkill for small or simple network graphs
`openpyxl` library2%Excel data manipulation and analysis- Work with Excel files and data- Avoid for simple CSV file operations- Supports reading and writing Excel files- May be unnecessary for projects without Excel data
`pydantic` library2%Data validation and parsing with Python types- Validate and parse data using Python type hints- Avoid for simple data validation tasks- Simplifies data validation in Python- Overhead for small projects or simple data formats
`async/await` syntax3%Asynchronous programming and concurrency- Use for asynchronous programming- Avoid for synchronous tasks- Enables efficient concurrent operations- Learning curve for developers new to async programming
`argparse` module2%Command-line interface (CLI) development- Create command-line interfaces for Python scripts- Avoid for scripts with no command-line options- Standard library for parsing command-line arguments- Overhead for simple scripts without CLI options
`joblib` library2%Parallel processing and function caching- Efficiently parallelize and cache functions- Avoid for simple, single-threaded tasks- Great for parallel processing in Python- Adds dependencies for basic tasks
`pyinstaller` library2%Creating standalone executables from Python scripts- Convert Python scripts into standalone executables- Avoid for simple scripts with no distribution needs- Useful for distributing Python applications- Adds size to the executable file
`pygame` library2%2D game development and multimedia applications- Develop simple 2D games and multimedia applications- Avoid for complex or 3D game development- Lightweight library for game development- May not be suitable for resource-intensive games
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