Python Advanced Functions Cheatsheet

Hira Ansari | December 10, 2023

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 Usage	Dos	Don'ts	Pros	Cons
`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` module	9%	Advanced iteration patterns	- Use for advanced iteration patterns	- Avoid for simple cases	- Provides powerful iterators	- Some functions may not be beginner-friendly
`functools` module	7%	Function manipulation and decorators	- Decorators, like `lru_cache`	- Don't overuse decorators	- Enhances function behavior	- May add complexity to simple functions
`collections` module	8%	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` module	5%	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` module	6%	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` module	5%	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` module	4%	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` module	3%	Logging and debugging	- Use for flexible and configurable logging	- Avoid excessive logging	- Customizable logging levels	- Overhead if not needed for the application
`requests` library	4%	HTTP requests and API interactions	- Make HTTP requests	- Avoid for simple URL retrievals	- Versatile and user-friendly	- Adds dependencies for basic tasks
`sqlite3` module	3%	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` module	4%	Asynchronous programming and concurrency	- Use for asynchronous programming	- Avoid for synchronous tasks	- Enables concurrent, non-blocking I/O	- Learning curve for beginners
`multiprocessing` module	3%	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` library	5%	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` library	4%	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` module	3%	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` library	4%	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` library	3%	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` library	4%	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` library	3%	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` framework	4%	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` framework	5%	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` library	4%	HTTP requests and API interactions	- Make HTTP requests	- Avoid for simple URL retrievals	- Versatile and user-friendly	- Adds dependencies for basic tasks
`pytorch` library	5%	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` library	4%	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` library	3%	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` library	2%	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` library	2%	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` library	2%	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` syntax	3%	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` module	2%	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` library	2%	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` library	2%	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` library	2%	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