Functional programming in Python

Are there any good books on the above? Or good write ups on different ways to do this. Preferably from a beginner FP point of view.

Async/Await: Switching from synchronous to asynchronous programming can significantly improve performance for I/O-bound tasks. "For API development utilizing async has to be at the top for me. Switching from synchronous to asynchronous has been a game changer for me..."
Libraries: Utilize libraries that support asynchronous operations, such as taskiq over celery for long tasks.

NumPy and Pandas: Use these libraries for data manipulation to avoid slow Python loops. "Are you vectorizing as much as possible (using pandas - or polars which is even faster)?"
Polars: A faster alternative to Pandas for dataframes. "When I switched from Pandas to Polars, my CPU usage went from around 20% to 80%, with a big speed increase."

Profiling Tools: Use Python's built-in profiling tools or external libraries like py-spy and pyinstrument to identify bottlenecks. "You can use profiling tools in Python to see where your code's slowing down."
Benchmarking: Regularly benchmark different parts of your code to track performance improvements. "Life's easier when you understand how things affect your benchmarks."

Data Caching: Cache frequently accessed data to reduce the need for repeated I/O operations. "Is the data fresh every single time? If not, and a third of it's for validation, can you save the validated/preprocessed NumPy arrays to a file?"
Efficient Data Formats: Use efficient data formats like Parquet instead of Excel for faster data loading. "Reading from Excel is pretty slow in my experience, so if you can avoid it or only do it once, that'd be best."

Numba: Use Numba to compile Python code to machine code for significant speed improvements. "numba"
PyPy: A just-in-time (JIT) compiler for Python that can offer substantial speedups. "PyPy gave us roughly an order of magnitude speedup over CPython."

Docstrings: Write docstrings for every non-trivial function or method. "At minimum write a docstring for every non-trivial function/method."
Inline Comments: Document your code as you go, not after. "Document your code as you go, not after."

Plan Before Coding: Make a plan before diving into coding to ensure a well-structured approach. "Make a plan before diving into the coding."
Modular Code: Break down problems into smaller, manageable chunks and use modules to organize your code. "His approach really drilled home to me how valuable it is to continually ask yourself 'Are any of my functions doing more than one thing?'"

Unit Testing: Learn to write unit tests to ensure your code works as expected and to catch bugs early. "I wish I learned git and unit testing earlier."
Debugger Usage: Use a debugger instead of print statements to inspect variable values and execution flow. "Debugger >>> print"

Type Hinting: Use type hints for function arguments, keyword arguments, and return values to improve code readability and maintainability. "Typing didn't exist when I learned Python, but use typing everywhere."

Use Venvs: Always use virtual environments to manage dependencies and avoid version conflicts. "Using virtual environments."

Git/GitHub: Internalize the git/version control mindset early on. "I wish I had internalized the git/version control mindset earlier."

Fluent Python: A highly recommended book for understanding the intricacies of Python. "Fluent Python has been one of my personal favorite books for learning the intricacies of the language..."
Raymond Hettinger Talks: Watch talks by experienced Python developers for insights into best practices. "Check out some of the talks by Raymond Hettinger."

These best practices and tips should help you optimize your Python code for better performance and readability. Happy coding!

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