Mastering Python

1. When we say that a function takes O(1) time, it means that it generally only takes 1 step to execute.
2. Similarly, a function with O(n) would take n steps to execute, where n is generally the size of the object.
3. A set is a structure that uses the hash method to get a unique collection of values.
4. ChainMap allows you to combine multiple mappings (dictionaries for example) into one.
5. OrderdDict is a dict that keeps track of the order in which the items were inserted. Whereas a normal dict will return your keys in the order of hash, OrderedDict will return your keys by the order of insertion.
6. The lambda statement in Python is simply an anonymous function.
7. The reduce function implements a mathematical technique called fold. It basically applies a function to the rst and second elements, uses that result to apply together with the third element, and continues until the list is exhausted.
8. The dropwhile function will drop all results until a given predicate evaluates to true.
9. Decorators are essentially function/class wrappers that can be used to modify the input, output, or even the function/class itself before executing it.
10. wrapping a function creates a new function and makes it harder to reach the inner function and its properties
11. Essentially, a decorator is nothing more than a function or class wrapper.
12. Whenever you are writing a decorator, always be sure to add functools.wraps to wrap the inner function. Without wrapping it, you will lose all properties from the original function, which can lead to confusion.
13. a function is just a callable object and a class can implement the callable interface as well
14. The classmethod passes a class object instead of a class instance (self), and staticmethod skips both the class and the instance entirely. This effectively makes staticmethod very similar to a regular function outside of a class.
15. property allows you to add getters/setters to existing instance properties so that you can add validators and modify your values before setting them to your instance properties. The property decorator can be used both as an assignment and as a decorator.
16. A generator is a speci c type of iterator that generates values through a function. While traditional methods build and return a list of items, a generator will simply yield every value separately at the moment when they are requested by the caller.
17. Coroutines are functions that allow for multitasking without requiring multiple threads or processes. Whereas generators can only yield values to the caller, coroutines actually receive values from the caller while it is still running.
18. A generator is a function that returns elements one at a time instead of returning a collection of items.
19. Generator comprehensions are pretty much identical to list comprehensions but use parentheses instead of brackets
20. The biggest difference between the class and the function-based approach is that you are required to raise a StopIteration explicitly instead of just returning it.
21. The results are available only once. After processing the results of a generator, it cannot be used again.
22. Generators are not indexable, which means that some_generator[5] will not work.
23. The tee program allows you to write outputs to both the screen and a le, so you can store an output while still maintaining a live view of it.
24. Coroutines are subroutines that offer non-pre-emptive multitasking through multiple entry points. The basic premise is that coroutines allow two functions to communicate with each other while running.
25. asyncio.sleep combined with the await statement actually tells Python that it should just continue with a task that needs actual processing at this time.
26. asyncio.sleep: This is the asynchronous version of time.sleep. The big difference between these two is that time.sleep will keep the Python process busy while it is sleeping, whereas asyncio.sleep will allow switching to a different task within the event loop.
27. The asyncio.Future class is essentially a promise of a result; it returns the results if they are available, and once it receives results, it will pass them along to all the registered callbacks. It maintains a state variable internally, which allows an outside party to mark a future as canceled.
28. a metaclass is a class containing meta information about a class.
29. • The big difference between multithreading and multiprocessing is that with multithreading everything is still executed within a single process. That effectively limits your performance to a single CPU core. It actually limits you even further because the code has to deal with the GIL limitations of CPython.
30. whenever possible don’t transfer any data, don’t share anything, and keep everything local.
31. The ctypes library makes it easily possible to call functions from C libraries, but you do need to be careful with memory access and data types.
32. Pip install package