PyNotes

#memory_management Title: Integer Caching in Python The Python implementation front loads an array of integers between -5 to 256. Hence, variables referring to an integer within the range would be pointing to the same object that already exists in memory Source

Title: getters and setters in Python Getters and Setters are used to ensure data encapsulation in OOP. In python they are not the same as in other languages, because private variables are not hidden in python. Getters and Setters are used to add validation for getting and setting values or to avoid direct access of a class field. 📌 get() and set() methods don't fully work as getters and setters. In this case, python has a special function property() Sources: Python cookbook and geeksforgeeks

Title: Namespaces A namespace is a system that has a unique name for each and every object in Python. An object might be a variable or a method. There are 3 types of namespaces: built-in namespaces global namespaces local namespaces e.g var1 = 5 # global namespace def some_func(): var2 = 6 # local namespace The built-in namespace is always available when Python is running. You can list all built-in namespaces with dir(__builtins__) Source

#memory_management Title: String interning To alleviate memory that can be quickly consumed by strings, Python implements string interning — A string will be interned if it is a compile-time constant, is not the production of constant folding or is not longer than 20 characters, and consists exclusively of ASCII letters, digits, or underscores. —Empty strings are interned. Source

MRO (method resolution order) list is simply a linear ordering of all the base classes

#class #methods Title: __repr__ with __str__ —When we call Class it is represented by __str__ method by default, if there is not __str__ method, __repr__() method will be called. —The __repr__() method returns the code representation of an instance, The __str__() method converts the instance to a string ! A good example is provided above source: "Python Cookbook:

Transforming and reducing data To calculate the sum of squares, do the following: nums = [1, 2, 3, 4, 5] s = sum(x * x for x in nums) instead of: s = sum((x * x for x in nums)) or s = sum([x * x for x in nums]) It introduces an extra step and creates an extra list or tuple. Source: Python cookbook

#counter #most_common Find Most Frequently repeated Items in a Sequence

Title: Remove duplicates from list, without changing items order You can do the same with set(items), but it doesn’t preserve any kind of ordering. Output will be: {1, 2, 10, 5, 9} ! This works with hashable items. Source: Python cookbook

#dicts Title: Comparing dictionaries Dictionary keys() and items() support common set operations such as unions, intersections, and differences. values() method of a dictionary does not support the set operations, therefore we cannot perform those operations with dictionary values. Source: Python Cookbook

#dicts Title: Calculating dicts

prices = {
       'ACME': 45.23,
       'AAPL': 612.78,
       'IBM': 205.55,
       'HPQ': 37.20,
       'FB': 10.75
}

Get min from dict:

min_price = min(zip(prices.values(), prices.keys())) 
# min_price is (10.75, 'FB')

Get max from dict

max_price = max(zip(prices.values(), prices.keys())) 
 # max_price is (612.78, 'AAPL')

Sort Dict: prices_sorted = sorted(zip(prices.values(), prices.keys())) 📌 be aware that zip() creates an iterator that can only be consumed once. If you call zip variable next time, it will return empty obj.

#heapq Title: heapq The most important feature of a heap is that heap[0] is always the smallest item. heapq.heappop()-pops off the first item and replaces it with the next smallest item (an operation that requires O(log N) operations where N is the size of the heap) source: Python cookbook

Dict keyword access If nested dictionary throws an error while getting item by key with get(), You can use default empty {}

sort() with sorted() the sort() function will modify the list it is called on. The sorted() function will create a new list containing a sorted version of the list it is given.

#heapq Finding the Largest or Smallest N Items The heapq module has two functions—nlargest() and nsmallest() ✍️ note that If you are simply trying to find the single smallest or largest item (N=1), it is faster to use min() and max() source: "python cookbook" 3rd edition

#SubArray, #SubSequence, #SubSet Comparing SubArray SubSequence and SubSet ✅ Subarray is contiguous sequence in an array. e.g we have an array of {1, 2, 3, 4} subarray can be: {1,2,3}, {2,3,4}, {1,2} etc. ✅ A subsequence doesn't need to be contiguous, but maintains order e.g subsequence can be: {1, 2, 4} {2, 4} {1, 3, 4} etc. ✅ A subset doesn’t need to maintain order and has non-contiguous behavior. e.g subset can be: {4, 1,3} {3, 1, 2 } etc.

#Deque_continue deque(maxlen=N) creates a fixed-sized queue. When new items are added and the queue is full, the oldest item is automatically removed. (Source: "Python Cookbook")

#Deque A deque (double-ended queue), from collections library, has the feature of adding and removing elements from either end(source). It is preferred over list in the cases where we need quicker append and pop operations from both the ends of container, as deque provides an O(1) time complexity for append and pop operations as compared to list which provides O(n) time complexity (source).

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