Python Cheat Sheet - Function

Function	Example	Description
List	l = [1, 2, 3] print(len(l)) # 3	A container data type that stores a sequence of elements. Unlike strings, lists are mutable: modification possible.
Adding elements	[1, 2, 3].append(4) # [1, 2, 3, 4] [1, 2, 3].insert(2,2) # [1, 2, 2, 3] [1, 2, 3] + [4] # [1, 2, 3, 4]	Add elements to a list with (i) append, (ii) insert, or (iii) list concatenation. The append operation is very fast.
Removal	[1, 2, 3, 4].remove(1) # [2, 3, 4]	Removing an element can be slower.
Reversing	[1, 2, 3].reverse() # [3, 2, 1]	This reverses the order of list elements.
Sorting	[2, 4, 3].sort() # [2, 3, 4]	Sorts a list. The computational complexity of sorting is O(n log n) for n list elements.
Indexing	[2, 4, 3].index(2) # index of element 4 is "0" [2, 4, 3].index(2,1) # index of element 2 after pos 1 is "1"	Finds the first occurrence of an element in the list & returns its index. Can be slow as the whole list is traversed.
Stack	stack = [3] stack.append(42) # [3, 42] stack.pop() # 42 (stack: [3]) stack.pop() # 3 (stack: [])	Python lists can be used intuitively as stack via the two list operations append() and pop().
Set	basket = {'apple', 'eggs', 'banana', 'orange'}	A set is an unordered collection of elements. Each can exist only once.
Dictionary	calories = {'apple': 52, 'banana': 89, 'choco': 546}	The dictionary is a useful data structure for storing (key, value) pairs.
Reading and writing elements	print(calories['apple'] < calories['choco']) # True calories['capu'] = 74 print(calories['banana'] < calories['capu']) # False print('apple' in calories.keys()) # True print(52 in calories.values()) # True	Read and write elements by specifying the key within the brackets. Use the keys() and values() functions to access all keys and values of the dictionary.
Dictionary Looping	for k, v in calories.items(): print(k) if v > 500 else None # 'chocolate'	You can loop over the (key, value) pairs of a dictionary with the items() method.
Membership operator	basket = {'apple', 'eggs', 'banana', 'orange'} print('eggs' in basket) # True print('mushroom' in basket) # False	Check with the 'in' keyword whether the set, list, or dictionary contains an element. Set containment is faster than list containment.
List and Set Comprehension	# List comprehension l = [i * i for i in [1, 2, 3]] # Set comprehension squares = {x**2 for x in [0,2,4] if x < 4}	List comprehension is the concise Python way to create lists. Use brackets plus an expression, followed by a for clause. Close with zero or more for or if clauses. Set comprehension is similar to list comprehension.

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