Data Structures Cheat Sheet, Cheat Sheet of Data Structures and Algorithms

Download Data Structures Cheat Sheet and more Cheat Sheet Data Structures and Algorithms in PDF only on Docsity! DATA STRUCTURES CHEAT SHEET Python - Data Structure It is a way of organizing data that contains the items stored and their relationship to each other The areas in which Data Structures are applied: • Compiler design • Operating system • Database Management System • Statistical Analysis Package • Numerical Analysis • Graphics • Artificial Intelligence • Simulations D a t a T y p e s T y p e s o f D a t a S t r u c t u r e s L i s t s a n d T u p l e s i n P y t h o n FURTHERMORE: Data Structures Certification Training Course Data structures can be used in the following areas: • RDBMS: Array ( Array of structure) • Network data model: Graph • Hierarchical Data model: Trees o We can use * to repeat the string for a specific number of times. Eg: x*2 o String can be sliced, that is to select parts of the string. Eg: Coke z1 = x[2:] print(z1) # Slicing z2 = y[0] + y[1] print(z2) Output: ke Co o To capitalize the strings Eg: str.capitalize('cookie') o To retrieve the length of the strings Eg: str1 = "Cake 4 U" str2 = "404" len(str1) o To replace parts of a string with another string o Eg: str1.replace('4 U', str2) • Boolean: It is a built-in data type that can take the values TRUE or FALSE Primitive Data Structures: • Integer: It is used to represent numeric data, more specifically whole numbers from negative infinity to infinity. Eg: 4, 5, -1 etc • Float: It stands for floating point number. Eg: 1.1,2.3,9.3 etc • String: It is a collection of Alphabets, words or other characters. In python it can be created by using a pair of single or double quotes for the sequence. Eg: x = 'Cake’ y = '’Cookie’’ Certain operations can be performed on a string: Non- Primitive Data Structures: • Array: It is a compact way of collecting data types where all entries must be of the same data type. Syntax of writing an array in python: import array as arr a = arr.array("I",[3,6,9]) type(a) • Linked list: List in Python is used to store collection of heterogeneous items. It is described using the square brackets [] and hold elements separated by comma Eg: x = [] # Empty list type(x) o The list can be classified into linear and non-linear data structures o Linear data structures contain Stacks and queues o Non-linear data structures contains Graphs and Trees • Stack: It is a container of objects that can be inserted or removed according to LIFO(Last In First Out) concept. pop() method is used during disposal in Python Eg: stack.pop() # Bottom -> 1 -> 2 -> 3 -> 4 -> 5 (Top) stack.pop() # Bottom -> 1 -> 2 -> 3 -> 4 (Top) print(stack) • Queue: It is a container of objects that can be inserted or removed according to FIFO(First In First Out) concept. • Graph: It is a data structure that consists of a finite set of vertices called nodes, and a finite set of ordered pair (u,v) called edges. It can be classified as direction and weight • Binary Tree: Tree is a hierarchical data structure. Here each node has at most two children • Binary Search Tree: It provides moderate access/ search and moderate insertion/ deletion • Heap: It is a complete tree and is suitable to be stored in an array, It is either MIN or Max • Hashing: Collection of items that are stored in a way that it becomes easy to find them is hashing • To specify size of tuple/list: Synatx: len(myListOrTuple) • Remove element in position X of list/tuple: Syntax: Lists: del myList[x] Tuples: tuples are immutable! • Concatenate two lists/tuples: Lists: myList1 + myList2 Tuples: myTuple1 + myTuple2 Concatenating a List and a Tuple will produce a TypeError exception • Insert element in position x of a list/tuple Syntax: Lists: myList.insert(x, "value") Tuples: tuples are immutable! • Append "x" to a list/tuple: Syntax: Lists: myList.append("x") Tuples: tuples are immutable! • Convert a list/tuple to tuple/list: Syntax: List to Tuple: tuple(myList) Tuple to List: list(myTuple) • To initialize empty list /tuple: Syntax: Lists: myList = [] Tuples: myTuple = () • To get an element in position x in list/tuple: Syntax: "x" in myListOrTuple • Index of element ‘X’ of list/tuple Syntax: myListOrTuple.index("x") - - If not found, throws a ValueError exception • Number of occurance of X in list/tuple: Syntax: myListOrTuple.count("x") • Update an item of List/tuple: Syntax: Lists: myList[x] = "x“ Tuples: tuples are immutable! • Remove element in position X of list/tuple: Syntax: Lists: del myList[x] Tuples: tuples are immutable! Ordered sequence of values indexed by integer numbers. Tuples are immutable Algorithm Best case Average case Worst case Remarks Selection sort ½ n 2 ½ n 2 ½ n 2 n exchanges, quadratic is the best case Insertion sort n ¼ n 2 ½ n 2 Used for small or partial- sorted arrays Bubble sort n ½ n 2 ½ n 2 Rarely useful, Insertion sort can be used instead Shell sort n log3 n unknown c n 3/2 Tight code, Sub quadratic Merge sort ½ n lg n n lg n n lg n n log n guarantee; stable Quick sort n lg n 2 n ln n ½ n 2 n log n probabilistic guarantee; fastest in practice Heap sort n † 2 n lg n 2 n lg n n log n guarantee; in place Worst Case Average Case Data Structure Search Insert Delete Search Insert Delete Sequential search n n n n n n Binary search log n n n log n n n Binary search tree n n n log n log n sqrt(n) Red-black BST log n log n log n log n log n log n Hash table n n n 1 † 1 † 1 † 1 † - Uniform hashing assumption S e t s • Union of two sets Syntax: Method 1: mySet1.union(mySet2) Method 2: mySet1 | mySet2 • Intersection of two sets Syntax: Method 1: mySet1.intersect(mySet2) Method 2: mySet1 & mySet2 • Difference of two sets Syntax: Method 1: mySet1.difference(mySet2) Method 2: mySet1 - mySet2 • Symmetric difference of two sets Syntax: Method 1: mySet1.symmetric_difference(m ySet2) Method 2: mySet1 ^ mySet2 • To initialize an empty set: Syntax: mySet = set() • Initialize a non empty set Syntax: mySet = set(element1, element2...) • To add element X to the set Syntax: mySet.add("x") • Remove element "x" from a set: Syntax: Method 1: mySet.remove("x") -- If "x" is not present, raises a KeyErorr Method 2: mySet.discard("x") -- Removes the element, if present • Remove every element from the set Syntax: mySet.clear() • Check if "x" is in the set Syntax: "x" in mySet • Size of the sets: Syntax: len(mySet) It is an unordered collection with no duplicate elements. It supports mathematical operations like union, intersection, difference and symmetric difference. Data Structures Primitive Non - Primitive Integer Float String Boolean Array List Tuple Set FileDictionary Linear Non - Linear Stacks Queues Graphs Trees D i c t i o n a r i e s It is an unordered set of key value pairs • Initialize an empty Dict Syntax: myDict = {} • Add an element with key "k" to the Dict Syntax: myDict["k"] = value • Update the element with key "k" Syntax: myDict["k"] = newValue • Get element with key "k" Syntax: myDict["k"] -- If the key is not present, a KeyError is raised • Check if the dictionary has key "k" Syntax: "k" in myDict • Get the list of keys Syntax: myDict.keys() • Get the size of the dictionary Syntax: len(myDict) • Delete element with key "k" from the dictionary Syntax: del myDict["k"] • Delete all the elements in the dictionary Syntax: myDict.clear()