**Introduction:**

Data Structure is a collection of data types and set of rules with a format of organizing, managing and storage which can be used for efficient accessing and modification. Data structures are used in every field for storing and organizing data in the computer.

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This Python Data Structure cheat sheet will help you understand what Data Structure is and the basic concepts and commands you must know to get started with it.

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**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

**Data structures** used in the following areas:

- RDBMS: Array (Array of structure)
- Network data model: Graph
- Hierarchical Data model: Trees

## Types of Data Structures:

**Primitive Data Structures:**

- Integer: It is used to represent numeric data, more specifically whole numbers from negative infinity to infinity

Example: 4, 5, -1 etc

- Float: It stands for floating point number

Example: 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

Example:

x = ‘Cake’

y = ‘’Cookie’’

**Certain operations can**** be performed on a string:**

- We can use * to repeat the string for a specific number of times

Example: x*2

- String can be sliced, that is to select parts of the string

Example: Coke

z1 = x[2:] print(z1) # Slicing z2 = y[0] + y[1] print(z2) Output: ke Co

- To capitalize the strings

Example:

str.capitalize('cookie')

- To retrieve the length of the strings

Example:

str1 = "Cake 4 U" str2 = "404" len(str1)

- To replace parts of a string with another string

Example:

str1.replace('4 U', str2)

- Boolean: It is a built-in data type that can take the values TRUE or FALSE

**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

Example:

x = [] # Empty list type(x)

The list can be classified into linear and non-linear data structures

Linear data structures contain **Stacks** and **queues**

Non-linear data structures contain **Graphs** and **Trees**

- Stack: It is a container of objects that can be inserted or removed according to
**LIFO (Last in First Out)**pop() method is used during disposal in Python

Example:

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)- 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

**Lists and tuples (In Python):**

Ordered sequence of values indexed by integer numbers. Tuples are immutable

- To initialize empty list /tuple:

Syntax:

Lists: myList = [] Tuples: myTuple = ()

- To specify size of tuple/list:

Syntax:

len(myListOrTuple)

- 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 occurrences 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!

- 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)

**Sets:**

It is an unordered collection with no duplicate elements. It supports mathematical operations like union, intersection, difference and symmetric difference.

- 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

- 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(mySet2) Method 2: mySet1 ^ mySet2

- Size of the sets:

Syntax:

len(mySet)

**Dictionaries:**

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()

**Algorithms and the complexities:**

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 log_{3} 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 |

**Symbol Table:**

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 ^{†} |

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