**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.

Further, if you want to learn Python Data Structure in-depth, you can refer to the tutorial blog on 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

**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’’

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**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)

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**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

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

An 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)

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**Sets:**

Set 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)

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**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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