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‎README.md‎

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## python-practice
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- Python-data-structure
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- Python-interview
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- Python-leetcode
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- Python-library
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- Understand-Python

‎python-data-structure/.ipynb_checkpoints/Data Structure Overview-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### General Data Structures\n",
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"The various data structures in computer science are divided broadly \n",
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"into two categories shown below"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### LINER DATA STRUCTURES\n",
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"\n",
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"- Array:It is a sequential arrangement of data elements paired with the index of the data element.\n",
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"- Linked List: Each data element contains a link to another element along with the data present in it.\n",
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"- Stack: LIFO(last in First Out ) or FILO(First in Last Out)\n",
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"- Queue: Only FIFO(First In First Out)\n",
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"- Matrix: It is two dimensional data structure in which the data element is referred by a pair of indices"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### NON-LINER DATA STRUCTURES\n",
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"- Binary Tree: It is a data structure where each data element can be connected to maximum two other data elements and it starts with a root node.\n",
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"- Heap: It is a special case of Tree data structure where the data in the parent node is either strictly greater than/ equal to the child nodes or strictly less than it’s child nodes.\n",
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"- Hash Table: It is a data structure which is made of arrays associated with each other using a hash function. It retrieves values using keys rather than index from a data element.\n",
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"- Graph: .It is an arrangement of vertices and nodes where some of the nodes are connected to each other through links."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### PYTHON SPECIFIC DATA STRUCTURES\n",
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"- List: It is similar to array with the exception that the data elements can be of different data types. You can have both numeric and string data in a python list.\n",
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"- Tuple: Tuples are similar to lists but they are immutable which means the values in a tuple cannot be modified they can only be read.\n",
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"- Dictionary: The dictionary contains Key-value pairs as its data elements."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "python pratice",
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"language": "python",
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"name": "env_name"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎python-data-structure/.ipynb_checkpoints/Python-Arrays-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Array Representation\n",
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"Arrays can be declared in various ways in different languages and these items should be of the same type\n",
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"![Array Representation](https://www.tutorialspoint.com/data_structures_algorithms/images/array_declaration.jpg)\n",
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"![](https://www.tutorialspoint.com/data_structures_algorithms/images/array_representation.jpg)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Basic Operations\n",
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"- Traverse\n",
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"- Insertion\n",
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"- Deletion\n",
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"- Search\n",
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"- Update"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Array is created in Python by importing array module to the python program . Then the array is declared as shown below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-23T07:01:22.031150Z",
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"start_time": "2019-05-23T07:01:22.026914Z"
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}
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},
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"outputs": [],
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"source": [
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"from array import *\n",
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"\n",
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"array_name = array(typecode, [Initializers])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Typecode are the codes that are used to define the type of value the array will hold. Some common typecodes used are:\n",
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"\n",
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"Typecode Value\n",
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"- b\t: signed integer of size 1 byte \n",
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"- B\t: unsigned integer of size 1 byte\n",
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"- c\t: character of size 1 byte\n",
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"- i\t: signed integer of size 2 bytes\n",
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"- I\t: unsigned integer of size 2 bytes\n",
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"- f\t: floating point of size 4 bytes\n",
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"- d\t: floating point of size 8 bytes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-23T07:06:41.672138Z",
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"start_time": "2019-05-23T07:06:41.664693Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1\n",
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"2\n",
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"3\n",
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"4\n"
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]
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}
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],
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"source": [
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"from array import *\n",
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"\n",
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"array1 = array('i', [1,2,3,4])\n",
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"for x in array1:\n",
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" print(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Accessing Array Element"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-23T07:07:57.070395Z",
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"start_time": "2019-05-23T07:07:57.063666Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3\n",
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"4\n"
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]
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}
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],
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"source": [
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"from array import *\n",
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"\n",
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"array1 = array('i', [1,2,3,4])\n",
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"\n",
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"print(array1[2])\n",
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"print(array1[-1])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Insertion Operation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-23T07:09:54.352372Z",
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"start_time": "2019-05-23T07:09:54.346697Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"array('i', [1, 44, 2, 3, 4, 5, 6])\n"
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]
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}
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],
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"source": [
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"from array import *\n",
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"\n",
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"array1 = array('i', [1,2,3,4,5,6])\n",
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"\n",
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"array1.insert(1, 44)\n",
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"print(array1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Deletion Operation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-23T07:10:58.372893Z",
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"start_time": "2019-05-23T07:10:58.366593Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"array('i', [1, 2, 3, 5, 10, 11, 100])\n"
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]
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}
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],
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"source": [
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"from array import *\n",
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"\n",
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"array1 = array('i', [1,2,3,4,5,10,11,100])\n",
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"\n",
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"array1.remove(4)\n",
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"print(array1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Search Operation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-23T07:12:00.675918Z",
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"start_time": "2019-05-23T07:12:00.668458Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3\n"
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]
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}
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],
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"source": [
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"from array import *\n",
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"\n",
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"array1 = array('i', [1,2,3,4,5])\n",
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"\n",
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"print(array1.index(4))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Update Operation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-05-23T07:13:06.729343Z",
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"start_time": "2019-05-23T07:13:06.722047Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"array('i', [1, 2, 100, 4, 5])\n"
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]
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}
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],
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"source": [
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"from array import *\n",
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"\n",
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"array1 = array('i', [1,2,3,4,5])\n",
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"\n",
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"array1[2] = 100\n",
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"print(array1)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "python pratice",
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"language": "python",
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"name": "env_name"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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