https://github.com/archish27/pythontutorial

Python Programming Tutorial for new geeks who want to learn python from scratch to deal with various applications
https://github.com/archish27/pythontutorial

matplotlib numpy pandas pygame python python-2 python-3 scikit-learn soup

Last synced: 2 months ago
JSON representation

Python Programming Tutorial for new geeks who want to learn python from scratch to deal with various applications

• Host: GitHub
• URL: https://github.com/archish27/pythontutorial
• Owner: Archish27
• License: gpl-3.0
• Created: 2017-12-19T07:00:12.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-03-22T09:24:23.000Z (about 8 years ago)
• Last Synced: 2026-03-27T23:42:55.709Z (3 months ago)
• Topics: matplotlib, numpy, pandas, pygame, python, python-2, python-3, scikit-learn, soup
• Language: Python
• Size: 198 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# PythonTutorial

Python Programming Tutorial for new geeks who want to learn python from scratch to deal with various applications

### Includes two parts:

Basics - Part 1

------

Quick start to learn python, beginner mode enables you to be familiar with syntax & semantics of python programming. Sample programs give clear view to understand python programming language.

* Arithmetic Operations

* List

* Tuple

* Dictionary

* Conditions

* Looping

* Functions

* Strings

* File I/O

* Inputs

* Object Oriented 

Intermediate - Part 2

------------

Intermediate tutorials enables where actually list,tuple or dictionary can be used efficiently.

* Advance Strings

* Command Line Arguments

* List Comprehension & Generators

* Custom Generators

* Timeit Module

* Enumerate

* Zip

* Multiprocessing

* Process Communication

* OOP Advanced

Data Analysis - Part 3

------------

Data Analysis tutorial deals with datasets & includes various libaries to transform such datasets.

* Pandas Basics

* Pandas Examples

* Pandas I/O

* Building Datasets

* Concatenation 

* Merge & Join

* Pickling

* Percentage Change

* Correlation 

* Sampling