Python's IDLE editor: How to Use - by Dr A. Dawson Copyright Dr A Dawson 2005 - 2016 This file is: Python_Editor_IDLE.htm First created: Tuesday 8th March 2005, 7:28 PT, ADLast updated: Saturday 31st January 2015, 9:05 PT, AD This page explains how to run the IDLE integrated development environment (IDE) for editing and running Python 2.x or Python 3 programs. Watch the IDLE Editor movie below (11 minutes)... Python Editor IDLE movie Notice that with syntax highlighting, Python keywords, comments, literal text etc are displayed in different colours or fonts, which makes it much easier for programmers to find errors in their program code. Which version of Python should you install? More Python Resources Follow these instructions to write and run a simple Python program using the IDLE editor: 1. 2. 4. 5. 8. 10. Sponsors: Example Python 2.x Programs (HTML format) Example Python 2.x Programs (text format) Example Python 3.0 Programs (text format) Search for more computer science topics on www.annedawson.net www.annedawson.net
Dive Into Python The One-Stop Shop for Big Data If you have decided to learn Python as your programming language. “What are the different Python libraries available to perform data analysis?” This will be the next question in your mind. There are many libraries available to perform data analysis in Python. Don’t worry; you don’t have to learn all of those libraries. So let’s get started, Numpy It is the foundation on which all higher level tools for scientific Python are built. N- Dimensional array, a fast and memory efficient multidimensional array providing vectorized arithmetic operations. NumPy does not provide high-level data analysis functionality, having an understanding of NumPy arrays and array-oriented computing will help you use tools like Pandas much more effectively. Tutorials Scipy The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. Tutorial I couldn’t find any good tutorial other than Scipy.org. Pandas Pandas is the best tool for doing data munging. Matplotlib Scikit-learn
Think Python: How to Think Like a Computer Scientist How to Think Like a Computer Scientist by Allen B. Downey This is the first edition of Think Python. It uses Python 2, with notes on differences in Python 3. If you are using Python 3, you might want to switch to the second edition. Buy this book at Amazon.com Download Think Python in PDF. Read Think Python in HTML. Example programs and solutions to some problems are here (links to specific examples are in the book). Description Think Python is an introduction to Python programming for beginners. Some examples and exercises are based on Swampy, a Python package written by the author to demonstrate aspects of software design, and to give readers a chance to experiment with simple graphics and animation. Think Python is a Free Book. If you have comments, corrections or suggestions, please send me email at feedback{at}thinkpython{dot}com. Other Free Books by Allen Downey are available from Green Tea Press. Download Precompiled copies of the book are available in PDF. Python 3.0 Michael Kart at St.
A Byte of Python · GitBook "A Byte of Python" is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you. For Python version 3 This book will teach you to use Python version 3. Who reads A Byte of Python? Here are what people are saying about the book: This is the best beginner's tutorial I've ever seen! The best thing i found was "A Byte of Python", which is simply a brilliant book for a beginner. Excellent gentle introduction to programming #Python for beginners -- Shan Rajasekaran Best newbie guide to python -- Nickson Kaigi start to love python with every single page read -- Herbert Feutl perfect beginners guide for python, will give u key to unlock magical world of python -- Dilip I should be doing my actual "work" but just found "A Byte of Python". Recently started reading a Byte of python. A Byte of Python, written by Swaroop. I love your book!
Top 15 Python Libraries for Data Science in 2017 – ActiveWizards: machine learning company – Medium As Python has gained a lot of traction in the recent years in Data Science industry, I wanted to outline some of its most useful libraries for data scientists and engineers, based on recent experience. And, since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity. Core Libraries. 1. NumPy (Commits: 15980, Contributors: 522) When starting to deal with the scientific task in Python, one inevitably comes for help to Python’s SciPy Stack, which is a collection of software specifically designed for scientific computing in Python (do not confuse with SciPy library, which is part of this stack, and the community around this stack). The most fundamental package, around which the scientific computation stack is built, is NumPy (stands for Numerical Python). 2. SciPy is a library of software for engineering and science. 3. There are two main data structures in the library: 5.
Welcome to Python.org Hacking Secret Ciphers with Python - Chapters Chapter 1 Read online: Chapter 1 - Making Paper Cryptography Tools PDF of the Caesar Cipher WheelInteractive Virtual Cipher Wheel Chapter 2 Read online: Chapter 2 - Downloading and Installing Python Download Python 3Download pyperclip.py Chapter 3 Read online: Chapter 3 - The Interactive Shell Chapter 4 Read online: Chapter 4 - String and Writing Programs Download source: hello.py Copy source to clipboard: Use the online diff tool to find typos in your code: hello.py Chapter 5 Read online: Chapter 5 - The Reverse Cipher Download source: reverseCipher.py Use the online diff tool to find typos in your code: reverseCipher.py Chapter 6 Read online: Chapter 6 - The Caesar Cipher Download source: caesarCipher.py Use the online diff tool to find typos in your code: caesarCipher.py Download source: caesarCipher2.py Use the online diff tool to find typos in your code: caesarCipher2.py Download source: password.py Use the online diff tool to find typos in your code: password.py Download source: password2.py Chapter 7
Beautiful Soup Documentation — Beautiful Soup v4.0.0 documentation Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. These instructions illustrate all major features of Beautiful Soup 4, with examples. This document covers Beautiful Soup version 4.12.1. You might be looking for the documentation for Beautiful Soup 3. This documentation has been translated into other languages by Beautiful Soup users: Getting help If you have questions about Beautiful Soup, or run into problems, send mail to the discussion group. When reporting an error in this documentation, please mention which translation you’re reading. Here’s an HTML document I’ll be using as an example throughout this document. Running the “three sisters” document through Beautiful Soup gives us a BeautifulSoup object, which represents the document as a nested data structure: $ apt-get install python3-bs4
PythonBooks - Learn Python the easy way ! How to Think Like a Computer Scientist Learning with Python by Allen Downey, Jeff Elkner and Chris Meyers. This book is now available for sale at Lulu.com. Hardcopies are no longer available from Green Tea Press. How to Think... is an introduction to programming using Python, one of the best languages for beginners. How to Think... is a Free Book available under the GNU Free Documentation License. Please send suggestions, corrections and comments about the book to feedback{at}thinkpython{dot}com. Download The book is available in a variety of electronic formats: Precompiled copies of the book are available in PDF and Postscript . Translations Here are some translations of the book into other (natural) languages: Spanish translation by Gregorio Inda. Other Free Books by Allen Downey are available from Green Tea Press. If you are using this book and would like to make a contribution to support my work, please consider making a donation toward my web hosting bill by clicking on the icon below.
Online Python Tutor - Learn programming by visualizing code execution Invent Your Own Computer Games with Python - Chapters Chapter 1 Read online: Chapter 1 - Installing Python Videos: Chapter 2 Read online: Chapter 2 - The Interactive Shell Chapter 3 Read online: Chapter 3 - Strings Download source: hello.py Copy source to clipboard: Use the online diff tool to find typos in your code: hello.py Chapter 4 Read online: Chapter 4 - Guess the Number Download source: guess.py Use the online diff tool to find typos in your code: guess.py Chapter 5 Read online: Chapter 5 - Jokes Download source: jokes.py Use the online diff tool to find typos in your code: jokes.py Chapter 6 Read online: Chapter 6 - Dragon Realm Download source: dragon.py Use the online diff tool to find typos in your code: dragon.py Chapter 7 Read online: Chapter 7 - Using the Debugger Chapter 8 Read online: Chapter 8 - Flow Charts Chapter 9 Read online: Chapter 9 - Hangman Download source: hangman.py Use the online diff tool to find typos in your code: hangman.py Chapter 10 Read online: Chapter 10 - Tic Tac Toe Download source: tictactoe.py Chapter 11 Download source: bagels.py