Programming/Learning and Teaching. Python Tutorials. Learn Python. How to Turn a Web App Into a Desktop App, Using Chromium and PyInstaller. Getting Started with Python Programming and Scripting in Linux - Part 1. It has been said (and often required by recruitment agencies) that system administrators need to be proficient in a scripting language.
While most of us may be comfortable using Bash (or other shell of our choice) to run command-line scripts, a powerful language such as Python can add several benefits. To begin with, Python allows us to access the tools of the command-line environment and to make use of Object Oriented Programming features (more on this later in this article). On top of it, learning Python can boost your career in the fields of desktop applications and data science. Being so easy to learn, so vastly used, and having a plethora of ready-to-use modules (external files that contain Python statements), no wonder Python is the preferred language to teach programming to first-year computer science students in the United States.
Python in Linux Python versions 2.x and 3.x are usually available in most modern Linux distributions out of the box. Then, you can install it as follows: Python Cheatsheet. Python Basics. Python Tutorial - Home. Learn Python - Free Interactive Python Tutorial. O tutorial de Python — Python v2.7.2 documentation. Python é uma linguagem de programação poderosa e de fácil aprendizado. Possui estruturas de dados de alto nível eficientes, bem como adota uma abordagem simples e efetiva para a programação orientada a objetos. Sua sintaxe elegante e tipagem dinâmica, além de sua natureza interpretada, tornam Python ideal para scripting e para o desenvolvimento rápido de aplicações em diversas áreas e na maioria das plataformas. O interpretador Python e sua extensa biblioteca padrão estão disponíveis na forma de código fonte ou binário para a maioria das plataformas a partir do site, e podem ser distribuídos livremente.
No mesmo sítio estão disponíveis distribuições e referências para diversos módulos, programas, ferramentas e documentação adicional, contribuídos por terceiros. O interpretador Python é facilmente extensível incorporando novas funções e tipos de dados implementados em C ou C++ (ou qualquer outra linguagem acessível a partir de C). Note Sobre esta tradução. Facebook. Use Python with Your Neural Networks -- Visual Studio Magazine. Neural Network Lab Use Python with Your Neural Networks A neural network implementation can be a nice addition to a Python programmer's skill set.
If you're new to Python, examining a neural network implementation is a great way to learn the language. Get Code Download One of the most common requests I get from readers is to demonstrate a neural network implemented using the Python programming language. The best way to get a feel for where this article is headed is to take a look at Figure 1, which shows a demonstration of a Python program predicting the species of an iris flower based on the flower's color (blue, pink or teal), petal length and petal width. Blue, 1.4, 0.3, setosa pink, 4.9, 1.5, versicolor teal, 5.6, 1.8, virginica The three predictor variables -- color, length and width -- are in the first three columns and the dependent variable, species (setosa, versicolor or virginica) is in the fourth column.
Listing 1: Overall Program Structure The training data is set up like so: How to Create GUI Applications Under Linux Desktop Using PyGObject - Part 1. Julia Calling Python Calling Julia... Julia is a young programming language. This means that its native libraries are immature. We are in a time when Julia is a mature enough as a language that it is out-pacing its libraries. One way to use mature libraries from a young language is to borrow them from another language. In this case, we'll be borrowing from Python. Python from Julia: PyCall.jl Calling Python from Julia requires PyCall. Let's start with a "Hello, World" scale example. using PyCallpyeval("2+2") #=> 4pyeval("str(5)") #=> "5" # doing things by hand, for fun :)math = pyimport(:math) #=> PyObject <module 'math'>pycall(math["sin"],Float64,1) #=> 0.8414709848078965 Using Python Libraries from Julia For a quick practical example of Python libraries filling in Julia's current gaps, we can use Python's matplotlib for graphing.
Using PyCall@pyimport pylabx = linspace(0,2*pi,1000); y = sin(3*x + 4*cos(2*x));pylab.plot(x, y; color="red", linewidth=2.0, linestyle="--")pylab.show() Julia from Python: the julia module Oh no! Tl;dr. Build a web app with Python and Flask using DocumentDB. Discover more resources for these services: DocumentDB Scenario To highlight how customers can efficiently leverage Azure DocumentDB to store and query JSON documents, this document provides an end-to-end walkthrough of building a voting web application using Azure Document DB. This walkthrough shows you how to use DocumentDB service provided by Azure to store and access data from an Python web application hosted on Azure and presumes that you have some prior experience using Python and Azure Websites.
You will learn: 1. 2. 3. 4. By following this walkthrough, you will build a simple voting application that allows you to vote for a poll. Prerequisites Before following the instructions in this article, you should ensure that you have the following installed: Visual Studio 2013 (or Visual Studio Express which is the free version) Python Tools for Visual Studio from here Azure SDK for Visual Studio 2013, version 2.4 or higher available from here From here, select the option for Azure DocumentDB. SMS Alerts with the Plushcap Python Package: Part 1. When your website crashes you can either hear about it from your alerting software or an angry customer.
That’s why a variety of hosted services exist to monitor websites. However, for small websites you often want a simple open source project that can be set up in a couple of minutes to get the monitoring job done. In this post we’ll walk through the creation of a monitoring and alerting Python package. This project is called Plushcap. What’s a Plushcap? It’s a species of tiny bird found in South American countries such as Argentina and Peru.
We’re going with the name Plushcap for a few reasons. Many blog posts just walk through coding an app. Setting up our Initial Python Package There’s some boilerplate code required for a Python project to be installable via PyPI. . $ virtualenv --no-site-packages ~/Envs/cookie Next we activate the virtualenv. $ source ~/Envs/cookie/bin/activate Install cookiecutter. $ pip install cookiecutter What did cookiecutter just do? Initial Git commit. Python Tutorial - javatpoint. Python is a simple, easy to learn, powerful, high level and object-oriented programming language. Python is an interpreted scripting language also. Guido Van Rossum is known as the founder of python programming. Introduction to Python It covers the topics such as python programming, features, history, versions, how to install, example, how to execute, variables, keywords, identifiers, literals, operators and comments.
Control Statement The control statement in python covers if statement, for loop, while loop, do while loop, break statement, continue statement and pass statement. Python Strings The string chapter in python provides the full functionality to work on strings such as accessing string, applying string operators, details of slice notation, applying different functions etc. Python Lists The list chapter in python covers the data structure part such as storing data in list, accessing data, manipulating data etc. Python Tuples Python Dictionary Python Functions Python Files I/O. Quickstart: Run a Gmail App in Python - Gmail API. Mocking in Python: A Guide to Better Unit Tests. How to Run Unit Tests Without Testing Your Patience More often than not, the software we write directly interacts with what we would label as “dirty” services. In layman’s terms: services that are crucial to our application, but whose interactions have intended but undesired side-effects—that is, undesired in the context of an autonomous test run.
For example: perhaps we’re writing a social app and want to test out our new ‘Post to Facebook feature’, but don’t want to actually post to Facebook every time we run our test suite. The Python unittest library includes a subpackage named unittest.mock—or if you declare it as a dependency, simply mock—which provides extremely powerful and useful means by which to mock and stub out these undesired side-effects. Note: mock is newly included in the standard library as of Python 3.3; prior distributions will have to use the Mock library downloadable via PyPI.
Fear System Calls A Simple Delete Function #! #! Refactoring with Mocks #! Potential Pitfalls #! #! #! Sharing Your Labor of Love: PyPI Quick And Dirty — Hynek Schlawack. A completely incomplete guide to packaging a Python module and sharing it with the world on PyPI. Abstract Few things give me caremads like Python modules I want to use that aren’t on PyPI (pronounced “pie pee eye”, or “cheese shop”, not “pie pie”!). On the other hand – as pydanny points out – the current situation on packaging is rather confusing. Therefore I want to help everyone who has some great code but feels lost with getting it on PyPI. I will be using my latest opus “pem” as a realistic yet simple example of how to get a pure-Python 2 and 3 module packaged up, tested and uploaded to PyPI. Including the new and shiny binary wheel format that’s faster and allows for binary extensions (read the wheel story if you want to know more and watch the list at Python Wheels get greener and greener)!
I’ll keep it super simple to get everyone started. Tools Used This is not a history lesson, therefore we will use: A Minimal Glimpse Into The Past setup.py One icky thing are dependencies. And Thanks. Python 101: An Introduction to Python’s Debugger. Python comes with its own debugger module that is named pdb. This module provides an interactive source code debugger for your Python programs. You can set breakpoints, step through your code, inspect stack frames and more. We will look at the following aspects of the module: How to start the debugger Stepping through your code Setting breakpoints Let’s start by creating a quick piece of code to attempt debugging with. Here’s a silly example: 04.def doubler(a): 06. result = a*2 07. print(result) 08. return result 11.def main(): 13. for i in range(1,10): 14. doubler(i) 16.if __name__ == "__main__": 17. main() Now let’s learn how to run the debugger against this piece of code.
How to Start the Debugger You can start the debugger three different ways. 01. >>> import debug_test 02. >>> import pdb 03. >>> pdb.run('debug_test.main()') 04. 05. Here we import our module and pdb. The other way to start the debugger is to execute the following command via your terminal session: 1.python -m pdb debug_test.py 02. 06. 09. 4. Parallel Programming in Python. Using Python's multiprocessing module -- written by Sebastian Raschka June 20, 2014 CPUs with multiple cores have become the standard in the recent development of modern computer architectures and we can not only find them in supercomputer facilities but also in our desktop machines at home, and our laptops; even Apple's iPhone 5S got a 1.3 Ghz Dual-core processor in 2013.
However, the default Python interpreter was designed with simplicity in mind and has a thread-safe mechanism, the so-called "GIL" (Global Interpreter Lock). In order to prevent conflicts between threads, it executes only one statement at a time (so-called serial processing, or single-threading). In this introduction to Python's multiprocessing module, we will see how we can spawn multiple subprocesses to avoid some of the GIL's disadvantages. This table of contents was created by markdown-toclify Multi-Threading vs. The multiprocessing module in Python's Standard Library has a lot of powerful features.
To find out more, follow the lessons. About Reeborg Inspired by Guido van Robot, itself inspired by the original Karel the Robot created by Richard Pattis, Reeborg first appeared in RUR-PLE, a program I created to learn Python, picking up programming as a hobby. Brython / brython-firefoxos-memos. Bitbucket is a code hosting site with unlimited public and private repositories.
Pypix | The Python Hub. High Performance Python at PyDataLondon 2014 | Entrepreneurial Geekiness. Yesterday I spoke on The High Performance Python Landscape at PyDataLondon 2014 (our first PyData outside of the USA – see my write-up). I was blessed with a full room and interesting questions. With Micha I’m authoring a High Performance Python book with O’Reilly (email list for early access) and I took the topics from a few of our chapters. “@ianozsvald providing eye-opening discussion of tools for high-performance #Python: #Cython, #ShedSkin, #Pythran, #PyPy, #numba… #pydata” – @davisjmcc Overall I covered: Here’s my room full of happy Pythonistas “Really useful and practical performance tips from @ianozsvald @pydata #pydata speeding up #Python code” – @iantaylorfb Slides from the talk: UPDATE Armin and Maciej came back today with some extra answers about the PyPy-numpy performance (here and here), the bottom line is that they plan to fix it (Maciej says it is now fixed – quick service!).
How to Create a Python Library. In this tutorial, our goal is to create a Python Library which is a FTP class that is well written, useful, and expandable. Outlining our Objective It’s always important to first outline exactly what functionality your class should include. In our case: connecting to a servercreate a folder on the serverupload a filechange directoryretrieving the directory listingdownload a file When Would I Use an FTP Class? There are several instances when one might use this sort of class. Automate uploading of images, such as a gallery, to a client’s website.Perform off-site backups by transferring a database backup file from your server to another. Note: It’s easy to run into issues with FTP due to different server configurations. What is FTP? FTP: ”A standard network protocol used to copy a file from one host to another.” Essentially, it allows you to copy a file(s) from one computer to another. Step 1 - Preparation We’ll start off as easy as possible.
Step 2 - Setting up the Class Step 3 - Class Variables. Viva o Python: Dica: Git e Github para iniciantes. Python para Bioinformatas. Python Importing - Amir Rachum. Creating and running a Python unit test - PyCharm. Invent Your Own Computer Games with Python. Teaching a Computer to Read:: NLP Hacking in Python. Python Quick Reference v2.7. How IPython Notebook and Github have changed the way I teach Python | peak 5390. S Python Class - Educational Materials. Wiki. Beginning Game Programming for Teens with Python. Writing a game in Python with Pygame. Part I.