Introduction to Python Web Frameworks So, you’ve learned Python, perhaps using one of the resources you found in our MegaGuide. You’ve built some projects that run on your computer, and now you’re ready to share them with the world online. What’s the next step? If you want to turn your Python project into a web application or a dynamic website, and host it on the Internet for anyone to access, you’re going to need to use a web framework. A web framework, also known as a web application framework, is the glue that bonds your project to the server that’s hosting it. Some web frameworks include many features right out of the box, like a built-in administrator interface, while others take a more minimal approach by only including the bare essentials to get your simple web application or website up and running. Learning a framework can be intimidating, especially for beginners, as the guides and documentation associated with any given framework can be difficult to understand. Django Website Official Documentation Official Tutorial

Probably Overthinking It: Regression with Python, pandas and StatsModels I was at Boston Data-Con 2014 this morning, which was a great event. The organizer, John Verostek, seems to have created this three-day event single-handedly, so I am hugely impressed. Imran Malek started the day with a very nice iPython tutorial. The description is here, and his slides are here. He grabbed passenger data from the MBTA and generated heat maps showing the number of passengers at each stop in the system during each hour. The tutorial covered a good range of features, and it seemed like many of the participants were able to download the data and follow along in iPython. And Imran very kindly let me use his laptop to project slides for my talk, which was next. Regression is a powerful tool for fitting data and making predictions. As an example, I will use data from the National Survey of Family Growth to generate predictions for the date of birth, weight, and sex of an expected baby. This talk is appropriate for people with no prior experience with regression.

The Hitchhiker’s Guide to Python! — The Hitchhiker's Guide to Python Greetings, Earthling! Welcome to The Hitchhiker’s Guide to Python. This is a living, breathing guide. This handcrafted guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a daily basis. This guide is opinionated in a way that is almost, but not quite, entirely unlike Python’s official documentation. Let’s get started! Getting Started with Python New to Python? Properly Install Python Writing Great Python Code This part of the guide focuses on the best-practices for writing Python code. Scenario Guide for Python Applications This part of the guide focuses on tool and module advice based on different scenarios. Shipping Great Python Code This part of the guide focuses on deploying your Python code. Python Development Environments This part of the guide focus on the Python development environment, and the best-practice tools that are available for writing Python code. Additional Notes Note

Is it hashable? Fun and games with hashing in Python - Lerner Consulting Blog One of the basic data types that Python developers learn to use, and to appreciate, is the dictionary, or “dict.” This is the Python term for what other languages call hashes, associative arrays, hashmaps, or hash tables. Dictionaries are pervasive in Python, both in the programs that we write, and in the implementation of the language; behind every namespace or object, at least one dictionary is behind the scenes. Dictionaries are fairly easy to use, once you get used to the rules of the road: A dictionary contains pairs, not individual elements. Each pair has two elements, a “key” and a “value.” To anyone familiar with dicts, or with hash tables in other languages, most of the above rules make a great deal of sense. This also explains why you can use keys to retrieve values, but not the reverse: The location of a value in memory depends completely on its key. For example, I can create a simple dictionary: Ruby stores our name-value pair inside of the hash, its equivalent of a dict.

The History of Python The Python I Would Like To See This post is surprisingly confused, it is phrased as a complaint about the language, then immediately degrades into CPython implementation specifics that have little bearing on the usability of the language itself. Ronacher should also know better than to post microbenchmarks like the one provided here, especially without corresponding (C) profiler output. At the C level, slots allow the implementation constant-time access to the most common code paths for an object, and especially when you have C code calling other C code via the type system (IMHO the primary use for Python, and still its strongest use case), "interpreter overhead" is reduced to a few extra memory indirection operations. In the alternative world, sure, perhaps some microbenchmark may behave faster, but now systemically, and for e.g. Python is all about providing a lightweight way to compose bits of fast code (the kernel, network stack, NumPy, MySQL, whatever).

The Python I Would Like To See written on Saturday, August 16, 2014 It's no secret that I'm not a fan of Python 3 or where the language is currently going. This has led to a bunch of emails flying my way over the last few months about questions about what exactly I would prefer Python would do. So I figured I might share some of my thoughts publicly to maybe leave some food for thought for future language designers :) Python is definitely a language that is not perfect. I want to take you on a journey that starts with a small oddity in the interpreter (slots) and ends up with the biggest mistake in the language design. In general though these posts will be an exploration about design decisions in the interpreter and what consequences they have on both the interpreter and the resulting language. Language vs Implementation In this particular case this obscure implementation detail of the interpreter changed or influenced the language design and also forced other Python implementations to adopt. Slots So what's a slot? $ .

Full Stack Python A guide to Python Namespaces | The ByteBaker This post is part of the Powerful Python series where I talk about features of the Python language that make the programmer’s job easier. The Powerful Python page contains links to more articles as well as a list of future articles. Namespaces are a fundamental idea in Python and can be very helpful in structuring and organizing your code (especially if you have a large enough project). However, namespaces might be a somewhat difficult concept to grasp and get used to if you’re new to programming or even coming from another programming language (in my case, Java). Here’s my attempt to make namespaces just a little easier to understand. What’s in a name? Before starting off with namespaces, you have to understand what Python means by a name. But you can also give names to things like functions: Now whenever you want to use func(), you can use f() instead. If you accessed the name var in between assignments, you’d get a number, a string and a list at different times. So much for names.

Minecraft: Pi Edition- How to Use Python @Raspberry_pi #piday #raspberrypi August 15, 2014 AT 2:00 am This tutorial from MakerSpace UK is a great intro into using python and your Raspberry Pi with Minecraft: First, we have to do a bit of setting up. To keep our code separate to the original code in the MCPI folder, we will make a new directory to keep all of our code in. In LXTerminal, type mkdir python This will make a directory called python. Read more. Each Friday is PiDay here at Adafruit! Related No comments yet. Adafruit has a "be excellent to each other" comment policy.