How to Think Like a Computer Scientist — How to Think like a Computer Scientist: Interactive Edition
This interactive book is a product of the Runestone Interactive Project at Luther College, led by Brad Miller and David Ranum. There have been many contributors to the project. Our thanks especially to the following:
The Hitchhiker’s Guide to Python!
Greetings, Earthling! Welcome to The Hitchhiker’s Guide to Python. This is a living, breathing guide. If you’d like to contribute, fork us on GitHub! 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.
Installation and quick start — nose 1.3.0 documentation
nose extends unittest to make testing easier. On most UNIX-like systems, you’ll probably need to run these commands as root or using sudo. Install nose using setuptools/distribute:
alyssa frazee
Thu 02 January 2014 | -- (permalink) My sister is a senior undergraduate majoring in sociology. She just landed an awesome analyst job for next semester and was told she'll be using some R in the course of her work.
Python Extension Packages for Windows - Christoph Gohlke
by Christoph Gohlke, Laboratory for Fluorescence Dynamics, University of California, Irvine. This page provides 32- and 64-bit Windows binaries of many scientific open-source extension packages for the official CPython distribution of the Python programming language. The files are unofficial (meaning: informal, unrecognized, personal, unsupported, no warranty, no liability, provided "as is") and made available for testing and evaluation purposes.
Open Source Finance 1. QuantLib - An Interview with Luigi Ballabio
This is the first in a new series of interviews with leading developers involved in open source financial software. In the first interview I talk to Luigi Ballabio about QuantLib, a free software framework for Quantitative Finance which he has been involved with since it's original launch in the year 2000. Luigi currently participates in the design, maintenance and development of the core C++ library for QuantLib, including the review and management of the contributions from users. In his day job, Luigi is Senior Quantitative Developer at StatPro Italia where he designs and implements the C++ financial libraries used in the StatPro Risk Management (SRM) and Complex Asset Pricing (CAP) applications. In this interview, Luigi also talks about his 3-day training course, Introduction to QuantLib Development, which is next due to take place in London, 22-24th September 2014 and costs £1900 per person + VAT. You can Download a Brochure and Registration Form for the Course here
Download – Orange
This page contains nightly builds from the code repository. These are typically stable and we recommend using them. Windows ¶ Full package: Snapshot of Orange with Python 2.7 and required libraries This package is recommended to those installing Orange for the first time.
1.4. Matplotlib: plotting — Scipy lecture notes
1.4.2. Simple plot Tip In this section, we want to draw the cosine and sine functions on the same plot.
Pong
Pong (marketed as PONG) is one of the earliest arcade video games; it is a tennis sports game featuring simple two-dimensional graphics. While other arcade video games such as Computer Space came before it, Pong was one of the first video games to reach mainstream popularity. The aim is to defeat the opponent in a simulated table tennis game by earning a higher score. The game was originally manufactured by Atari Incorporated (Atari), who released it in 1972. Allan Alcorn created Pong as a training exercise assigned to him by Atari co-founder Nolan Bushnell. Bushnell based the idea on an electronic ping-pong game included in the Magnavox Odyssey, which later resulted in a lawsuit against Atari.
numexpr - Fast numerical array expression evaluator for Python and NumPy.
Please be aware that the numexpr project has been migrated to GitHub. This site has been declared unmaintained as of 2014-01-21. Sorry for the inconveniences. -- Francesc Alted What It Is The numexpr package evaluates multiple-operator array expressions many times faster than NumPy can.
s Python Class - Educational Materials
Welcome to Google's Python Class -- this is a free class for people with a little bit of programming experience who want to learn Python. The class includes written materials, lecture videos, and lots of code exercises to practice Python coding. These materials are used within Google to introduce Python to people who have just a little programming experience. The first exercises work on basic Python concepts like strings and lists, building up to the later exercises which are full programs dealing with text files, processes, and http connections.
Welcome — Theano 0.6 documentation
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.dynamic C code generation – Evaluate expressions faster.extensive unit-testing and self-verification – Detect and diagnose many types of errors.
24.6. IDLE — Python v2.7.4 documentation
IDLE is the Python IDE built with the tkinter GUI toolkit. IDLE has the following features: coded in 100% pure Python, using the tkinter GUI toolkitcross-platform: works on Windows and Unixmulti-window text editor with multiple undo, Python colorizing and many other features, e.g. smart indent and call tipsPython shell window (a.k.a. interactive interpreter)debugger (not complete, but you can set breakpoints, view and step) 24.6.2.
