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{
"category": "SciPy 2013",
"language": "English",
"slug": "numpy-and-ipython-scipy2013-tutorial-part-2-of-1",
"speakers": [],
"tags": [
"Tech"
],
"id": 2175,
"state": 1,
"title": "NumPy and IPython, SciPy2013 Tutorial, Part 2 of 2",
"summary": "Description\n\nThis tutorial is a hands-on introduction to the two most basic building-blocks of the scientific Python stack: the enhanced interactive interpreter IPython and the fast numerical container Numpy. Amongst other things you will learn how to structure an interactive workflow for scientific computing and how to create and manipulate numerical data efficiently. You should have some basic familiarity with Python (variables, loops, functions) and basic command-line usage (executing commands, using history).\n\nOutline\n\nIpython (1 hour)\n\nUsing the IPython notebook\nHelp system, magic functions, aliases and history\nNumpy (3 hours)\n\nBasic arrays, dtypes and numerical operations\nIndexing, slicing, reshaping and broadcasting\nCopies, views and fancy indexing\nThe tutorial will feature short bursts of small exercises every 5-10 minutes.\n\nRequired Packages\n\nAn install of Anaconda should be enough\n\nNumpy (Version 1.6 or higher)\nIpython (Version 0.13 or higher)\nMatplotlib (Version 1.2.1 or higher)\nDocumentation\n\nI have converted a large part of the Numpy chaper from the Python Scientific Lecture Notes to IPython notebook format using the sphinx2ipynb converter from the nbconvert project. All materials are collected in my scipy2013-tutorial-numpy-ipython Gitub repository at https://github.com/esc/scipy2013-tutorial-numpy-ipython or http://git.io/bocNDg.",
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