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<!doctype html>
<html>
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="chrome=1">
<title>Python-Sp15 by uiuc-cse</title>
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<header>
<h1>Python-Sp15</h1>
<p>CSE Training Workshops in Python, Spring 2015</p>
</header>
<div id="banner">
<span id="logo"></span>
<a href="https://github.com/uiuc-cse/python-sp15" class="button fork"><strong>View On GitHub</strong></a>
<div class="downloads">
<span>Downloads:</span>
<ul>
<li><a href="https://github.com/uiuc-cse/python-sp15/zipball/master" class="button">ZIP</a></li>
<li><a href="https://github.com/uiuc-cse/python-sp15/tarball/master" class="button">TAR</a></li>
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<nav>
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<section>
<p>All workshops will be held in the EWS computer laboratory, L440 Digital Computer Laboratory. There is no sign-up for this series—walk-ins are welcome and encouraged!</p>
<p>L440 DCL is a little tricky to find if you haven't been there before. It's located in the basement, and can be accessed by going down the main staircase in DCL and turning right.</p>
<p><img src="./img/map-l440.png" alt=""></p>
<h1>
<a id="setup-python-and-jupyter-notebook" class="anchor" href="#setup-python-and-jupyter-notebook" aria-hidden="true"><span class="octicon octicon-link"></span></a>Setup (Python and Jupyter Notebook)</h1>
<p>For most of the lessons, we will require outside modules. While several methods for managing your own library of modules exists, we will use <a href="https://www.enthought.com/products/canopy/">Enthought Canopy</a>, which is installed on your EWS machines already. <a href="https://store.continuum.io/cshop/anaconda/">Anaconda</a> is another excellent Python distribution for your home machine.</p>
<h1>
<a id="introduction-to-python" class="anchor" href="#introduction-to-python" aria-hidden="true"><span class="octicon octicon-link"></span></a><a href="#intro">Introduction to Python</a>
</h1>
<h2>
<a id="january-28-10-amnoon" class="anchor" href="#january-28-10-amnoon" aria-hidden="true"><span class="octicon octicon-link"></span></a>January 28, 10 am–noon</h2>
<ul>
<li><a href="https://github.com/uiuc-cse/python-sp15/blob/gh-pages/lessons/intro.md">Lesson Notes</a></li>
</ul>
<h1>
<a id="numerical--scientific-programming-with-python-numpy-scipy" class="anchor" href="#numerical--scientific-programming-with-python-numpy-scipy" aria-hidden="true"><span class="octicon octicon-link"></span></a><a href="#numpy">Numerical & Scientific Programming with Python (<code>numpy</code>, <code>scipy</code>)</a>
</h1>
<h2>
<a id="february-4-10-amnoon" class="anchor" href="#february-4-10-amnoon" aria-hidden="true"><span class="octicon octicon-link"></span></a>February 4, 10 am–noon</h2>
<p>We will use <a href="http://jupyter.org/">Jupyter</a> notebooks (formerly <a href="http://ipython.org/">I-Python</a>), which are interactive worksheets for code. To open these, please navigate on the command line to your home directory (or wherever your downloaded <code>ipynb</code> files are located), and open the Jupyter notebook interface:</p>
<pre><code>cd
module load canopy
ipython notebook
</code></pre>
<h5>
<a id="lesson-workbooks" class="anchor" href="#lesson-workbooks" aria-hidden="true"><span class="octicon octicon-link"></span></a>Lesson Workbooks</h5>
<ul>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/numpy-scipy-working.ipynb">Blank workbook</a> (please download)</li>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/numpy-scipy.ipynb">Full workbook</a> (use for your later reference)</li>
<li>
<a href="http://nbviewer.ipython.org/github/uiuc-cse/python-sp15/blob/gh-pages/lessons/numpy-scipy.ipynb">Static view of workbook</a> (if you don't have Python installed)</li>
</ul>
<h1>
<a id="data-analysis-with-python-pandas" class="anchor" href="#data-analysis-with-python-pandas" aria-hidden="true"><span class="octicon octicon-link"></span></a><a href="#pandas">Data Analysis with Python (<code>pandas</code>)</a>
</h1>
<h2>
<a id="february-11-10-amnoon" class="anchor" href="#february-11-10-amnoon" aria-hidden="true"><span class="octicon octicon-link"></span></a>February 11, 10 am–noon</h2>
<p>This lesson will introduce the basics of the <a href="http://pandas.pydata.org/"><code>pandas</code></a> module, a popular library for interacting with data and discovering trends.</p>
<p>We will use <a href="http://jupyter.org/">Jupyter</a> notebooks (formerly <a href="http://ipython.org/">I-Python</a>), which are interactive worksheets for code. To open these, please navigate on the command line to your home directory (or wherever your downloaded <code>ipynb</code> files are located), and open the Jupyter notebook interface:</p>
<pre><code>cd
module load canopy
ipython notebook
</code></pre>
<h5>
<a id="lesson-workbooks-1" class="anchor" href="#lesson-workbooks-1" aria-hidden="true"><span class="octicon octicon-link"></span></a>Lesson Workbooks</h5>
<ul>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/pandas-working.ipynb">Blank workbook</a> (please download)</li>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/pandas.ipynb">Full workbook</a> (use for your later reference)</li>
<li>
<a href="http://nbviewer.ipython.org/gist/glaksh100/81ed89adbe8a2b54314f">Static view of workbook</a> (if you don't have Python installed)</li>
</ul>
<h1>
<a id="plotting-in-python-matplotlib" class="anchor" href="#plotting-in-python-matplotlib" aria-hidden="true"><span class="octicon octicon-link"></span></a><a href="#matplotlib">Plotting in Python (<code>matplotlib</code>)</a>
</h1>
<h2>
<a id="february-18-10-amnoon" class="anchor" href="#february-18-10-amnoon" aria-hidden="true"><span class="octicon octicon-link"></span></a>February 18, 10 am–noon</h2>
<p>We will discuss MatPlotLib, Seaborn, and principles for making your Python data plots expressive and professional.</p>
<h5>
<a id="lesson-workbooks-2" class="anchor" href="#lesson-workbooks-2" aria-hidden="true"><span class="octicon octicon-link"></span></a>Lesson Workbooks</h5>
<ul>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/matplotlib-working.ipynb">Blank workbook</a> (please download)</li>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/matplotlib.ipynb">Full workbook</a> (use for your later reference)</li>
<li>
<a href="http://nbviewer.ipython.org/github/uiuc-cse/python-sp15/blob/gh-pages/lessons/matplotlib-executed.ipynb">Static view of workbook</a> (if you don't have Python installed)</li>
</ul>
<h1>
<a id="advanced-programming-in-python" class="anchor" href="#advanced-programming-in-python" aria-hidden="true"><span class="octicon octicon-link"></span></a><a href="#advanced">Advanced Programming in Python</a>
</h1>
<h2>
<a id="february-25-10-amnoon" class="anchor" href="#february-25-10-amnoon" aria-hidden="true"><span class="octicon octicon-link"></span></a>February 25, 10 am–noon</h2>
<p>We will cover more advanced Python topics such as classes and object-oriented programming, keyword arguments, package installation, and dynamic creation of variables at runtime.</p>
<h5>
<a id="lesson-workbooks-3" class="anchor" href="#lesson-workbooks-3" aria-hidden="true"><span class="octicon octicon-link"></span></a>Lesson Workbooks</h5>
<ul>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/oop-intro-working.ipynb">Blank workbook</a> (please download)</li>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/oop-intro.ipynb">Full workbook</a> (use for your later reference)</li>
<li>
<a href="http://nbviewer.ipython.org/gist/anonymous/7d09fc26b6e6a302920e">Static view of workbook</a> (if you don't have Python installed)</li>
</ul>
<h1>
<a id="machine-learning-in-python-scikit-learn" class="anchor" href="#machine-learning-in-python-scikit-learn" aria-hidden="true"><span class="octicon octicon-link"></span></a><a href="#sklearn">Machine Learning in Python (<code>scikit-learn</code>)</a>
</h1>
<h2>
<a id="march-4-10-amnoon" class="anchor" href="#march-4-10-amnoon" aria-hidden="true"><span class="octicon octicon-link"></span></a>March 4, 10 am–noon</h2>
<p>Using <code>scikit-learn</code>, we will explore machine learning principles such as clustering.</p>
<h5>
<a id="lesson-workbooks-4" class="anchor" href="#lesson-workbooks-4" aria-hidden="true"><span class="octicon octicon-link"></span></a>Lesson Workbooks</h5>
<ul>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/scikit-learn-working.ipynb">Blank workbook</a> (please download)</li>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/scikit-learn.ipynb">Full workbook</a> (use for your later reference)</li>
<li>
<a href="http://nbviewer.ipython.org/gist/glaksh100/42a65abe0d64eed286c7">Static view of workbook</a> (if you don't have Python installed)</li>
</ul>
<p><img src="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/img/satellite.png" alt=""></p>
<h1>
<a id="error-handling-in-python-pdb-numerical-error" class="anchor" href="#error-handling-in-python-pdb-numerical-error" aria-hidden="true"><span class="octicon octicon-link"></span></a><a href="#pdb">Error handling in Python (<code>pdb</code>, numerical error)</a>
</h1>
<h2>
<a id="march-11-10-amnoon" class="anchor" href="#march-11-10-amnoon" aria-hidden="true"><span class="octicon octicon-link"></span></a>March 11, 10 am–noon</h2>
<p>We will discuss the error tracebacks, debugging, and systematic sources of error.</p>
<h5>
<a id="lesson-workbooks-5" class="anchor" href="#lesson-workbooks-5" aria-hidden="true"><span class="octicon octicon-link"></span></a>Lesson Workbooks</h5>
<ul>
<li>
<p>Error workbook</p>
<ul>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/pdb-working.ipynb">Blank workbook</a> (please download)</li>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/pdb-full.ipynb">Full workbook</a> (use for your later reference)</li>
<li>
<a href="http://nbviewer.ipython.org/github/uiuc-cse/python-sp15/blob/gh-pages/lessons/pdb-full.ipynb">Static view of workbook</a> (if you don't have Python installed)</li>
</ul>
</li>
<li>
<p>Numerical Error workbook</p>
<ul>
<li><a href="https://raw.githubusercontent.com/uiuc-cse/hpc-sp15/gh-pages/lessons/numerical-error-working.ipynb">Blank copy</a></li>
<li><a href="https://raw.githubusercontent.com/uiuc-cse/hpc-sp15/gh-pages/lessons/numerical-error.ipynb">Full copy</a></li>
<li><a href="http://nbviewer.ipython.org/github/uiuc-cse/hpc-sp15/blob/gh-pages/lessons/numerical-error.ipynb">Static view</a></li>
</ul>
</li>
</ul>
<h1>
<a id="optimizing-numerical-code-in-python" class="anchor" href="#optimizing-numerical-code-in-python" aria-hidden="true"><span class="octicon octicon-link"></span></a><a href="#opt">Optimizing Numerical Code in Python</a>
</h1>
<h2>
<a id="march-18-10-amnoon" class="anchor" href="#march-18-10-amnoon" aria-hidden="true"><span class="octicon octicon-link"></span></a>March 18, 10 am–noon</h2>
<p>There are many ways to speed up your code in Python, including coupling it with C (<code>cython</code>) and Fortran (<code>f2py</code>) and using the popular <code>numba</code> optimization package.</p>
<ul>
<li>
<p>C/Fortran interface workbook</p>
<ul>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/api-blank.ipynb">Blank workbook</a> (please download)</li>
<li>
<a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/api.ipynb">Full workbook</a> (use for your later reference)</li>
<li>
<a href="http://nbviewer.ipython.org/gist/glaksh100/cf620d979e026a9a05b8">Static view of workbook</a> (if you don't have Python installed)</li>
</ul>
</li>
<li>
<p><a href="http://numba.pydata.org/">Numba</a> optimization workbook</p>
<ul>
<li><a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/numba-blank.ipynb">Blank workbook</a></li>
<li><a href="https://raw.githubusercontent.com/uiuc-cse/python-sp15/gh-pages/lessons/numba.ipynb">Full workbook</a></li>
<li><a href="http://nbviewer.ipython.org/gist/glaksh100/3af7e0b0b389abc0bd61">Static view of workbook</a></li>
</ul>
</li>
</ul>
<h1>
<a id="about-these-workshops" class="anchor" href="#about-these-workshops" aria-hidden="true"><span class="octicon octicon-link"></span></a>About These Workshops</h1>
<h3>
<a id="contributors" class="anchor" href="#contributors" aria-hidden="true"><span class="octicon octicon-link"></span></a>Contributors</h3>
<p>Neal Davis and Lakshmi Rao developed these materials. This content is available under a Creative Commons Attribution 4.0 Unported License.</p>
<p><img src="https://i.creativecommons.org/l/by/4.0/88x31.png" alt="CC-BY-4.0"></p>
<h1>
<a id="contact" class="anchor" href="#contact" aria-hidden="true"><span class="octicon octicon-link"></span></a>Contact</h1>
<p>If you have any questions about course availability, concepts, or content, please contact Neal Davis, Training Coördinator for Computational Science & Engineering, at training at cse dot illinois dot edu.</p>
</section>
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