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<h1>Data Analysis and Plotting in Python with Pandas</h1>
<h2>FZJ/JSC Training Course</h2>
<div clsas="sidenote">
- <p>Occasion 1: Thursday, 27 May 2021, 9:00 - 12:30, <strong>online</strong></p>
- <p>Occasion 2: Wednesday, 2 June 2021, 9:00 - 12:30, <strong>online</strong></p>
+ <p class="active">Occasion 1: Thursday, 27 May 2021, 9:00 - 12:30, <strong>online</strong></p>
+ <p class="inactive">Occasion 2: Wednesday, 2 June 2021, 9:00 - 12:30, <strong>online</strong></p>
<p>Pandas solves the full stack of data analysis in Python; reading-in of data, mangling and manipulation, analysis, and visualisation (and much more, actually). It builds up on established Python packages and can be used interchangeably with them (like Numpy, matplotlib); it fits perfectly into the Jupyter Notebooks workflow of modern-day data analysis. 🐼</p>
<p>Setup: Lecture/hands-on sessions with prepared Jupyter Notebooks</p>
<p><strong>Video Conference</strong>: <a href="https://webconf.fz-juelich.de/b/and-dpz-oxw-rwc">webconf.fz-juelich.de/b/and-dpz-oxw-rwc</a></p>
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</header>
<section id="main">
<h1>Tutorial Material</h1>
- <emph>TBD</emph>
- <!--
<ul>
<li><a href="pandas-tutorial-tasks.tar.gz">Task bundle</a> (Notebooks, data)</li>
<li>Slides
<ul>
<li><a href="Introduction-to-Pandas--slides.pdf">PDF</a> (exported from HTML presentation; imperfect)</li>
<li><a href="Introduction-to-Pandas--slides.ipynb">Jupyter Notebook</a></li>
- <li><a href="pandas-tutorial-slides.tar.gz">HTML presentation bundle</a></li>
+ <li><a href="pandas-tutorial-slides-bundle.tar.gz">HTML presentation bundle</a></li>
</ul>
</li>
</ul>
- -->
</section>
<footer>
<a href="mailto:a.herten@fz-juelich.de" rel="author">—Andreas Herten</a>