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<meta name="author" content="Andreas Herten a.herten@fz-juelich.de" />
<title>JSC Pandas Course</title>
<link rel="icon" href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>🐼</text></svg>">
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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>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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<h1>Tutorial Material</h1>
<emph>TBD</emph>
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<li><a href="pandas-tutorial-tasks.tar.gz">Task bundle</a> (Notebooks, data)</li>
<li>Slides
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<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>
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<a href="mailto:a.herten@fz-juelich.de" rel="author">—Andreas Herten</a>
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