Welcome to "If error: continue" – an introduction to the Python programming language.

In this course, you will learn something about

• the very basics of Python (syntax, variable types, etc.),
• data handling and manipulation with <b>pandas</b> and <b>numpy</b>,
• common visualization techniques using <b>matplotlib</b> and <b>ggplot</b>,
• tools for data analysis, particularly in the context of machine learning and text mining, and
• (hopefully) many other things that might come in handy one day.

Our overall aim is to provide you with a general guideline which should help you tackle a rather broad range of applications. Nonetheless, please keep in mind that this course is part of the MSc Physical Geography at Marburg University, and hence, you will regularly encounter case studies and scientific problems with explicit geographic relevance.

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Most of the Python (and sometimes even R) code presented herein is wrapped up in so-called <a href="https://jupyter.org/">Jupyter Notebooks</a>. This is simply to ensure an uncomplicated distribution of the code-based course material through our servers. At the same time, this technical concession is considered to offer a comfortable and flexible way for you to re-run existing code and, of course, test your own code snippets.

Please mind that Jupyter Notebooks cannot be merely understood as an interactive environment for re-running or testing code chunks. In fact, the interface may as well be used as a stand-alone integrated development environment (<a href="https://en.wikipedia.org/wiki/Integrated_development_environment">IDE</a>) for Python – principally the same like <a href="https://www.rstudio.com/">RStudio</a> for R. In general, it is up to you whether or not to carry out your coding work in an IDE or on the command line. As the coding assignments for this course are to be submitted as Jupyter Notebooks (<code>.ipynb</code>, i.e. Python code chunks intermingled with <a href="https://daringfireball.net/projects/markdown/">Markdown</a> text fields), however, we highly encourage you to start out with a proper IDE right away. Possible candidates, to which we have grown accustomed, include (but surely are not limited to)

• the R-Studio like <a href="https://www.yhat.com/products/rodeo">Rodeo</a> developed by <a href="https://www.yhat.com/">Yhat</a>,
• Microsoft's highly sophisticated <a href="https://www.visualstudio.com/en/vs/">Visual Studio</a>,
• or any other Python IDE (see for example this ranking of <a href="https://www.datacamp.com/community/tutorials/data-science-python-ide">Top 5 Python IDEs for Data Science</a> provided by <a href="https://www.datacamp.com/">DataCamp</a>).

<div align="center"><img src="https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python+IDEs/patolino-pernalonga-python-ide2.gif" alt="IDE" style="width:300px"><div class="caption">Source: <a href:"https://www.datacamp.com/community/tutorials/data-science-python-ide">Data Camp, 2017</a></div></div>
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