Plotting

In our final teaching session, we’re going to cover an absolutely essential topic: plotting. Plotting is a fundamental part of data analysis, so it’s crucial that you understand how to do it!

You might wonder, “why cant I just use Excel for plotting?” While Excel can be useful for simple plots, it has limitations when it comes to customization and reproducibility, and is incredibly bad at handling large datasets!

We’ll be using the Matplotlib package to do our plotting. This is a powerful and incredibly popular plotting library which provides a wide range of plotting functions and allows for extensive customization.

We’ll explore best-practices for plotting data, dispelling a few of the myths that you might have been taught at school, and We’ll also touch on how these plots can be properly included in a scientific report, ensuring that they are clear, informative, and well-integrated with the text.

Finally, we’ll use plotting to demonstrate a key data literacy skill - the ability to fit a straight line to data. This is a common task in chemistry, and is a great example of how plotting can be used to analyze and interpret data.