Motivation for the course¶
The main part of the first half of this course is to learn to program in Python. However, in addition to learning to program, we hope to help you learn a number of other skills related to open science. These include:
- Writing programs that are easy to understand and share
- Keeping a log of the changes you make to your programs
- Using programs that ensure your science is reproducible
- Producing simple, effective data visualizations that make your results easy to understand
To help clarify our goals, consider the example below.
Effective data visualization¶
One of the things we will learn in this course is how to use Python to plot data. As you well know, raw data itself is often not particularly useful in helping you understand what the data shows. Let’s look at an example that might be familiar to you, global temperature anomalies. And we can start with the data.
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-9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -62 -18 9 48 78 131 215 301 411 596 897 1245 1520 1698 1841 1994 2148 2300 2443 2570 2669 2734 2770 2797 2821 2840 2855 2862 2845 2814 2793 2779 2778 2762 2749 2745 2740 2719 2679 2627 2577 2532 2488 2447 2396 2348 2316 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -180 -180 -180 -180 -180 -180 -180 -180 -180 -180
...
Not that exciting, right? There is an interesting story here, but we need some way to illustrate the power of this data.
One option is to use an x-y plot of temperature anomalies versus time.
This is obviously much better, showing clearly how temperatures have changed with time and how global temperatures have increased significantly since 1970. Now we see a clear step toward making the data easier to understand. However, this is global data and we are missing something important about the data, its connection to geographical locations.
Let’s consider another option, plotting temperature anomalies on a map.
And yet again, this helps us understand the data further. Not only do we see the changed in temperature, but now we see how temperatures vary across the globe. The drawback here is that we only see a single time snapshot, rather than a time series. To see both will require a truly remarkable visualization.
So, let’s look now at an excellent example of data visualization with Python. We have essentially the same data plotted above, but now we can see how temperatures vary in space and time,
Global temperature anomalies by country from 1900-2016. Visualization by Antti Lipponen (@anttilip). Source: https://flic.kr/p/W3wPeE
This animated radial plot of temperature anomalies conveys a huge amount of information in a simple form. People can immediately understand what is plotted, and the combination of the plot format, colors and animation are very effective. What even better is the fact that this animation was made using Python!
For the rest of the first part of this course, plots like that above can be our inspiration. In fact, we will be working with similar data throughout this part of the course and may even end up producing similar plots in by the end of this teaching period.