Demo: Visualize data with Python

A graph of GDP versus time for countries in Oceania.

This demo shows you how to use BadgerCompute for visualizing data using Python's matplotlib and pandas packages.

Part of these materials are adapted from the Software Carpentries's Plotting with Python lesson.

Prerequisites

Launch BadgerCompute and sign in using your NetID.
Choose the "Basic Data Science" environment. Click "Start".
In the left-hand sidebar (navigation), double-click the "work" folder to navigate to your work directory.
Create a new working directory by clicking the "New Folder" button. Name the directory "demo-python-plot". Double-click the newly created directory to navigate into it.
Click the large "+" button (or CTRL + SHIFT + L) to create a New Launcher page from the current directory.
At the bottom of the Launcher page, click "Terminal".
After the terminal launches, paste the command below to copy download the materials for the demo.
```
wget http://badgercompute.wisc.edu/notebooks/demo-python-plot.ipynb
wget https://swcarpentry.github.io/python-novice-gapminder/files/python-novice-gapminder-data.zip
unzip python-novice-gapminder-data.zip
```
In the navigation sidebar, you should see three new items:
1. data (directory)
2. python-novice-gapminder.zip
3. demo-python-plot.ipynb
In the navigation sidebar, double-click demo-python-plot.ipynb to open the notebook. We will be working in this notebook for the rest of the demo.

Read and follow the the rest of the guide in the notebook.

To execute code in cells (AKA "run cells"), there are multiple options.

To the left of the cell, you'll see brackets ([ ]).

After running a cell, you may see an asterisk in the brackets (e.g., [*]). This means the cell is currently running.
After the cell runs, you will see a number in the brackets (e.g., [1]). This means the cell has finished running. The number indicates the order in which the cells were run.

Still want to learn more?

Try plotting data from different .csv files.
Follow along the Software Carpentry's Plotting and Programming in Python workshop, which includes more examples and exercises.
Add interactive sliders, buttons, and more using Jupyter Widgets!