This exercise is used to reinforce key Python visualization skills. It offers practice with web-enabled Jupyter notebooks and lays some important foundations for presenting information obtained from mining the web and processing language. We'll use the Matplotlib Links to an external site. library. The Matplotlib library includes the pyplot module which provides a simple interface for building charts.

Anytime we use an external library (something not included in the Python Standard Library) like Matplotlib, we'll need to:

• install it into our active virtual environment and
• use an import statement in our Python.

1. Copy the base repository into your GitHub account by selecting the "Use this Template" button on GitHub and specifying yourself as the owner. The base repository is available at: https://github.com/wmnlp-materials/pyplot/blob/master/pyplot.ipynb Links to an external site.
2. Clone YOUR new repo down to your machine.
3. Open Notebook and complete the tasks.
4. Execute each notebook.
5. After executing, export each notebook to HTML.
6. Commit and push your HTML files to your GitHub repo along with the executed notebooks.
7. Verify you have a professional README.md that introduces your GitHub repository and provides helpful information about your project.

Each question is worth two points:

• Data plotted as described by the question (1 pt)
• Plot contains required elements (title, axis labels, axis titles, legend if required)