Course Description
Matplotlib is a powerful and popular library for creating data visualizations in Python. Matplotlib makes it easy to develop publication quality plots with just a few lines of code, while also allowing full control of each visual element. The session will be a basic introduction to data visualization with Matplotlib, covering common plot types, essential concepts and useful tips. Participants will have the hands-on experience to write, modify and execute visualization scripts. This session will hopefully be helpful to researchers at various stages of their projects, especially those looking at facilitating their research with the power of Python.

Topics to be covered
Common plot types: line graphs, bar plots, scatter plots and histograms
Basic concepts: elements of a figure, interfaces and backends
Customizing plots: color, shape, annotations, and panels
Reproducible visualization: examples and best practices

Learning Objectives
Visualize results of simple scientific data with Matplotlib
Learn about basic data manipulations
Make figures for publications and presentations
Write scripts to automate the visualization of standardized datasets

Prior knowledge required
Basic Python programming skills

Software required
Python 3 with Matplotlib, NumPy and Pandas libraries
Jupyter Notebook
Installation through Anaconda (www.anaconda.com) is recommended.

Pre course work
Basic Python tutorial www.learnpython.org (recommended for participants more familiar with other languages such as R)

Jupyter Notebook introduction: realpython.com/jupyter-notebook-introduction
Please install the required software before the session.