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Exercises last minute updates Jul 9, 2018
PITCHME working on gitpitch Jun 6, 2018
datasets last minute updates Jul 9, 2018
.gitignore existing materials Oct 24, 2017 working on gitpitch Jun 6, 2018
PITCHME.yaml working on gitpitch Jun 6, 2018
plotting.ipynb Version 07/25/19 Jul 25, 2019
plotting_solutions.ipynb Add files via upload Jul 25, 2019 resource add Apr 26, 2019

General Info

General information about RCS Python Workshops can be found in the Python Workshops Repository. This includes information about software installations and general Python resources.

Visualization Overview

The pandas workshop covers plotting pandas data frames directly, but sometimes you need to go beyond the defaults, especially to make publication-quality graphics.

matplotlib is the core data visualization library in Python.

pandas plotting is built on matplotlib, as are additional visualization libraries like Seaborn. For R users, there's a version of ggplot for Python too. There are specialty toolkits that build on matplotlib for tasks like geospatial visualization, but many require extra software installations, so we don't use them here.

Bokeh is for interactive visualizations, as is Plotly.

There are other packages too.

Downloading Files

Recommended: Entire directory

You can download all of the files by clicking the green button above and choosing "Download ZIP."

Individual Files

If you download files from the links above, you have to click through to the RAW version of the notebook and download that. If you download directly from the links above, the files won't open because they are web pages, not the raw files.

To download exercise/workshop files, right-click on the links below, and choose Save Link As (or the similar option in your browser). Make sure to choose All file types as the content type, or remove any .txt or similar extensions from the file when you save it. The files should be *.ipynb files, with no additional file type extensions.

On a Mac, to open the files in Jupyter Notebook, start Jupyter Notebook from the folder where you saved the files. On Windows, navigate to the directory within Jupyter Notebook.

Workshop File; if you only download this file, you'll be missing some linked image files

Exercises WITHOUT Answers

Exercises WITH Answers


Some general Python resources that cover multiple topics can be found in our Python Resource List. Additional visualization-specific resources include:

matplotlib Tutorial from UC Boulder Research Computing

Python Plotting for Exploratory Data Analysis: examples of plots frequently used when exploring data

The Python Graph Gallery provides example plots with the code to make them; spans across different visualization libraries

Data Visualization: from Non-Coder to Coder by Alexis Cook; visualization tutorial/course aimed at those new to programming

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