gapdata/pythonplot.com

📈 Interactive comparison of Python plotting libraries for exploratory data analysis. Examples of using Pandas plotting, plotnine, Seaborn, and Matplotlib.

By Tim Hopper: tdhopper.com

An Introduction to Python Plotting for Exploratory Data Analysis

Available at pythonplot.com

Contributing

The site is generated from plots in the Examples.ipynb Python 3, Jupyter notebook.

You can create a Conda dev environment to run the notebook with make dev_environment.

Each plot in the notebook is tagged with metadata using Jupyter cell tags. You can view the cell tags in your notebook with View > Cell Toolbar > Tags.

The tags look like this:

ex
name:scatter-with-regression
package:ggplot

If you are using Jupyter lab, these are available through the Cell Tools left sidebar menu:

{
    "tags": [
        "ex",
        "name:scatter-with-regression",
        "package:ggplot"
    ]
}

ex identifies the cell as an example. The name tag corresponds to an item in the names dictionary in render.py. The package tag corresponds to a package in the packages dict in render.py.

The site layout current allows for ~46 characters in the code window. Please keep this in mind and wrap your code to avoid too much side scrolling.

At the moment, the code must return a png image into the output cell. To generate image files with plotly, using their server is currently the only way. Please see the getting started page for instructions on the .credentials file.

A Markdown comment can be added within triple quotes on the first line of the cell. (This currently doesn't work for R cells.)

You can render the images to web/img/plots by running $ make. Afterward, launch a local server (e.g. python -m http.server) from the web directory.