Usage

The examples below plot vehicle fuel economy (in miles per gallon) versus horsepower for a dataset from the altair project.

Set marker color by the Year column and set the shape of the each marker according to the Origin column:

    from altair import load_dataset
    import matplotlib as mpl
    import matplotlib.style
    import matplotlib_helpers as mplh
    import matplotlib_helpers.chart

    # load data as a pandas DataFrame
    cars = load_dataset('cars')

    with mpl.style.context(['ggplot']):
        mplh.chart.encode(cars,
                          x='Horsepower',
                          y='Miles_per_Gallon',
                          shape='Year',
                          color='Origin', 
                          cell_size=5, fill=False)

Split plot into multiple subplots, with the subplot in each column corresponding to a distinct value in the Origin column.

The same type of handling can be applied using the row keyword.

    with mpl.style.context(['ggplot']):
        mplh.chart.encode(cars,
                          x='Horsepower',
                          y='Miles_per_Gallon',
                          color='Year',
                          shape='Year',
                          column='Origin', 
                          cell_size=5, fill=False)

By default, all plots share the same x axis scale and y axis scale. This behaviour can be changed by setting the sharexscale keyword argument or the shareyscale keyword argument.

For example, note that the subplots below all have different x axis and y axis scales.

    with mpl.style.context(['ggplot']):
        mplh.chart.encode(cars,
                          x='Horsepower',
                          y='Miles_per_Gallon',
                          color='Year',
                          shape='Year',
                          column='Origin', 
                          sharexscale=False,
                          shareyscale=False,
                          cell_size=5, fill=False)