class labels in Pandas scattermatrix

Question

This question has been asked before, Multiple data in scatter matrix, but didn't receive an answer.

I'd like to make a scatter matrix, something like in the pandas docs, but with differently colored markers for different classes. For example, I'd like some points to appear in green and others in blue depending on the value of one of the columns (or a separate list).

Here's an example using the Iris dataset. The color of the points represents the species of Iris -- Setosa, Versicolor, or Virginica.

Does pandas (or matplotlib) have a way to make a chart like that?

Answer 1

Update: This functionality is now in the latest version of Seaborn. Here's an example.

The following was my stopgap measure:

def factor_scatter_matrix(df, factor, palette=None):
    '''Create a scatter matrix of the variables in df, with differently colored
    points depending on the value of df[factor].
    inputs:
        df: pandas.DataFrame containing the columns to be plotted, as well 
            as factor.
        factor: string or pandas.Series. The column indicating which group 
            each row belongs to.
        palette: A list of hex codes, at least as long as the number of groups.
            If omitted, a predefined palette will be used, but it only includes
            9 groups.
    '''
    import matplotlib.colors
    import numpy as np
    from pandas.tools.plotting import scatter_matrix
    from scipy.stats import gaussian_kde

    if isinstance(factor, basestring):
        factor_name = factor #save off the name
        factor = df[factor] #extract column
        df = df.drop(factor_name,axis=1) # remove from df, so it 
        # doesn't get a row and col in the plot.

    classes = list(set(factor))

    if palette is None:
        palette = ['#e41a1c', '#377eb8', '#4eae4b', 
                   '#994fa1', '#ff8101', '#fdfc33', 
                   '#a8572c', '#f482be', '#999999']

    color_map = dict(zip(classes,palette))

    if len(classes) > len(palette):
        raise ValueError('''Too many groups for the number of colors provided.
We only have {} colors in the palette, but you have {}
groups.'''.format(len(palette), len(classes)))

    colors = factor.apply(lambda group: color_map[group])
    axarr = scatter_matrix(df,figsize=(10,10),marker='o',c=colors,diagonal=None)


    for rc in xrange(len(df.columns)):
        for group in classes:
            y = df[factor == group].icol(rc).values
            gkde = gaussian_kde(y)
            ind = np.linspace(y.min(), y.max(), 1000)
            axarr[rc][rc].plot(ind, gkde.evaluate(ind),c=color_map[group])

    return axarr, color_map

As an example, we'll use the same dataset as in the question, available here

>>> import pandas as pd
>>> iris = pd.read_csv('iris.csv')
>>> axarr, color_map = factor_scatter_matrix(iris,'Name')
>>> color_map
{'Iris-setosa': '#377eb8',
 'Iris-versicolor': '#4eae4b',
 'Iris-virginica': '#e41a1c'}

Hope this is helpful!