Coloring Scatter Plots by Column Values

In Python, the Matplotlib library provides several means of customizing scatter plot aesthetics. One common task is assigning colors based on values in a specific column.

Seaborn Integration

One solution is to leverage the Seaborn library, which builds upon Matplotlib. Seaborn offers high-level functions like sns.relplot and sns.FacetGrid that allow you to easily map scatter plots onto specific columns. By specifying the hue parameter, you can color points according to a third column containing category labels.

import seaborn as sns

sns.relplot(data=df, x='Weight (kg)', y='Height (cm)', hue='Gender')

Directly Using Matplotlib

Alternatively, you can directly use Matplotlib's plt.scatter function to create scatter plots and specify colors manually. This requires creating a custom color dictionary that maps category labels to colors.

def dfScatter(df, xcol='Height', ycol='Weight', catcol='Gender'):
    fig, ax = plt.subplots()

    categories = np.unique(df[catcol])
    colors = np.linspace(0, 1, len(categories))
    colordict = dict(zip(categories, colors))

    df['Color'] = df[catcol].apply(lambda x: colordict[x])
    ax.scatter(df[xcol], df[ycol], c=df.Color)
    return fig

By calling this function, you can generate a scatter plot colored by the specified category column:

df = pd.DataFrame({'Height': np.random.normal(size=10),
                   'Weight': np.random.normal(size=10),
                   'Gender': ["Male", "Male", "Unknown", "Male", "Male",
                              "Female", "Did not respond", "Unknown", "Female", "Female"]})
fig = dfScatter(df)