Color-Coding Scatter Plots by Column Values in Python

In data visualization, assigning colors to different categories can enhance clarity and reveal patterns. This functionality is readily available in ggplot2 for R, but how can we achieve the same in Python using pandas and matplotlib?

Update: Seaborn Enhancements

Since the original answer, Seaborn has emerged as a powerful library for creating informative and visually appealing plots. Its recent updates offer convenient functions for coloring scatter plots based on column values:

• Using seaborn.relplot: This high-level function combines aspects of matplotlib.pyplot.scatter and Seaborn's FacetGrid. It automatically handles color coding based on specified hue and order parameters.
• Mapping matplotlib.pyplot.scatter to seaborn.FacetGrid: Similar to the original approach, you can map the scatter function onto a FacetGrid and customize colors based on hue.

Original Pandas and Matplotlib Approach

For those seeking a direct approach with Matplotlib, here's a custom function that assigns colors to points based on a categorical column:

import matplotlib.pyplot as plt
import pandas as pd

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

This function creates a color dictionary from unique category values and assigns corresponding colors to data points. The scatter plot is then generated with color-coded points.

Example

Using the provided sample dataframe:

df = pd.DataFrame({'Height': np.append(np.random.normal(6, 0.25, size=5), np.random.normal(5.4, 0.25, size=5)),
                   'Weight': np.append(np.random.normal(180, 20, size=5), np.random.normal(140, 20, size=5)),
                   'Gender': ["Male", "Male", "Male", "Male", "Male",
                              "Female", "Female", "Female", "Female", "Female"]})

Calling the dfScatter function with the dataframe:

fig = dfScatter(df)
fig.savefig('color_coded_scatterplot.png')

Produces a scatter plot where points are colored by gender:

[Image of scatter plot colored by gender]

Seaborn's advanced features and the custom dfScatter function provide flexible options for adding color-coding to scatter plots in Python, making data visualization more informative and visually engaging.