Color by Column Values in Matplotlib

Imports and Data

import numpy import pandasimport matplotlib.pyplot as pltimport seabornseaborn.set(style='ticks')numpy.random.seed(0)N = 37_genders= ['Female', 'Male', 'Non-binary', 'No Response']df = pandas.DataFrame({    'Height (cm)': numpy.random.uniform(low=130, high=200, size=N),    'Weight (kg)': numpy.random.uniform(low=30, high=100, size=N),    'Gender': numpy.random.choice(_genders, size=N)})

Update August 2021

• With seaborn 0.11.0, it's recommended to use new figure level functions like seaborn.relplot than to use FacetGrid directly.

sns.relplot(data=df, x='Weight (kg)', y='Height (cm)', hue='Gender', hue_order=_genders, aspect=1.61)plt.show()

Update October 2015

Seaborn handles this use-case splendidly:

• Map matplotlib.pyplot.scatter onto a seaborn.FacetGrid

fg = seaborn.FacetGrid(data=df, hue='Gender', hue_order=_genders, aspect=1.61)fg.map(plt.scatter, 'Weight (kg)', 'Height (cm)').add_legend()

Which immediately outputs:

Old Answer

In this case, I would use matplotlib directly.

import numpy as npimport matplotlib.pyplot as pltimport pandas as pddef 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 figif 1:    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)    fig.savefig('fig1.png')

And that gives me:

As far as I know, that color column can be any matplotlib compatible color (RBGA tuples, HTML names, hex values, etc).

I'm having trouble getting anything but numerical values to work with the colormaps.

Actually you could use ggplot for python:

from ggplot import *import numpy as npimport pandas as pddf = pd.DataFrame({'Height':np.random.randn(10),                   'Weight':np.random.randn(10),                   'Gender': ["Male","Male","Male","Male","Male",                              "Female","Female","Female","Female","Female"]})ggplot(aes(x='Height', y='Weight', color='Gender'), data=df)  + geom_point()

https://seaborn.pydata.org/generated/seaborn.scatterplot.html

import numpy import pandasimport seaborn as snsnumpy.random.seed(0)N = 37_genders= ['Female', 'Male', 'Non-binary', 'No Response']df = pandas.DataFrame({    'Height (cm)': numpy.random.uniform(low=130, high=200, size=N),    'Weight (kg)': numpy.random.uniform(low=30, high=100, size=N),    'Gender': numpy.random.choice(_genders, size=N)})sns.scatterplot(data=df, x='Height (cm)', y='Weight (kg)', hue='Gender')