Color by Column Values in Matplotlib

One of my favorite aspects of using the ggplot2 library in R is the ability to easily specify aesthetics. I can quickly make a scatterplot and apply color associated with a specific column and I would love to be able to do this with python/pandas/matplotlib. I'm wondering if there are there any convenience functions that people use to map colors to values using pandas dataframes and Matplotlib?

##ggplot scatterplot example with R dataframe, `df`, colored by col3
ggplot(data = df, aes(x=col1, y=col2, color=col3)) + geom_point()

##ideal situation with pandas dataframe, 'df', where colors are chosen by col3
df.plot(x=col1,y=col2,color=col3)

EDIT: Thank you for your responses but I want to include a sample dataframe to clarify what I am asking. Two columns contain numerical data and the third is a categorical variable. The script I am thinking of will assign colors based on this value.

np.random.seed(250)
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"]})

     Height      Weight  Gender
0  5.824970  159.210508    Male
1  5.780403  180.294943    Male
2  6.318295  199.142201    Male
3  5.617211  157.813278    Male
4  6.340892  191.849944    Male
5  5.625131  139.588467  Female
6  4.950479  146.711220  Female
7  5.617245  121.571890  Female
8  5.556821  141.536028  Female
9  5.714171  134.396203  Female

Imports and Data

import numpy 
import pandas
import matplotlib.pyplot as plt
import seaborn
seaborn.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.

seaborn.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 np
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

if 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 np
import pandas as pd

df = 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()