Color by Column Values in Matplotlib

Question

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 

Answer 1

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.

Answer 2

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()