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()

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

import numpy 
import pandas
import seaborn as sns

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)
})

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

You can use the color parameter to the plot method to define the colors you want for each column. For example:

from pandas import DataFrame
data = DataFrame({'a':range(5),'b':range(1,6),'c':range(2,7)})
colors = ['yellowgreen','cyan','magenta']
data.plot(color=colors)

You can use color names or Color hex codes like '#000000' for black say. You can find all the defined color names in matplotlib's color.py file. Below is the link for the color.py file in matplotlib's github repo.

https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/colors.py

• This answer has been added because the question is canonical, and many users are seeking an answer for categorical or numeric data.
  • The OP is coloring by a categorical column, but this answer is for coloring by a column that is numeric, or can be interpreted as numeric, such as a datetime dtype.
• pandas.DataFrame.plot and matplotlib.pyplot.scatter can take a c or color parameter, which must be a color, a sequence of colors, or a sequence of numbers.
• Tested in python 3.8, pandas 1.3.1, and matplotlib 3.4.2
• Choosing Colormaps in Matplotlib for other valid cmap options.

Imports and Test Data

• 'Date' is already a datetime64[ns] dtype from DataReader
• conda install -c anaconda pandas-datareader or pip install pandas-datareader depending on your environment.

import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader as web  # for data; not part of pandas

tickers = 'amzn'
df = web.DataReader(ticker, data_source='yahoo', start='2018-01-01', end='2021-01-01').reset_index()
df['ticker'] = ticker

        Date        High          Low         Open        Close   Volume    Adj Close ticker
0 2018-01-02  1190.00000  1170.510010  1172.000000  1189.010010  2694500  1189.010010   amzn
1 2018-01-03  1205.48999  1188.300049  1188.300049  1204.199951  3108800  1204.199951   amzn

c as a number

pandas.DataFrame.plot

• df.Date.dt.month creates a pandas.Series of month numbers

ax = df.plot(kind='scatter', x='Date', y='High', c=df.Date.dt.month, cmap='Set3', figsize=(11, 4), title='c parameter as a month number')
plt.show()

matplotlib.pyplot.scatter

fig, ax = plt.subplots(figsize=(11, 4))
ax.scatter(data=df, x='Date', y='High', c=df.Date.dt.month, cmap='Set3')
ax.set(title='c parameter as a month number', xlabel='Date', ylabel='High')
plt.show()

c as a datetime dtype

pandas.DataFrame.plot

ax = df.plot(kind='scatter', x='Date', y='High', c='Date', cmap='winter', figsize=(11, 4), title='c parameter as a datetime dtype')
plt.show()

matplotlib.pyplot.scatter

fig, ax = plt.subplots(figsize=(11, 4))
ax.scatter(data=df, x='Date', y='High', c='Date', cmap='winter')
ax.set(title='c parameter as a datetime dtype', xlabel='Date', ylabel='High')
plt.show()

Though not matplotlib, you can achieve this using plotly express:

import numpy as np
import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Height':np.random.normal(size=10),
    'Weight':np.random.normal(size=10),
    'Size': 1,  # How large each point should be?
    'Gender': ["Male","Male","Male","Male","Male","Female","Female","Female","Female","Female"]})

# Create your plot
px.scatter(df, x='Weight', y='Height', size='Size', color='Gender')

If creating in a notebook, you'll get an interactive output like the following: