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

Answers:

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Method 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.

Method 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()

Method 3

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')

Method 4

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

Method 5

• 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()

Method 6

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:

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0