Pandas – Why does the Seaborn color palette not work for Pandas bar plots

matplotlibpandasseaborn

An online Jupyter notebook demonstrating the code and showing the color differences is at:
https://anaconda.org/walter/pandas_seaborn_color/notebook

The colors are wrong when I make bar plots using Pandas dataframe method. Seaborn improves the color palette of matplotlib. All plots from matplotlib automatically use the new Seaborn palette. However, bar plots from Pandas dataframes revert to the non-Seaborn colors. This behavior is not consistent, because line plots from Pandas dataframes do use Seaborn colors. This makes my plots appear to be in different styles, even if I use Pandas for all my plots.

How can I plot using Pandas methods while getting a consistent Seaborn color palette?

I'm running this in python 2.7.11 using a conda environment with just the necessary packages for this code (pandas, matplotlib and seaborn).

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame({'y':[5,7,3,8]})

# matplotlib figure correctly uses Seaborn color palette
plt.figure()
plt.bar(df.index, df['y'])
plt.show()

# pandas bar plot reverts to default matplotlib color palette
df.plot(kind='bar')
plt.show()

# pandas line plots correctly use seaborn color palette 
df.plot()
plt.show()

Best Answer

Credit to @mwaskom for pointing to sns.color_palette(). I was looking for that but somehow I missed it hence the original mess with prop_cycle.

As a workaround you can set the color by hand. Note how the color keyword argument behaves differently if you are plotting one or several columns.

df = pd.DataFrame({'x': [3, 6, 1, 2], 'y':[5, 7, 3, 8]})

df['y'].plot(kind='bar', color=sns.color_palette(n_colors=1))

df.plot(kind='bar', color=sns.color_palette())

My original answer:

prop_cycle = plt.rcParams['axes.prop_cycle']
df['y'].plot(kind='bar', color=next(iter(prop_cycle))['color'])
df.plot(kind='bar', color=[x['color'] for x in prop_cycle])

Related Solutions

Pandas bar plot — specify bar color by column

You can pass a list as the colors. This will require a little bit of manual work to get it to line up, unlike if you could pass a dictionary, but may be a less cluttered way to accomplish your goal.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pds

data_files = ['a', 'b', 'c', 'd']

df1 = pds.DataFrame(np.random.rand(4,3), columns=data_files[:-1])
df2 = pds.DataFrame(np.random.rand(4,3), columns=data_files[1:])

color_list = ['b', 'g', 'r', 'c']


df1.plot(kind='bar', ax=plt.subplot(121), color=color_list)
df2.plot(kind='bar', ax=plt.subplot(122), color=color_list[1:])

plt.show()

enter image description here

EDIT Ajean came up with a simple way to return a list of the correct colors from a dictionary:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pds

data_files = ['a', 'b', 'c', 'd']
color_list = ['b', 'g', 'r', 'c']
d2c = dict(zip(data_files, color_list))

df1 = pds.DataFrame(np.random.rand(4,3), columns=data_files[:-1])
df2 = pds.DataFrame(np.random.rand(4,3), columns=data_files[1:])

df1.plot(kind='bar', ax=plt.subplot(121), color=map(d2c.get,df1.columns))
df2.plot(kind='bar', ax=plt.subplot(122), color=map(d2c.get,df2.columns))

plt.show()

Python – Seaborn plots not showing up

Plots created using seaborn need to be displayed like ordinary matplotlib plots. This can be done using the

plt.show()

function from matplotlib.

Originally I posted the solution to use the already imported matplotlib object from seaborn (sns.plt.show()) however this is considered to be a bad practice. Therefore, simply directly import the matplotlib.pyplot module and show your plots with

import matplotlib.pyplot as plt
plt.show()

If the IPython notebook is used the inline backend can be invoked to remove the necessity of calling show after each plot. The respective magic is

%matplotlib inline

Best Answer

Related Solutions

Pandas bar plot — specify bar color by column

Python – Seaborn plots not showing up

Related Topic