Seaborn configuration hides default matplotlib

问题:

Seaborn provides of a handful of graphics which are very interesting for scientifical data representation. Thus I started using these Seaborn graphics interspersed with other customized matplotlib plots. The problem is that once I do:

import seaborn as sb

This import seems to set the graphic parameters for seaborn globally and then all matplotlib graphics below the import get the seaborn parameters (they get a grey background, linewithd changes, etc, etc).

In SO there is an answer explaining how to produce seaborn plots with matplotlib configuration, but what I want is to keep the matplotlib configuration parameters unaltered when using both libraries together and at the same time be able to produce, when needed, original seaborn plots.

回答1:

As of seaborn version 0.8 (july 2017) the graph style is not altered anymore on import. The OP wish is now the default behaviour. From https://seaborn.pydata.org/whatsnew.html:

The default (seaborn) style is no longer applied when seaborn is imported. It is now necessary to explicitly call set() or one or more of set_style(), set_context(), and set_palette(). Correspondingly, the seaborn.apionly module has been deprecated.

You can choose the style of any plot with plt.style.use().

import matplotlib.pyplot as plt
import seaborn as sns

plt.style.use('seaborn')#switch to seaborn style
#plot code

plt.style.use('default')#switches back to matplotlib style
#plot code

#To see all available styles
print(plt.style.available)

More on plt.style() here

回答2:

If you never want to use the seaborn style, but do want some of the seaborn functions, you can import seaborn using this following line (documentation):

import seaborn.apionly as sns

If you want to produce some plots with the seaborn style and some without, in the same script, you can turn the seaborn style off using the seaborn.reset_orig function.

It seems that doing the apionly import essentially sets reset_orig automatically on import, so its up to you which is most useful in your use case.

Here's an example of switching between matplotlib defaults and seaborn:

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

# a simple plot function we can reuse (taken from the seaborn tutorial)
def sinplot(flip=1):
    x = np.linspace(0, 14, 100)
    for i in range(1, 7):
        plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)

sinplot()

# this will have the matplotlib defaults
plt.savefig('seaborn-off.png')
plt.clf()

# now import seaborn
import seaborn as sns

sinplot()

# this will have the seaborn style
plt.savefig('seaborn-on.png')
plt.clf()

# reset rc params to defaults
sns.reset_orig()

sinplot()

# this should look the same as the first plot (seaborn-off.png)
plt.savefig('seaborn-offagain.png')

which produces the following three plots:

seaborn-off.png:

seaborn-on.png:

seaborn-offagain.png:

回答3:

You may use the matplotlib.style.context functionality as described in the style guide.

#%matplotlib inline #if used in jupyter notebook
import matplotlib.pyplot as plt
import seaborn as sns

# 1st plot 
with plt.style.context("seaborn-dark"):
    fig, ax = plt.subplots()
    ax.plot([1,2,3], label="First plot (seaborn-dark)")

# 2nd plot 
with plt.style.context("default"):
    fig, ax = plt.subplots()
    ax.plot([3,2,1], label="Second plot (matplotlib default)")

#  3rd plot 
with plt.style.context("seaborn-darkgrid"):
    fig, ax = plt.subplots()
    ax.plot([2,3,1], label="Third plot (seaborn-darkgrid)")

回答4:

Restore all RC params to original settings (respects custom rc) is allowed by seaborn.reset_orig() function

回答5:

As explained in this other question you can import seaborn with:

import seaborn.apionly as sns

And the matplotlib styles will not be modified.