This question already has answers here:
How can I use seaborn without changing the matplotlib defaults?
(2 answers)
Closed 3 years ago.
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.
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:
As of seaborn version 0.8 (July 2017) the graph style is not altered anymore on import:
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)
Read more about plt.style().
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)")
Restore all RC params to original settings (respects custom rc) is allowed by seaborn.reset_orig() function
As explained in this other question you can import seaborn with:
import seaborn.apionly as sns
And the matplotlib styles will not be modified.
Related
Here's how to create a "stateful" plot in matplotlib and show it in non-interactive mode:
import matplotlib.pyplot as plt
plt.plot([1,2,8])
plt.show()
I am more interested in the "stateless" approach as I wish to embed matplotlib in my own python library. The same plot can be constructed "statelessly" as follows:
from matplotlib.figure import Figure
fig = Figure()
ax = fig.subplots()
lines = ax.plot([1,2,8])
However I don't know how to show it without resorting to pyplot , which I don't want to do as I would like to build up my own display mechanism.
How do I show the figure without resorting to pyplot?
While doing some practice problems using seaborn and a Jupyter notebook, I realized that the distplot() graphs did not have the darker outlines on the individual bins that all of the sample graphs in the documentation have. I tried creating the graphs using Pycharm and noticed the same thing. Thinking it was a seaborn problem, I tried some hist() charts using matplotlib, only to get the same results.
import matplotlib.pyplot as plt
import seaborn as sns
titanic = sns.load_dataset('titanic')
plt.hist(titanic['fare'], bins=30)
yielded the following graph:
Finally I stumbled across the 'edgecolor' parameter on the plt.hist() function, and setting it to black did the trick. Unfortunately I haven't found a similar parameter to use on the seaborn distplot() function, so I am still unable to get a chart that looks like it should.
I looked into changing the rcParams in matplotlib, but I have no experience with that and the following script I ran seemed to do nothing:
import matplotlib as mpl
mpl.rcParams['lines.linewidth'] = 1
mpl.rcParams['lines.color'] = 'black'
mpl.rcParams['patch.linewidth'] = 1
mpl.rcParams['patch.edgecolor'] = 'black'
mpl.rcParams['axes.linewidth'] = 1
mpl.rcParams['axes.edgecolor'] = 'black'
I was just kind of guessing at the value I was supposed to change, but running my graphs again showed no changes.
I then attempted to go back to the default settings using mpl.rcdefaults()
but once again, no change.
I reinstalled matplotlib using conda but still the graphs look the same. I am running out of ideas on how to change the default edge color for these charts. I am running the latest versions of Python, matplotlib, and seaborn using the Conda build.
As part of the update to matplotlib 2.0 the edges on bar plots are turned off by default. However, you may use the rcParam
plt.rcParams["patch.force_edgecolor"] = True
to turn the edges on globally.
Probably the easiest option is to specifically set the edgecolor when creating a seaborn plot, using the hist_kws argument,
ax = sns.distplot(x, hist_kws=dict(edgecolor="k", linewidth=2))
For matplotlib plots, you can directly use the edgecolor or ec argument.
plt.bar(x,y, edgecolor="k")
plt.hist(x, edgecolor="k")
Equally, for pandas plots,
df.plot(kind='hist',edgecolor="k")
A complete seaborn example:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x = np.random.randn(100)
ax = sns.distplot(x, hist_kws=dict(edgecolor="k", linewidth=2))
plt.show()
As of Mar, 2021 :
sns.histplot(data, edgecolor='k', linewidth=2)
work.
Using hist_kws=dict(edgecolor="k", linewidth=2) gave an error:
AttributeError: 'PolyCollection' object has no property 'hist_kws'
Using the available styles in seaborn could also solve your problem.
Available styles in seaborn are :
ticks
dark
darkgrid
white
whitegrid
I'm using the following in a Jupyter notebook, using the latest Anaconda update (including Matplotlib 3.1.1,)
Thanks to SpghttCd, I have the code to do a stacked horizontal bar, but Seaborn puts it on a new plot below the default one.
How might I best fix this problem?
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data=pd.DataFrame(data={"R1":["Yes","Yes","Yes","No","No"]})
freq = data["R1"].value_counts(normalize=True)*100
fig,ax = plt.subplots()
freq.to_frame().T.plot.barh(stacked=True)
You see two axes in Jupyter because you create a fresh one with plt.subplots() and pandas also creates another one.
If you need to reuse an existing axe, pass it to plotting method using ax switch:
fig, axe = plt.subplots()
freq.to_frame().T.plot.barh(stacked=True, ax=axe)
See pandas documentation for details, plotting method always exhibits an ax switch:
ax : Matplotlib axis object, optional
If you accept pandas creates it for you, as #Bharath M suggested, just issue:
axe = freq.to_frame().T.plot.barh(stacked=True)
Then you will see an unique axes and you can access it trough the variable axe.
While doing some practice problems using seaborn and a Jupyter notebook, I realized that the distplot() graphs did not have the darker outlines on the individual bins that all of the sample graphs in the documentation have. I tried creating the graphs using Pycharm and noticed the same thing. Thinking it was a seaborn problem, I tried some hist() charts using matplotlib, only to get the same results.
import matplotlib.pyplot as plt
import seaborn as sns
titanic = sns.load_dataset('titanic')
plt.hist(titanic['fare'], bins=30)
yielded the following graph:
Finally I stumbled across the 'edgecolor' parameter on the plt.hist() function, and setting it to black did the trick. Unfortunately I haven't found a similar parameter to use on the seaborn distplot() function, so I am still unable to get a chart that looks like it should.
I looked into changing the rcParams in matplotlib, but I have no experience with that and the following script I ran seemed to do nothing:
import matplotlib as mpl
mpl.rcParams['lines.linewidth'] = 1
mpl.rcParams['lines.color'] = 'black'
mpl.rcParams['patch.linewidth'] = 1
mpl.rcParams['patch.edgecolor'] = 'black'
mpl.rcParams['axes.linewidth'] = 1
mpl.rcParams['axes.edgecolor'] = 'black'
I was just kind of guessing at the value I was supposed to change, but running my graphs again showed no changes.
I then attempted to go back to the default settings using mpl.rcdefaults()
but once again, no change.
I reinstalled matplotlib using conda but still the graphs look the same. I am running out of ideas on how to change the default edge color for these charts. I am running the latest versions of Python, matplotlib, and seaborn using the Conda build.
As part of the update to matplotlib 2.0 the edges on bar plots are turned off by default. However, you may use the rcParam
plt.rcParams["patch.force_edgecolor"] = True
to turn the edges on globally.
Probably the easiest option is to specifically set the edgecolor when creating a seaborn plot, using the hist_kws argument,
ax = sns.distplot(x, hist_kws=dict(edgecolor="k", linewidth=2))
For matplotlib plots, you can directly use the edgecolor or ec argument.
plt.bar(x,y, edgecolor="k")
plt.hist(x, edgecolor="k")
Equally, for pandas plots,
df.plot(kind='hist',edgecolor="k")
A complete seaborn example:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x = np.random.randn(100)
ax = sns.distplot(x, hist_kws=dict(edgecolor="k", linewidth=2))
plt.show()
As of Mar, 2021 :
sns.histplot(data, edgecolor='k', linewidth=2)
work.
Using hist_kws=dict(edgecolor="k", linewidth=2) gave an error:
AttributeError: 'PolyCollection' object has no property 'hist_kws'
Using the available styles in seaborn could also solve your problem.
Available styles in seaborn are :
ticks
dark
darkgrid
white
whitegrid
I am trying to display a complex signal in x and y and have the awesome interactive tools available from bokeh inside of an ipython notebook. In particular I would like to restrict the wheel zoom to the x axis, but I can't see how to do this after using mpl.to_bokeh(). Is there a way to set the default tools before using mpl.to_bokeh()?
For context here is a sample plot I would like to use:
import matplotlib.pyplot as plt
import bokeh.plotting as blt
from bokeh import mpl
from bokeh.plotting import show
blt.output_notebook()
import numpy as np
blt.figure(tools='xwheel_zoom') # this doesn't help
x= np.arange(100)/100
y= np.exp(1j*2*np.pi*x)
ax= plt.subplot(211)
plt.plot(x,y.real)
plt.subplot(212, sharex=ax)
plt.plot(x,y.imag)
fig= mpl.to_bokeh(name='subplots')
Unfortunately, doing this with the MPL compat layer would already be somewhat difficult with just a single plot. I am not sure there is currently any way at all to do it with a grid plot and MPL. However, it is pretty trivial to do if you use the bokeh API directly. In case that is an option for you, and it is helpful:
from bokeh.plotting import figure, gridplot, show
import numpy as np
x = np.arange(100)/100
y = np.exp(1j*2*np.pi*x)
p1 = figure(tools='xwheel_zoom')
p1.line(x,y.real)
p2 = figure(tools='xwheel_zoom')
p2.line(x,y.imag)
grid = gridplot([[p1, p2]])
show(grid)