Matplotlib style sheets

Matplotlib style sheet list

Matplotlib provides built-in style sheets to customize the appearance of the charts. You can list the available style sheets or themes importing matplotlib as plt and printing the following:

import matplotlib.pyplot as plt

print(plt.style.available)

The console will print the following style names:

['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh',
'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 
'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind',
'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 
'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel',
'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid',
'tableau-colorblind10']

Customizing matplotlib plots with styles

In order to set a matplotlib style you will need to use plt.style.use and select the desired theme. For instance, in the following block of code we are setting the 'Solarize_Light2' style.

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("Solarize_Light2")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Solarize_Light2 matplotlib style

plt.style.use will change the global styling for all plots.

Temporary or local style

It is important to note that using the previous code the styling will be applied not only to the first plot you create, but all the plots of the session. If you want to set a theme only for one chart or block of code you will need to use with plt.style.context("style_name"): as in the example below.

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

with plt.style.context("dark_background"):
  
  fig, ax = plt.subplots()
  x = np.linspace(-10, 10, 100)
  
  for i in range(0, 3):
    J = jv(i, x)
    ax.plot(x, J)

# plt.show()

Temporary or locally change a matplotlib style with plt.style.context

Recovering the matplotlib default style after setting a style sheet

Nonetheless, if you have already set a theme and want to recover the default matplotlib settings you can call mpl.rcParams.update(mpl.rcParamsDefault) after importing matplotlib as mpl or a hidden style named "default".

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv
import matplotlib as mpl

# Default settings
mpl.rcParams.update(mpl.rcParamsDefault)

plt.style.use("ggplot")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Recovering the matplotlib default style after setting a style sheet with mpl.rcParamsDefault

Style samples

In the following examples you will see the rest of the available styles:

_classic_test_patch

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("_classic_test_patch")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Classic test patch matplotlib style sheet

_mpl-gallery

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("_mpl-gallery")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

mpl gallery style sheet

_mpl-gallery-nogrid

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("_mpl-gallery-nogrid")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

mpl gallery no grid style in matplotlib

bmh

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("bmh")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

bmh style in matplotlib

classic

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("classic")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

classic plt style

fast

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("fast")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

fast matplotlib pyplot theme

fivethirtyeight

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("fivethirtyeight")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

fivethirtyeight style for matplotlib charts

grayscale

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("grayscale")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

matplotlib grayscale style sheet

seaborn

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Seaborn style in matplotlib

seaborn-bright

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-bright")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Seaborn bright theme in Python with matplotlib

seaborn-colorblind

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-colorblind")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Seaborn colorblind style sheet

seaborn-dark

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-dark")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Seaborn dark style with plt.style

seaborn-dark-palette

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-dark-palette")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Seaborn dark palette in matplotlib

seaborn-darkgrid

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-darkgrid")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-darkgrid style

seaborn-deep

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-deep")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-deep style sheet

seaborn-muted

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-deep")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-muted matplotlib style

seaborn-notebook

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-notebook")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-notebook stylesheet

seaborn-paper

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-paper")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Setting the seaborn-paper style in matplotlib

seaborn-pastel

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-pastel")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-pastel theme in Python

seaborn-poster

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-poster")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-poster stylesheet

seaborn-talk

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-talk")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-talk matplotlib style

seaborn-ticks

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-ticks")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-ticks theme

seaborn-white

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-white")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

matplotlib seaborn-white style

seaborn-whitegrid

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("seaborn-whitegrid")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

seaborn-whitegrid style in Python

tableau-colorblind10

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("tableau-colorblind10")

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

# plt.show()

Setting the tableau-colorblind10 style in matplotlib

Creating your own styles

It is possible to create your own styles or customize any style you want.

Customizing a style

Consider that you like the "fivethirtyeight" style, but you want to customize some of its features. In this scenario you can use rcParams to change the desired parameters as in the example below.

Create your own matplotlib style sheet

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv
import matplotlib as mpl

plt.style.use("fivethirtyeight")

mpl.rcParams['font.size'] = 5
mpl.rcParams['grid.color'] = "black"

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

plt.show()

Create a new style

If you want to create a new style you will need to create a .mplstyle file. For instance, you might want to create a style named mystyle with the styling of the following block of code and save it inside a folder named styles under your working environment, e.g. ./styles/mystyle.mplstyle. Recall to call mpl.rcParams for a list of params.

# mystyle.mplstyle

font.size: 5.0,
axes.facecolor: 'lavender',
lines.linewidth : 3
lines.linestyle: '-'
lines.color: 'red'

In this scenario, you can call the style with the following line:

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use("./styles/mystyle.mplstyle")

# Plot
# ...

Note that you can also call a style sheet from a URL.

Alternatively, you can save your style into mpl_configdir/stylelib. You will find where that folders are calling mpl.get_configdir() after importing matplotlib as mpl. If the folders doesn’t exist, you might need to create them. Then, you will be able to call your styles by their name, e.g. plt.style.use("mystyle").

More themes

If you don’t want to create a new style and all the built-in styles doesn’t fit your needs you will find lots of styles available online. Take a look for instance to this GitHub repository. Using one of these styles is straightforward, as you can input an URL as a style:

Example 1

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

plt.show()

Use an online matplotlib style sheet

Example 2

import numpy as np
import matplotlib.pyplot as plt
from scipy.special import jv

plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-dark.mplstyle')

fig, ax = plt.subplots()
x = np.linspace(-10, 10, 100)

for i in range(0, 3):
  J = jv(i, x)
  ax.plot(x, J)

plt.show()

Use a matplotlib style sheet from GitHub