Plot Styles

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Plot Styles露

Colors, font sizes, line thickness, and many other plot attributes all have default values in Matplotlib. In addition to the default style for these plot attributes, additional styles are available.

To use the default style, either don鈥檛 specify a style or use the line plt.style.use('default'). If you want to apply a different style to a plot use the line:

plt.style.use('style')

The 'style' is supplied to the method as a string, surrounded by quotes.

The code below builds a plot with 'fivethirtyeight' style, a style similar to the plots on fivethirtyeight.com.

import numpy as np import matplotlib.pyplot as plt # if using a Jupyter notebook, include: %matplotlib inline x = np.linspace(0, 10) fig, ax = plt.subplots() plt.style.use('fivethirtyeight') for n in range(-20,30,10): ax.plot(x, np.cos(x) + np.random.randn(50) + n) ax.set_title("'fivethirtyeight' style") plt.show()

There are many different styles available. You can list the available Matplotlib styles with the command:

for style in plt.style.available: print(style) Solarize_Light2 _classic_test_patch 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

The code section below displays a couple of Matplotlib鈥檚 available plot styles including 'default', 'seaborn' and Matplotlib鈥檚 older 'classic' style.

def pl(ax,st): x = np.linspace(0, 10) for n in range(-20,30,10): ax.plot(x, np.cos(x) + np.random.randn(50) + n) def his(ax): x = np.random.randn(50) ax.hist(x) def lb(ax,s): ax.text(0.2,0.5,s, fontsize=14) ax.xaxis.set_ticklabels([]) ax.yaxis.set_ticklabels([]) def make_fig(st): with plt.style.context(st): fig,[ax1,ax2,ax3] = plt.subplots(1,3,figsize=(9,1.5)) lb(ax1,st) his(ax2) pl(ax3,st) s = ['default','fivethirtyeight','seaborn', 'Solarize_Light2','classic'] for st in s: make_fig(st) plt.show()

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Plot Styles

Plot Styles

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