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1. Color_palette () function

This function is the key function for seaborn to select color.

Color_palette () will accept the name of any seaborn palette or matplotlib colorma

Syntax: seaborn.color_palette (palette=None, n_colors=None, desat=None)

Import seaborn as snsimport matplotlib.pyplot as pltplt.figure (dpi=250) sns.palplot (sns.color_palette ()) # output default color

Print (sns.color_palette ()) # returns the list of the default color tuple

# palette, pass in the colormap name sns.palplot (sns.color_palette (palette='Accent')) # use the colormap in matplotlib

# n_colorssns.palplot (sns.color_palette (n_colors=21)) # returns the color category, which exceeds the automatic loop

# desatsns.palplot (sns.color_palette (n_colors=21, desat=0.2)) # set color saturation

# withplt.figure (dpi=100) with sns.color_palette (n_colors=21): # Recycle color disk _ = plt.plot (np.c_ [np.zeros (21), np.arange (21)] .T)

# pass the hex format color number to sns.color_paletteflatui = ["# 9b59b6", "# 3498db", "# 95a5a6", "# e74c3c", "# 34495e", "# 2ecc71"] sns.palplot (sns.color_palette (flatui))

# Color use plt.figure (dpi=100) plt.subplot (1mine2jue 1) plt.bar ([1dje 2je 3], [1je 2je 3], color=sns.color_palette () [0]) # take a color plt.subplot (1m 2m 2) plt.bar ([1m 2je 3], [1m 2je 3], color=sns.color_palette () [0:3]) # take three colors

2. Seaborn available color palette

There are three main categories: 'sequential' (gradient),' diverging' (indescribable, see below), and 'qualitative' (distinct colors)

Choose_colorbrewer_palette function

This function can preview a variety of color plates and can only be used in jupyter notebook.

The above three types of colors are described in detail below.

Qualitative color palettes

To distinguish discrete chunks of data that do not have an inherent ordering is divided into the following categories:

1 、 deep, muted, pastel, bright, dark, colorblind

2 、 hls

3 、 husl

4 、 palettable 5 、 xkcd

6. Input color list

# deep, muted, pastel, bright, dark, colorblindfor i in list ('deep, muted, pastel, bright, dark, colorblind'.split (','): print (sns.color_palette (palette=i))

From top to bottom are: deep, muted, pastel, bright, dark, colorblind

# hls sns.palplot (sns.color_palette (palette='hls')) sns.palplot (sns.hls_palette (8, lumb.3, sq.8))

# husl sns.palplot (sns.color_palette (palette='husl')) sns.palplot (sns.color_palette ("husl", 8))

Import palettable#python palettable library sns.palplot (sns.color_palette (palette=palettable.colorbrewer.qualitative.Dark2_7.mpl_colors)) # uses colormapsns.palplot in palettable (sns.color_palette (palette=palettable.scientific.sequential.Nuuk_7.mpl_colors))

# xkcdplt.plot ([0,1], [0,1], sns.xkcd_rgb ["pale red"], lw=3) plt.plot ([0,1], [0,2], sns.xkcd_rgb ["medium green"], lw=3) plt.plot ([0,1], [0,3], sns.xkcd_rgb ["denim blue"], lw=3)

Xkcd, for more information, please see: matplotlib built-in single color of Python visual learning

# pass color list to ns.xkcd_palette () colors = ["windows blue", "amber", "greyish", "faded green", "dusty purple"] sns.palplot (sns.xkcd_palette (colors))

Sequential color palettes

Is appropriate when data range from relatively low or uninteresting values to relatively high or interesting values

1. "Blues" and so on

2. 'cubehelix',seaborn.cubehelix_palette (n_colors=6, start=0, rot=0.4, gamma=1.0, hue=0.8, light=0.85, dark=0.15, reverse=False, as_cmap=False)

3. Gradient of traditional color, light_palette (), dark_palette ()

# sns.palplot (sns.color_palette ("Blues")) sns.palplot (sns.color_palette ("Blues_d")) # _ d, such as "Blues", indicates the dark color system ("dark" palettes by appending "d") sns.palplot (sns.color_palette ("Blues_r")) showing the color

# cubehelixsns.palplot (sns.color_palette ("cubehelix", 8)) sns.palplot (sns.color_palette ("ch:2.5,-.2,dark=.3")) # use cubehelix interface to make color sns.palplot (sns.cubehelix_palette (8, start=2, rot=0, dark=0, light=.95, reverse=True))

# light_palettesns.palplot (sns.light_palette ("seagreen", reverse=True)) sns.palplot (sns.light_palette ((260,75,60), input= "husl"))

Diverging color palettes

For data where both large low and high values are interesting.

1. Diverging_palette ()

Sns.palplot (sns.color_palette ("coolwarm", 7))

Sns.palplot (sns.diverging_palette (240,10,9) sns.palplot (sns.diverging_palette (150,275,80,55,9) sns.palplot (sns.diverging_palette (250,15,75,40,299, center= "dark")

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