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The 'hands-on” in the palette world-palettable
- Python当打之年forward
- 2023-08-10 15:13:391873browse
Some friends say that python visualization’s built-in colors are ugly, then you must not have encountered palettable , palettable is a colorbar (Colormap) library written in pure python, which brings together a large number of colorbars from well-known visualization software (such as Tableau color system, matplotlib partial color system, etc.). There are a total of 1587 colorbar (Colormap) categories that can be used. There are tens of thousands of single colors. This article details how to use palettable.
Contents of this article
1. Quick installation of palettable
pip install palettable -i https://pypi.tuna.tsinghua.edu.cn/simple
2. Quick use of palettable color bar (Colormap)
Import palettable package
import palettable from palettable.cartocolors.qualitative import Bold_9 #为了描述方便，此处直接倒入palettable.cartocolors.qualitative大类下的Bold_9小类， #实际使用时可直接用palettable.cartocolors.qualitative.Bold_9
palettable important properties-visualized chroma bar
Bold_9.show_discrete_image()#Bold_9各种颜色条图片
palettable important attribute-the number of single colors in the output chroma bar
print(Bold_9.number)#Bold_9这种colormap中单颜色的数目
9That is, the above picture has 9 cells
palettable important attributes-output the color number value of a single color in the chroma bar
print(Bold_9.colors)#Bold_9 colormap中每种颜色的RGB格式色号 print(Bold_9.hex_colors)#Bold_9 colormap中每种颜色的hex格式色号 print(Bold_9.mpl_colors)#RGB tuples in the range 0-1 as used by matplotlib
[[127, 60, 141], [17, 165, 121], [57 , 105, 172], [242, 183, 1], [231, 63, 116], [128, 186, 90], [230, 131, 16], [0, 134, 149], [207, 28 , 144]]
['#7F3C8D', '#11A579', '#3969AC', '#F2B701', '#E73F74', '#80BA5A', '#E68310', '#008695' . 0.003 92156862745098), (0.9058823529411765, 0.24705882352941178, 0.4549019607843137), (0.5019607843137255, 0.7294117647058823, 0.35294117647058826), (0.9019607843137255, 0.5137254901960784, 0.06274509803921569), (0.0, 0.5254901960784314, 0.5843137254901961), (0.8117647058823529, 0.10980392156862745, 0.56470588235294 12)]
Matplotlib中使用palettable
import matplotlib.pyplot as plt
import matplotlib as mpl
import palettable
mpl.rc_file_defaults()
my_dpi = 96
plt.figure(figsize=(580 / my_dpi, 580 / my_dpi), dpi=my_dpi)
plt.subplot(221)
patches, texts, autotexts = plt.pie(
x=[1, 2, 3],
labels=['A', 'B', 'C'],
#使用palettable.tableau.BlueRed_6
colors=palettable.tableau.BlueRed_6.mpl_colors[0:3],
autopct='%.2f%%',
explode=(0.1, 0, 0))
patches[0].set_alpha(0.3)
patches[2].set_hatch('|')
patches[1].set_hatch('x')
plt.title('tableau.BlueRed_6', size=12)
mpl.rc_file_defaults()
plt.subplot(222)
patches, texts, autotexts = plt.pie(
x=[1, 2, 3],
labels=['A', 'B', 'C'],
#使用palettable.cartocolors.qualitative.Bold_9
colors=palettable.cartocolors.qualitative.Bold_9.mpl_colors[0:3],
autopct='%.2f%%',
explode=(0.1, 0, 0))
patches[0].set_alpha(0.3)
patches[2].set_hatch('|')
patches[1].set_hatch('x')
plt.title('cartocolors.qualitative.Bold_9', size=12)
mpl.rc_file_defaults()
plt.subplot(223)
patches, texts, autotexts = plt.pie(
x=[1, 2, 3],
labels=['A', 'B', 'C'],
#使用palettable.cartocolors.qualitative.Bold_9
colors=palettable.cartocolors.qualitative.Bold_9.mpl_colors[0:3],
autopct='%.2f%%',
explode=(0.1, 0, 0))
patches[0].set_alpha(0.3)
patches[2].set_hatch('|')
patches[1].set_hatch('x')
plt.title('cartocolors.qualitative.Bold_9', size=12)
plt.subplot(223)
patches, texts, autotexts = plt.pie(
x=[1, 2, 3],
labels=['A', 'B', 'C'],
#使用palettable.lightbartlein.sequential.Blues10_5
colors=palettable.lightbartlein.sequential.Blues10_5.mpl_colors[0:3],
autopct='%.2f%%',
explode=(0.1, 0, 0))
#matplotlib.patches.Wedge
patches[0].set_alpha(0.3)
patches[2].set_hatch('|')
patches[1].set_hatch('x')
plt.title('lightbartlein.sequential.Blues10_5', size=12)
plt.subplot(224)
patches, texts, autotexts = plt.pie(
x=[1, 2, 3],
labels=['A', 'B', 'C'],
colors=palettable.wesanderson.Moonrise5_6.mpl_colors[0:3],
autopct='%.2f%%',
explode=(0.1, 0, 0))
patches[0].set_alpha(0.3)
patches[2].set_hatch('|')
patches[1].set_hatch('x')
plt.title('wesanderson.Moonrise5_6', size=12)
plt.show()Seaborn中使用palettable
例子来自几行代码绘制靓丽矩阵图
使用palettable.tableau.TrafficLight_9
import seaborn as sns
iris_sns = sns.load_dataset("iris")
import palettable
g = sns.pairplot(
iris_sns,
hue='species',
palette=palettable.tableau.TrafficLight_9.mpl_colors, #Matplotlib颜色
)
sns.set(style='whitegrid')
g.fig.set_size_inches(10, 8)
sns.set(style='whitegrid', font_scale=1.5)
使用palettable.tableau.BlueRed_6
使用palettable.cartocolors.qualitative.Bold_9
使用palettable.wesanderson.Moonrise5_6
使用palettable.cartocolors.diverging.ArmyRose_7_r
3、palettable包含那些颜色条(Colormap)
palettable下面有16大类Colormap，共计1587小类Colormap，合计上万种单颜色可供使用，已经整理为pdf格式，感兴趣的可以
包含以下16大类
palettable.cartocolors.diverging palettable.cartocolors.qualitative palettable.cartocolors.sequential palettable.cmocean.diverging palettable.cmocean.sequential palettable.colorbrewer.diverging palettable.colorbrewer.qualitative palettable.colorbrewer.sequential palettable.lightbartlein.diverging palettable.lightbartlein.sequential palettable.scientific.diverging palettable.scientific.sequential palettable.matplotlib palettable.mycarta palettable.tableau palettable.wesanderson
共计1587小类【每个小类还有逆类，名称后面加“_r”即可】上面16大类下面有数个小类，例如，著名BI软件Tableau的配色条palettable.tableau这一大类，下面有palettable.tableau.BlueRed_12，palettable.tableau.GreenOrange_12等等数个小类：
palettable.tableau.BlueRed_12 palettable.tableau.BlueRed_6 palettable.tableau.ColorBlind_10 palettable.tableau.Gray_5 palettable.tableau.GreenOrange_12 palettable.tableau.GreenOrange_6 palettable.tableau.PurpleGray_12 palettable.tableau.PurpleGray_6 palettable.tableau.TableauLight_10 palettable.tableau.TableauMedium_10 palettable.tableau.Tableau_10 palettable.tableau.Tableau_20 palettable.tableau.TrafficLight_9
也就是类似上面的这种有1587行。
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