Python matplotlib 中的命名颜色
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Named colors in matplotlib
提问by T.C. Proctor
What named colors are available in matplotlib for use in plots? I can find a list on the matplotlib documentation that claims that these are the only names:
matplotlib 中有哪些命名颜色可用于绘图？我可以在 matplotlib 文档中找到一个列表，声称这些是唯一的名称：
b: blue
g: green
r: red
c: cyan
m: magenta
y: yellow
k: black
w: white
However, I've found that these colors can also be used, at least in this context:
但是，我发现也可以使用这些颜色，至少在这种情况下：
scatter(X,Y, color='red')
scatter(X,Y, color='orange')
scatter(X,Y, color='darkgreen')
but these are not on the above list. Does anyone know an exhaustive list of the named colors that are available?
但这些不在上面的列表中。有谁知道可用的命名颜色的详尽列表？
采纳答案by joelostblom
I constantly forget the names of the colors I want to use and keep coming back to this question =)
我经常忘记我想使用的颜色的名称，并不断回到这个问题 =)
The previous answers are great, but I find it a bit difficult to get an overview of the available colors from the posted image. I prefer the colors to be grouped with similar colors, so I slightly tweaked the matplotlib answerthat was mentioned in a comment above to get a color list sorted in columns. The order is not identical to how I would sort by eye, but I think it gives a good overview.
以前的答案很好，但我发现从发布的图像中获得可用颜色的概述有点困难。我更喜欢将颜色与相似的颜色分组，所以我稍微调整了上面评论中提到的matplotlib 答案，以获得按列排序的颜色列表。该顺序与我按眼睛排序的方式不同，但我认为它提供了一个很好的概述。
I updated the image and code to reflect that 'rebeccapurple' has been added and the three sage colors have been moved under the 'xkcd:' prefix since I posted this answer originally.
我更新了图像和代码以反映添加了“rebeccapurple”，并且自从我最初发布此答案以来，三种鼠尾草颜色已移到“xkcd：”前缀下。
I really didn't change much from the matplotlib example, but here is the code for completeness.
我真的没有从 matplotlib 示例中改变太多，但这里是完整性的代码。
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
# Sort colors by hue, saturation, value and name.
by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
for name, color in colors.items())
sorted_names = [name for hsv, name in by_hsv]
n = len(sorted_names)
ncols = 4
nrows = n // ncols
fig, ax = plt.subplots(figsize=(12, 10))
# Get height and width
X, Y = fig.get_dpi() * fig.get_size_inches()
h = Y / (nrows + 1)
w = X / ncols
for i, name in enumerate(sorted_names):
row = i % nrows
col = i // nrows
y = Y - (row * h) - h
xi_line = w * (col + 0.05)
xf_line = w * (col + 0.25)
xi_text = w * (col + 0.3)
ax.text(xi_text, y, name, fontsize=(h * 0.8),
horizontalalignment='left',
verticalalignment='center')
ax.hlines(y + h * 0.1, xi_line, xf_line,
color=colors[name], linewidth=(h * 0.8))
ax.set_xlim(0, X)
ax.set_ylim(0, Y)
ax.set_axis_off()
fig.subplots_adjust(left=0, right=1,
top=1, bottom=0,
hspace=0, wspace=0)
plt.show()
Additional named colors
其他命名颜色
Updated 2017-10-25. I merged my previous updates into this section.
2017 年 10 月 25 日更新。我将我以前的更新合并到本节中。
xkcd
xkcd
If you would like to use additional named colors when plotting with matplotlib, you can use the xkcd crowdsourced color names, via the 'xkcd:' prefix:
如果您想在使用 matplotlib 绘图时使用其他命名颜色，您可以使用xkcd 众包颜色名称，通过 'xkcd:' 前缀：
plt.plot([1,2], lw=4, c='xkcd:baby poop green')
Now you have access to a plethora of named colors!
现在您可以访问大量的命名颜色！
Tableau
表
The default Tableau colors are available in matplotlib via the 'tab:' prefix:
默认 Tableau 颜色可通过“tab:”前缀在 matplotlib 中使用：
plt.plot([1,2], lw=4, c='tab:green')
There are ten distinct colors:
有十种不同的颜色：
HTML
HTML
You can also plot colors by their HTML hex code:
您还可以通过HTML 十六进制代码绘制颜色：
plt.plot([1,2], lw=4, c='#8f9805')
This is more similar to specifying and RGB tuple rather than a named color (apart from the fact that the hex code is passed as a string), and I will not include an image of the 16 million colors you can choose from...
这更类似于指定和 RGB 元组而不是命名颜色（除了十六进制代码作为字符串传递的事实），我不会包括您可以选择的 1600 万种颜色的图像......
For more details, please refer to the matplotlib colors documentationand the source file specifying the available colors, _color_data.py.
有关更多详细信息，请参阅matplotlib 颜色文档和指定可用颜色的源文件_color_data.py.
回答by BoshWash
Matplotlib uses a dictionary from its colors.py module.
Matplotlib 使用来自它的 colors.py 模块的字典。
To print the names use:
要打印名称，请使用：
# python2:
import matplotlib
for name, hex in matplotlib.colors.cnames.iteritems():
print(name, hex)
# python3:
import matplotlib
for name, hex in matplotlib.colors.cnames.items():
print(name, hex)
This is the complete dictionary:
这是完整的字典：
cnames = {
'aliceblue': '#F0F8FF',
'antiquewhite': '#FAEBD7',
'aqua': '#00FFFF',
'aquamarine': '#7FFFD4',
'azure': '#F0FFFF',
'beige': '#F5F5DC',
'bisque': '#FFE4C4',
'black': '#000000',
'blanchedalmond': '#FFEBCD',
'blue': '#0000FF',
'blueviolet': '#8A2BE2',
'brown': '#A52A2A',
'burlywood': '#DEB887',
'cadetblue': '#5F9EA0',
'chartreuse': '#7FFF00',
'chocolate': '#D2691E',
'coral': '#FF7F50',
'cornflowerblue': '#6495ED',
'cornsilk': '#FFF8DC',
'crimson': '#DC143C',
'cyan': '#00FFFF',
'darkblue': '#00008B',
'darkcyan': '#008B8B',
'darkgoldenrod': '#B8860B',
'darkgray': '#A9A9A9',
'darkgreen': '#006400',
'darkkhaki': '#BDB76B',
'darkmagenta': '#8B008B',
'darkolivegreen': '#556B2F',
'darkorange': '#FF8C00',
'darkorchid': '#9932CC',
'darkred': '#8B0000',
'darksalmon': '#E9967A',
'darkseagreen': '#8FBC8F',
'darkslateblue': '#483D8B',
'darkslategray': '#2F4F4F',
'darkturquoise': '#00CED1',
'darkviolet': '#9400D3',
'deeppink': '#FF1493',
'deepskyblue': '#00BFFF',
'dimgray': '#696969',
'dodgerblue': '#1E90FF',
'firebrick': '#B22222',
'floralwhite': '#FFFAF0',
'forestgreen': '#228B22',
'fuchsia': '#FF00FF',
'gainsboro': '#DCDCDC',
'ghostwhite': '#F8F8FF',
'gold': '#FFD700',
'goldenrod': '#DAA520',
'gray': '#808080',
'green': '#008000',
'greenyellow': '#ADFF2F',
'honeydew': '#F0FFF0',
'hotpink': '#FF69B4',
'indianred': '#CD5C5C',
'indigo': '#4B0082',
'ivory': '#FFFFF0',
'khaki': '#F0E68C',
'lavender': '#E6E6FA',
'lavenderblush': '#FFF0F5',
'lawngreen': '#7CFC00',
'lemonchiffon': '#FFFACD',
'lightblue': '#ADD8E6',
'lightcoral': '#F08080',
'lightcyan': '#E0FFFF',
'lightgoldenrodyellow': '#FAFAD2',
'lightgreen': '#90EE90',
'lightgray': '#D3D3D3',
'lightpink': '#FFB6C1',
'lightsalmon': '#FFA07A',
'lightseagreen': '#20B2AA',
'lightskyblue': '#87CEFA',
'lightslategray': '#778899',
'lightsteelblue': '#B0C4DE',
'lightyellow': '#FFFFE0',
'lime': '#00FF00',
'limegreen': '#32CD32',
'linen': '#FAF0E6',
'magenta': '#FF00FF',
'maroon': '#800000',
'mediumaquamarine': '#66CDAA',
'mediumblue': '#0000CD',
'mediumorchid': '#BA55D3',
'mediumpurple': '#9370DB',
'mediumseagreen': '#3CB371',
'mediumslateblue': '#7B68EE',
'mediumspringgreen': '#00FA9A',
'mediumturquoise': '#48D1CC',
'mediumvioletred': '#C71585',
'midnightblue': '#191970',
'mintcream': '#F5FFFA',
'mistyrose': '#FFE4E1',
'moccasin': '#FFE4B5',
'navajowhite': '#FFDEAD',
'navy': '#000080',
'oldlace': '#FDF5E6',
'olive': '#808000',
'olivedrab': '#6B8E23',
'orange': '#FFA500',
'orangered': '#FF4500',
'orchid': '#DA70D6',
'palegoldenrod': '#EEE8AA',
'palegreen': '#98FB98',
'paleturquoise': '#AFEEEE',
'palevioletred': '#DB7093',
'papayawhip': '#FFEFD5',
'peachpuff': '#FFDAB9',
'peru': '#CD853F',
'pink': '#FFC0CB',
'plum': '#DDA0DD',
'powderblue': '#B0E0E6',
'purple': '#800080',
'red': '#FF0000',
'rosybrown': '#BC8F8F',
'royalblue': '#4169E1',
'saddlebrown': '#8B4513',
'salmon': '#FA8072',
'sandybrown': '#FAA460',
'seagreen': '#2E8B57',
'seashell': '#FFF5EE',
'sienna': '#A0522D',
'silver': '#C0C0C0',
'skyblue': '#87CEEB',
'slateblue': '#6A5ACD',
'slategray': '#708090',
'snow': '#FFFAFA',
'springgreen': '#00FF7F',
'steelblue': '#4682B4',
'tan': '#D2B48C',
'teal': '#008080',
'thistle': '#D8BFD8',
'tomato': '#FF6347',
'turquoise': '#40E0D0',
'violet': '#EE82EE',
'wheat': '#F5DEB3',
'white': '#FFFFFF',
'whitesmoke': '#F5F5F5',
'yellow': '#FFFF00',
'yellowgreen': '#9ACD32'}
You could plot them like this:
你可以像这样绘制它们：
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.colors as colors
import math
fig = plt.figure()
ax = fig.add_subplot(111)
ratio = 1.0 / 3.0
count = math.ceil(math.sqrt(len(colors.cnames)))
x_count = count * ratio
y_count = count / ratio
x = 0
y = 0
w = 1 / x_count
h = 1 / y_count
for c in colors.cnames:
pos = (x / x_count, y / y_count)
ax.add_patch(patches.Rectangle(pos, w, h, color=c))
ax.annotate(c, xy=pos)
if y >= y_count-1:
x += 1
y = 0
else:
y += 1
plt.show()
回答by Mathias711
In addition to BoshWash's answer, here is the picture generated by his code:
除了 BoshWash 的回答，这里是他的代码生成的图片：
回答by jnfran92
To get a full list of colors to use in plots:
要获取在绘图中使用的完整颜色列表：
import matplotlib.colors as colors
colors_list = list(colors._colors_full_map.values())
So, you can use in that way quickly:
因此，您可以通过这种方式快速使用：
scatter(X,Y, color=colors_list[0])
scatter(X,Y, color=colors_list[1])
scatter(X,Y, color=colors_list[2])
...
scatter(X,Y, color=colors_list[-1])
