blocks|key|258936|text|假设您的数据在一个二维数组中，这应该是可行的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|258937|import+numpy
import+pylab
xy+=+numpy.zeros((2,+1000))
xy[0]+=+range(1000)
xy[1]+=+range(1000)
colors+=+[int(i+%25+23)+for+i+in+xy[0]]
pylab.scatter(xy[0],+xy[1],+c=colors)
pylab.show()|code-block|syntax|javascript|258938|您还可以设置cmap属性，以控制将通过使用色彩映射表显示哪些颜色；例如，将pylab.scatter行替换为：|offset|length|style|CODE|258939|pylab.scatter(xy[0],+xy[1],+c=colors,+cmap=pylab.cm.cool)|258940|可以在here上找到色彩映射表的列表|258941|entityMap|0|LINK|mutability|MUTABLE|url|http://matplotlib.org/examples/color/colormaps_reference.html^0|0|0|6|4|11|D|0|0|3|4|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|10|8|@$I|11|J|12|K|L]|$I|13|J|14|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|15|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|16|8|@]|9|@$I|17|J|18|1|19]]|A|$]]|$1|Q|3|-4|5|6|7|1A|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X]]]]

Assuming that you have your data in a 2d array, this should work:

<pre><code>import numpy
import pylab
xy = numpy.zeros((2, 1000))
xy[0] = range(1000)
xy[1] = range(1000)
colors = [int(i % 23) for i in xy[0]]
pylab.scatter(xy[0], xy[1], c=colors)
pylab.show()
</code></pre>

You can also set a <code>cmap</code> attribute to control which colors will appear through use of a colormap; i.e. replace the <code>pylab.scatter</code> line with:

<pre><code>pylab.scatter(xy[0], xy[1], c=colors, cmap=pylab.cm.cool)
</code></pre>

A list of color maps can be found 
<a href="http://matplotlib.org/examples/color/colormaps_reference.html" rel="noreferrer">here</a>

blocks|key|475826|text|被接受的答案是正确的，但是如果您想要指定哪个类标签应该分配给特定的颜色或标签，您可以执行以下操作。我用颜色条做了一个小小的体操标签，但使情节本身变成了一个很好的一行。这对于绘制使用sklearn完成的分类结果非常有用。每个标签都匹配一个(x，y)坐标。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|475827|import+matplotlib
import+matplotlib.pyplot+as+plt
import+numpy+as+np

x+=+[4,8,12,16,1,4,9,16]
y+=+[1,4,9,16,4,8,12,3]
label+=+[0,1,2,3,0,1,2,3]
colors+=+['red','green','blue','purple']

fig+=+plt.figure(figsize=(8,8))
plt.scatter(x,+y,+c=label,+cmap=matplotlib.colors.ListedColormap(colors))

cb+=+plt.colorbar()
loc+=+np.arange(0,max(label),max(label)/float(len(colors)))
cb.set_ticks(loc)
cb.set_ticklabels(colors)|code-block|syntax|javascript|475828|​|475829|📷|atomic|offset|length|475830|475831|使用略微修改的this答案版本，可以将上述N种颜色概括如下：|475832|import+numpy+as+np
import+matplotlib+as+mpl
import+matplotlib.pyplot+as+plt

N+=+23+#+Number+of+labels

#+setup+the+plot
fig,+ax+=+plt.subplots(1,1,+figsize=(6,6))
#+define+the+data
x+=+np.random.rand(1000)
y+=+np.random.rand(1000)
tag+=+np.random.randint(0,N,1000)+#+Tag+each+point+with+a+corresponding+label++++

#+define+the+colormap
cmap+=+plt.cm.jet
#+extract+all+colors+from+the+.jet+map
cmaplist+=+[cmap(i)+for+i+in+range(cmap.N)]
#+create+the+new+map
cmap+=+cmap.from_list('Custom+cmap',+cmaplist,+cmap.N)

#+define+the+bins+and+normalize
bounds+=+np.linspace(0,N,N%2B1)
norm+=+mpl.colors.BoundaryNorm(bounds,+cmap.N)

#+make+the+scatter
scat+=+ax.scatter(x,y,c=tag,s=np.random.randint(100,500,N),cmap=cmap,+++++norm=norm)
#+create+the+colorbar
cb+=+plt.colorbar(scat,+spacing='proportional',ticks=bounds)
cb.set_label('Custom+cbar')
ax.set_title('Discrete+color+mappings')
plt.show()|475833|这就给出了：|475834|475835|475836|475837|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/f2ae74b57c4f724d44f11781d2b467a4.png|imageAlt|1|LINK|MUTABLE|url|https://stackoverflow.com/questions/14777066/matplotlib-discrete-colorbar|2|https://ask.qcloudimg.com/http-save/yehe-900000/29e083b96f57874824e584808334ac51.png^0|0|0|0|0|1|0|0|0|7|4|1|0|0|0|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|1D|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1E|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1F|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|1G|8|@]|9|@$L|1H|M|1I|1|1J]]|A|$]]|$1|N|3|H|5|6|7|1K|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1L|8|@]|9|@$L|1M|M|1N|1|1O]]|A|$]]|$1|Q|3|R|5|D|7|1P|8|@]|9|@]|A|$E|F]]|$1|S|3|T|5|6|7|1Q|8|@]|9|@]|A|$]]|$1|U|3|H|5|6|7|1R|8|@]|9|@]|A|$]]|$1|V|3|J|5|K|7|1S|8|@]|9|@$L|1T|M|1U|1|1V]]|A|$]]|$1|W|3|H|5|6|7|1W|8|@]|9|@]|A|$]]|$1|X|3|-4|5|6|7|1X|8|@]|9|@]|A|$]]]|Y|$Z|$5|10|11|12|A|$13|14|15|-4]]|16|$5|17|11|18|A|$19|1A]]|1B|$5|10|11|12|A|$13|1C|15|-4]]]]

The accepted answer has it spot on, but if you might want to specify which class label should be assigned to a specific color or label you could do the following. I did a little label gymnastics with the colorbar, but making the plot itself reduces to a nice one-liner. This works great for plotting the results from classifications done with sklearn. Each label matches a (x,y) coordinate. 

<pre><code>import matplotlib
import matplotlib.pyplot as plt
import numpy as np

x = [4,8,12,16,1,4,9,16]
y = [1,4,9,16,4,8,12,3]
label = [0,1,2,3,0,1,2,3]
colors = ['red','green','blue','purple']

fig = plt.figure(figsize=(8,8))
plt.scatter(x, y, c=label, cmap=matplotlib.colors.ListedColormap(colors))

cb = plt.colorbar()
loc = np.arange(0,max(label),max(label)/float(len(colors)))
cb.set_ticks(loc)
cb.set_ticklabels(colors)
</code></pre>

<a href="https://i.stack.imgur.com/Jnq3p.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Jnq3p.png" alt="Scatter plot color labels"></a>

Using a slightly modified version of <a href="https://stackoverflow.com/questions/14777066/matplotlib-discrete-colorbar">this</a> answer, one can generalise the above for N colors as follows:

<pre><code>import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

N = 23 # Number of labels

# setup the plot
fig, ax = plt.subplots(1,1, figsize=(6,6))
# define the data
x = np.random.rand(1000)
y = np.random.rand(1000)
tag = np.random.randint(0,N,1000) # Tag each point with a corresponding label 

# define the colormap
cmap = plt.cm.jet
# extract all colors from the .jet map
cmaplist = [cmap(i) for i in range(cmap.N)]
# create the new map
cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)

# define the bins and normalize
bounds = np.linspace(0,N,N+1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

# make the scatter
scat = ax.scatter(x,y,c=tag,s=np.random.randint(100,500,N),cmap=cmap, norm=norm)
# create the colorbar
cb = plt.colorbar(scat, spacing='proportional',ticks=bounds)
cb.set_label('Custom cbar')
ax.set_title('Discrete color mappings')
plt.show()
</code></pre>

Which gives:

<a href="https://i.stack.imgur.com/Rmm0n.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Rmm0n.png" alt="enter image description here"></a>

blocks|key|259288|text|一个简单的解决方案是为每个类分配颜色。这样，我们就可以控制每种颜色对每个类的影响。例如：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|259289|arr1+=+[1,+2,+3,+4,+5]
arr2+=+[2,+3,+3,+4,+4]
labl+=+[0,+1,+1,+0,+0]
color=+['red'+if+l+==+0+else+'green'+for+l+in+labl]
plt.scatter(arr1,+arr2,+color=color)|code-block|syntax|javascript|259290|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

A simple solution is to assign color for each class. This way, we can control how each color is for each class. For example:

<pre><code>arr1 = [1, 2, 3, 4, 5]
arr2 = [2, 3, 3, 4, 4]
labl = [0, 1, 1, 0, 0]
color= ['red' if l == 0 else 'green' for l in labl]
plt.scatter(arr1, arr2, color=color)
</code></pre>

I have two vectors, one with values and one with class labels like 1,2,3 etc. 

I would like to plot all the points that belong to class 1 in red, to class 2 in blue, to class 3 in green etc. How can I do that?

Matplotlib color according to class labels

我有两个向量，一个有值，另一个有类标签，比如1,2,3等等。我想用红色绘制属于第1类的所有点，用蓝色绘制属于第2类的点，用绿色绘制属于第3类的点，等等。我该怎么做？

问根据类标签的Matplotlib颜色
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