Setting different color for each series in scatter plot on matplotlib


Question

Suppose I have three data sets:

X = [1,2,3,4]
Y1 = [4,8,12,16]
Y2 = [1,4,9,16]

I can scatter plot this:

from matplotlib import pyplot as plt
plt.scatter(X,Y1,color='red')
plt.scatter(X,Y2,color='blue')
plt.show()

How can I do this with 10 sets?

I searched for this and could find any reference to what I'm asking.

Edit: clarifying (hopefully) my question

If I call scatter multiple times, I can only set the same color on each scatter. Also, I know I can set a color array manually but I'm sure there is a better way to do this. My question is then, "How can I automatically scatter-plot my several data sets, each with a different color.

If that helps, I can easily assign a unique number to each data set.

1
142
12/29/2016 7:48:19 PM

Accepted Answer

I don't know what you mean by 'manually'. You can choose a colourmap and make a colour array easily enough:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

x = np.arange(10)
ys = [i+x+(i*x)**2 for i in range(10)]

colors = cm.rainbow(np.linspace(0, 1, len(ys)))
for y, c in zip(ys, colors):
    plt.scatter(x, y, color=c)

Matplotlib graph with different colors

Or you can make your own colour cycler using itertools.cycle and specifying the colours you want to loop over, using next to get the one you want. For example, with 3 colours:

import itertools

colors = itertools.cycle(["r", "b", "g"])
for y in ys:
    plt.scatter(x, y, color=next(colors))

Matplotlib graph with only 3 colors

Come to think of it, maybe it's cleaner not to use zip with the first one too:

colors = iter(cm.rainbow(np.linspace(0, 1, len(ys))))
for y in ys:
    plt.scatter(x, y, color=next(colors))
227
11/13/2018 1:20:58 PM

The normal way to plot plots with points in different colors in matplotlib is to pass a list of colors as a parameter.

E.g.:

import matplotlib.pyplot
matplotlib.pyplot.scatter([1,2,3],[4,5,6],color=['red','green','blue'])

3 colors

When you have a list of lists and you want them colored per list. I think the most elegant way is that suggesyted by @DSM, just do a loop making multiple calls to scatter.

But if for some reason you wanted to do it with just one call, you can make a big list of colors, with a list comprehension and a bit of flooring division:

import matplotlib
import numpy as np

X = [1,2,3,4]
Ys = np.array([[4,8,12,16],
      [1,4,9,16],
      [17, 10, 13, 18],
      [9, 10, 18, 11],
      [4, 15, 17, 6],
      [7, 10, 8, 7],
      [9, 0, 10, 11],
      [14, 1, 15, 5],
      [8, 15, 9, 14],
       [20, 7, 1, 5]])
nCols = len(X)  
nRows = Ys.shape[0]

colors = matplotlib.cm.rainbow(np.linspace(0, 1, len(Ys)))

cs = [colors[i//len(X)] for i in range(len(Ys)*len(X))] #could be done with numpy's repmat
Xs=X*nRows #use list multiplication for repetition
matplotlib.pyplot.scatter(Xs,Ys.flatten(),color=cs)

All plotted

cs = [array([ 0.5,  0. ,  1. ,  1. ]),
 array([ 0.5,  0. ,  1. ,  1. ]),
 array([ 0.5,  0. ,  1. ,  1. ]),
 array([ 0.5,  0. ,  1. ,  1. ]),
 array([ 0.28039216,  0.33815827,  0.98516223,  1.        ]),
 array([ 0.28039216,  0.33815827,  0.98516223,  1.        ]),
 array([ 0.28039216,  0.33815827,  0.98516223,  1.        ]),
 array([ 0.28039216,  0.33815827,  0.98516223,  1.        ]),
 ...
 array([  1.00000000e+00,   1.22464680e-16,   6.12323400e-17,
          1.00000000e+00]),
 array([  1.00000000e+00,   1.22464680e-16,   6.12323400e-17,
          1.00000000e+00]),
 array([  1.00000000e+00,   1.22464680e-16,   6.12323400e-17,
          1.00000000e+00]),
 array([  1.00000000e+00,   1.22464680e-16,   6.12323400e-17,
          1.00000000e+00])]

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