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.

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)
```

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))
```

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))
```

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'])
```

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)
```

```
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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