Reputation: 73
I have two sets of data, x
and y
as ints. I need to plot both of these data points using matplotlib.pyplot.scatter
. I also need to plot the first category y == 0
in one color and the second, y == 1
in a different color.
I've looked at the documentation for the scatter function, but I don't understand how to do all of this in one plot.
Sample data:
2.897534798034255,0.872359037956732,1
1.234850239781278,-0.293047584301112,1
0.238575209753427,0.129572680572429,0
-0.109757648021958,0.484048547480385,1
1.109735783200013,-0.002785328902198,0
1.572803975652908,0.098547849368397,0
x and y are defined as:
x = data[:, [0, 1]]
y = data[:, -1].astype(int)
Size of x is 2000, size of y is 1000
My attempt:
pl.scatter(x, y==0, s=3, c='r')
pl.scatter(x, y==1, s=3, c='b')
pl.show()
Upvotes: 1
Views: 7594
Reputation: 12410
Not sure why you want to extract x
and y
first and filter later. Given that you have a lot of data and not many categories, plt.plot
with markers should also be faster than plt.scatter
:
import numpy as np
import matplotlib.pyplot as plt
data = np.asarray([[2.897534798034255,0.872359037956732,1],
[1.234850239781278,-0.293047584301112,1],
[0.238575209753427,0.129572680572429,0],
[-0.109757648021958,0.484048547480385,1],
[1.109735783200013,-0.002785328902198,0],
[1.572803975652908,0.098547849368397,0]])
colors = ["blue", "red", "green"]
labels = ["A", "B", "C"]
for i, c, l in zip(np.unique(data[:, 2]), colors, labels):
plt.plot(data[data[:, 2]==i][:, 0], data[data[:, 2]==i][:, 1],
marker="o", markersize=7, ls="None", color=c,
label=f"The letter {l} represents category {int(i)}")
plt.legend()
plt.show()
Upvotes: 0
Reputation: 998
pyplot.scatter()
accepts a list of colors, hence:
c = ['r' if yy==0 else 'b' for yy in y]
plt.scatter(x, y, c=c)
In your code, y==0
produces a mask that has only True
and False
values, not y
values to be plotted. If x
and y
are numpy arrays, you can do:
mask = (y == 0)
plt.scatter(x[mask], y[mask], c='r')
mask = (y == 1)
plt.scatter(x[mask], y[mask], c='b')
Upvotes: 3
Reputation: 4588
You can do it like this:
import numpy as np
import matplotlib.pyplot as plt
data = np.array([[2.897534798034255,0.872359037956732,1],
[1.234850239781278,-0.293047584301112,1],
[0.238575209753427,0.129572680572429,0],
[-0.109757648021958,0.484048547480385,1],
[1.109735783200013,-0.002785328902198,0],
[1.572803975652908,0.098547849368397,0]])
x = data[:, [0, 1]]
y = data[:, -1].astype(int)
plt.scatter(x[:,0][y==0], x[:,1][y==0], s=3, c='r')
plt.scatter(x[:,0][y==1], x[:,1][y==1], s=3, c='b')
plt.show()
Although this is perhaps more readable:
x1 = data[:, 0]
x2 = data[:, 1]
y = data[:, -1].astype(int)
plt.scatter(x1[y==0], x2[y==0], s=3, c='r')
plt.scatter(x1[y==1], x2[y==1], s=3, c='b')
Output:
Upvotes: 3