iterating markers in plots

Question

I\'m trying to denote the predictions with a color and the correct labels as markers for the iris data set. Here is what I have so far:

from sklearn.mixture          


        

Answer 1

You could modify your code like the following to get the desired result:

markers = ["o" , "s" , "D"]
colors = ["red", "green", "blue"]

for i in range(4):
    for j in range(4):
        for k in range(x.shape[0]):
            if(i != j):
                axes[i, j].scatter(x[k, i], x[k, j], color=colors[labels[k]], marker = markers[y[k]], s=40, cmap='viridis')  
            else:
                axes[i,j].text(0.15, 0.3, Superman[i], fontsize = 8)

Answer 2

Using several markers in a single scatter is currently not a feature matplotlib supports. There is however a feature request for this at https://github.com/matplotlib/matplotlib/issues/11155

It is of course possible to draw several scatters, one for each marker type. A different option is the one I proposed in the above thread, which is to set the markers after creating the scatter:

import numpy as np
import matplotlib.pyplot as plt

def mscatter(x,y,ax=None, m=None, **kw):
    import matplotlib.markers as mmarkers
    if not ax: ax=plt.gca()
    sc = ax.scatter(x,y,**kw)
    if (m is not None) and (len(m)==len(x)):
        paths = []
        for marker in m:
            if isinstance(marker, mmarkers.MarkerStyle):
                marker_obj = marker
            else:
                marker_obj = mmarkers.MarkerStyle(marker)
            path = marker_obj.get_path().transformed(
                        marker_obj.get_transform())
            paths.append(path)
        sc.set_paths(paths)
    return sc


N = 40
x, y, c = np.random.rand(3, N)
s = np.random.randint(10, 220, size=N)
m = np.repeat(["o", "s", "D", "*"], N/4)

fig, ax = plt.subplots()

scatter = mscatter(x, y, c=c, s=s, m=m, ax=ax)

plt.show()

If you only have numbers, instead of marker symbols you would first need to map numbers to symbols and supply the list of symbols to the function.