python - Matplotlib - color as third variable for already normalized data - not in grayscale -

i've searched haven't been able find answer - how implement color third variable in matplotlib if data normalized? data in range 0-1 , produces graph in grayscale, 1 fades say, blue yellow, blue = 1, sort of green values 0.5 , yellow values around 0.

my code reads:

    line in lines:     if line:        x1.append(line.split()[0])        y1.append(line.split()[1])        z1.append(line.split()[4])     xv = np.array(x1)     yv = np.array(y1)     zv = np.array(z1)      plt.scatter(xv, yv, c=zv, cmap=?) 

i've tried sorts of variations, including no_norm best can still grayscale plot... don't think using vmin , vmax going down right route, , i've tried doing

    cmap = plt.cm.get_cmap('autumn')     plt.scatter(xv, yv, c=cmap(zv)) 

but produces error:

     typeerror: cannot cast array data dtype('s11') dtype('int64') according rule 'safe'  

thanks in advance, anna

after reading @tcaswell's comment, found this page. solutions seems way more elegant. can pick cmap want page , use like:

import matplotlib.pyplot plt  xs = range(11) ys = [0] * 11 colors = [ * 0.1 in range(11) ]   plt.scatter(xs, ys, s=600, c = colors, cmap='ylgnbu') plt.colorbar() plt.show() 

output: