If by "easier" you mean shorter, you can directly evaluate the colormap on a numpy array.

import matplotlib.pyplot as plt
import numpy as np

colors =  plt.cm.Vega20c( (4./3*np.arange(20*3/4)).astype(int) )
plt.scatter(np.arange(15),np.ones(15), c=colors, s=180)

plt.show()

You can create a ListedColormap from the colors of other colormaps.

E.g to get the darkorange color from the tab20c colormap, use plt.cm.tab20c(4) (as this is the 5th color in that map). Note that this works only for indexed colormaps - otherwise you need to use a value between 0 and 1.

From a list of thus obtained colors, you can create a new ListedColormap.

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

colors = [plt.cm.tab20(0),plt.cm.tab20(1),plt.cm.tab20c(4),
          plt.cm.tab20c(5),plt.cm.tab20c(6),plt.cm.tab20c(7)]

cmap=matplotlib.colors.ListedColormap(colors)

x = np.arange(1,7)

plt.scatter(x,x,c=x, s=100, cmap=cmap, vmin=1, vmax=7)

plt.show()

Or, to get a nice colorbar as well,

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

colors = [plt.cm.tab20(0),plt.cm.tab20(1),plt.cm.tab20c(4),
          plt.cm.tab20c(5),plt.cm.tab20c(6),plt.cm.tab20c(7)]

cmap=matplotlib.colors.ListedColormap(colors)
norm = matplotlib.colors.BoundaryNorm(np.arange(1,8)-0.5,len(colors))

x = np.arange(1,7)

sc = plt.scatter(x,x,c=x, s=100, cmap=cmap, norm=norm)
plt.colorbar(sc, ticks=x)

plt.show()