def scatter3d(x,y,z, cs, colorsMap='jet'):
   cm = plt.get_cmap(colorsMap)
   cNorm = matplotlib.colors.Normalize(vmin=min(cs), vmax=max(cs))
   scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
   fig = plt.figure()
   ax = Axes3D(fig)
   ax.scatter(x, y, z,c=scalarMap.to_rgba(cs))
   ax.set_xlabel('Thita1')
   ax.set_ylabel('Thita2')
   ax.set_zlabel('Fairness (%)')
   scalarMap.set_array(cs)
   fig.colorbar(scalarMap,label='Error Rate (%)')
   plt.show()

def surfacePlot(x,y,z, cs, colorsMap='jet'):
    cm = plt.get_cmap(colorsMap)
    cNorm = matplotlib.colors.Normalize(vmin=min(cs), vmax=max(cs))
    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    ax.plot_surface(x, y, z, facecolors=scalarMap.to_rgba(cs))
    ax.set_xlabel('Thita1')
    ax.set_ylabel('Thita2')
    ax.set_zlabel('Fairness')
    scalarMap.set_array(cs)
    fig.colorbar(scalarMap,label='Error Rate (%)')
    plt.show()

grid_x, grid_y = np.meshgrid(x, y)

# I'm assuming that your data is already mesh-like, which it looks like it is.
# The data would also need to be appropriately sorted for `reshape` to work.
# `dx` here is number of unique x values, and `dy` is number unique y values.
grid_z = z.reshape(dy, dx)

ax.plot_scatter(grid_x, grid_y, grid_z)

from scipy.interpolate import griddata
xy = np.column_stack([x, y])
grid_x, grid_y = np.mgrid[0:1:100j, 0:1:100j]  # grid you create
grid_z = griddata(xy, z, (grid_x, grid_y))
ax.plot_scatter(grid_x, grid_y, grid_z)