Limit/mask matplotlib contour to data area

As seen in the answer to this question one may introduce a condition to mask the values.

The sentence from the question "I do not want griddata to interpolate outside of the boundaries given by the min and max values of the x and y data." implies that there is some min/max condition present, which can be used.

Should that not be the case, one may clip the contour using a path. The points of this path need to be specified as there is no generic way of knowing which points should be the edges. The code below does this for three different possible paths.

import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch
from matplotlib.mlab import griddata
import numpy as np
import pandas as pd

df = pd.DataFrame({'x':[0, 0, 1, 1, 3, 3, 3, 4, 4, 4], 
                   'y':[0, 1, 0, 1, 0.2, 0.7, 1.4, 0.2, 1.4, 2], 
                   'z':[50, 40, 40, 30, 30, 30, 20, 20, 20, 10]})

x = df['x']
y = df['y']
z = df['z']

xi = np.linspace(x.min(), x.max(), 100)
yi = np.linspace(y.min(), y.max(), 100)
z_grid = griddata(x, y, z, xi, yi, interp='linear')

clipindex = [ [0,2,4,7,8,9,6,3,1,0],
              [0,2,4,7,5,8,9,6,3,1,0],
              [0,2,4,7,8,9,6,5,3,1,0]]

fig, axes = plt.subplots(ncols=3, sharey=True)
for i, ax in enumerate(axes):
    cont = ax.contourf(xi, yi, z_grid, 15)
    ax.scatter(x, y, color='k') # The original data points
    ax.plot(x[clipindex[i]], y[clipindex[i]], color="crimson")

    clippath = Path(np.c_[x[clipindex[i]], y[clipindex[i]]])
    patch = PathPatch(clippath, facecolor='none')
    ax.add_patch(patch)
    for c in cont.collections:
        c.set_clip_path(patch)

plt.show()