I have 3 lots of data. These are x and y values as well as a temperature value for each xy point. I would like to plot each point and interpolate the area between points to get a continuous surface. The issue I have is specifying the temperature values. I can't get it to work with an equal number of x,y and z (temperature) values and all the examples I can find online use a function of x and y to create z or have z values for every point on an xy grid. Is there a simple way to do this?

import numpy as np
import matplotlib.pyplot as plt

fig, axs = plt.subplots()

x = np.linspace(0, 1, 100)
y = np.linspace(0,1,100)
X, Y = np.meshgrid(x, y)
#Z = np.sin(X)*np.sin(Y)  # want to specify not an equation
Z = np.linspace(1,2,100)
levels = np.linspace(-1, 1, 40)
cs = axs.contourf(X, Y, Z, levels=levels)
fig.colorbar(cs, ax=axs, format="%.2f")
plt.show()

Update:

Here is what I have so far. I still need to work out a good method to fill in the area between points. Does anyone have any ideas?

import numpy as np
import matplotlib.pyplot as plt

fig, axs = plt.subplots()

# create a grid in the correct shape / size
x = np.linspace(0, 1, 3)
y = np.linspace(0,1,3)
X, Y = np.meshgrid(x, y)

# specify and change the relevent areas
y = [1,2,0]  # location of point in x direction
x =[2,1,1]  #location of point in y direction
z = [40,30,20]  #temperature
Z = np.arange(1,10).reshape((3,3))
Z[y,x] = z

levels = np.linspace(0, 40, 40)
cs = axs.contourf(X, Y, Z, levels=levels)
fig.colorbar(cs, ax=axs, format="%.2f")
plt.show()