contour plot or topological map with unequal sized arrays?

I have a dataframe with 20 rows and 100 columns (dummy data):

Right now I'm plotting the data as a heat map, but I'll like to have a more topological view of the data.

I want to create something like a contour map:

But with visible points, kind of like this map:

(from the contour plot of irregularly spaced data documentation)

Is there a way to do this using contour or isobar or something else? From what I can tell for the contour plot documentation, the arrays for X, Y, and Z need to be of equal size, but my dataframe has to have 20 rows and 100 columns.

Thanks in advance!!

If your data is in a dataframe, then it is regularly spaced. I assume x and y coodinates shall be the dataframe's column and index indices, respectively. Then you can simply make a contour plot from the dataframe and then overplot the grid points on the same Axes.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# create sample dataframe
x = np.linspace(-2, 2, 100)
y = np.linspace(-2, 2, 20)
xx, yy = np.meshgrid(x, y)
z = np.exp(-(xx**2 + yy**2))
df = pd.DataFrame(z)

# plot it
fig, ax = plt.subplots(figsize=(20, 10))
cont = ax.contourf(df)
ax.plot(df.columns, np.broadcast_to(df.index, (len(df.columns),len(df)) ), 'k.')
fig.colorbar(cont)