matplotlib pcolormesh creates data artifacts

Question

I'm plotting data using the matplotlib functions pcolormesh and imshow and when I use pcolormesh it produces artifacts where it seems to slide some of the data around:

whereas imshow does not:

I was able to produce an example that has the same artifacting

import numpy as np
import pandas as pd

data = pd.DataFrame({'x':np.random.normal(loc=0.5, size=5000)
                     , 'y':np.random.normal(loc=0.5, size=5000)
                     , 'z':np.random.normal(loc=0.5, size=5000)})

data_pivot = data.pivot(index='x', columns='y', values='z')
x = data_pivot.index.values
y = data_pivot.columns.values
z = data_pivot.values
masked_data = np.ma.masked_invalid(z)

which produces the following figures like so:

fig, ax = plt.subplots(1, figsize=(8,8))
ax.pcolormesh(x, y, masked_data)

Where do these artifacts come from? There isn't anything wrong with the data as far as I can tell since the original data and the made up data produce the same result.

Answer 1

The answer is, my data was not shaped correctly. pcolormesh expects the input array to have the right number of columns and rows, even if those columns and rows are full of NaNs. Thus when it finds a "gap" in the data it fills it with the last known value. The artifacts are not artifacts but gaps in the data. In my original example I had assumed imshow to be correct when in fact it is not accounting for these gaps.