ValueError: Wrong number of items passed - Meaning and suggestions?

In general, the error ValueError: Wrong number of items passed 3, placement implies 1 suggests that you are attempting to put too many pigeons in too few pigeonholes. In this case, the value on the right of the equation

results['predictedY'] = predictedY

is trying to put 3 "things" into a container that allows only one. Because the left side is a dataframe column, and can accept multiple items on that (column) dimension, you should see that there are too many items on another dimension.

Here, it appears you are using sklearn for modeling, which is where gaussian_process.GaussianProcess() is coming from (I'm guessing, but correct me and revise the question if this is wrong).

Now, you generate predicted values for y here:

predictedY, MSE = gp.predict(testX, eval_MSE = True)

However, as we can see from the documentation for GaussianProcess, predict() returns two items. The first is y, which is array-like (emphasis mine). That means that it can have more than one dimension, or, to be concrete for thick headed people like me, it can have more than one column -- see that it can return (n_samples, n_targets) which, depending on testX, could be (1000, 3) (just to pick numbers). Thus, your predictedY might have 3 columns.

If so, when you try to put something with three "columns" into a single dataframe column, you are passing 3 items where only 1 would fit.

Not sure if this is relevant to your question but it might be relevant to someone else in the future: I had a similar error. Turned out that the df was empty (had zero rows) and that is what was causing the error in my command.

Another cause of this error is when you apply a function on a DataFrame where there are two columns with the same name.