FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated use `arr[tuple(seq)]`

For python>=3.7 you need to upgrade your scipy>=1.2.

A fuller traceback would be nice. My guess is that seaborn.distplot is using scipy.stats to calculate something. The error occurs in

def _compute_qth_percentile(sorted, per, interpolation_method, axis):    ....    indexer = [slice(None)] * sorted.ndim    ...    indexer[axis] = slice(i, i + 2)    ...    return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

So in this last line, the list indexer is used to slice sorted.

In [81]: x = np.arange(12).reshape(3,4)In [83]: indexer = [slice(None), slice(None,2)]In [84]: x[indexer]/usr/local/bin/ipython3:1: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.  #!/usr/bin/python3Out[84]: array([[0, 1],       [4, 5],       [8, 9]])In [85]: x[tuple(indexer)]Out[85]: array([[0, 1],       [4, 5],       [8, 9]])

Using a list of slices works, but the plan is to depreciate in the future. Indexes that involve several dimensions are supposed to be tuples. The use of lists in the context is an older style that is being phased out.

So the scipy developers need to fix this. This isn't something end users should have to deal with. But for now, don't worry about the futurewarning. It doesn't affect the calculations or plotting. There is a way of suppressing future warnings, but I don't know it off hand.

FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated use `arr[tuple(seq)]` instead of `arr[seq]`

I was running seaborn.regplot, and got rid of the warning by upgrading scipy 1.2 as NetworkMeister suggested.

pip install --upgrade scipy --user

If you still get warnings in other seaborn plots, you can run the following beforehand. This is helpful in Jupyter Notebook because the warnings kind of make the report look bad even if your plots are great.

import warningswarnings.filterwarnings("ignore")