issue_comments: 399615463

OK, I think I'm convinced. Now it's probably a good time to go back to the pandas issues (or open a new one) with a proposal to add tolerance to Float64Index.

On Fri, Jun 22, 2018 at 4:56 PM Benjamin Root notifications@github.com wrote:

I am not concerned about the non-commutativeness of the indexer itself. There is no way around that. At some point, you have to choose values, whether it is done by an indexer or done by some particular set operation.

As for the different sizes, that happens when the tolerance is greater than half the smallest delta. I figure a final implementation would enforce such a constraint on the tolerance.

On Fri, Jun 22, 2018 at 5:56 PM, Stephan Hoyer notifications@github.com wrote:

@WeatherGod https://github.com/WeatherGod One problem with your definition of tolerance is that it isn't commutative, even if both indexes have the same tolerance:

a = ImpreciseIndex([0.1, 0.2, 0.3, 0.4]) a.tolerance = 0.1 b = ImpreciseIndex([0.301, 0.401, 0.501, 0.601]) b.tolerance = 0.1print(a.union(b)) # ImpreciseIndex([0.1, 0.2, 0.3, 0.4, 0.501, 0.601], dtype='float64')print(b.union(a)) # ImpreciseIndex([0.1, 0.2, 0.301, 0.401, 0.501, 0.601], dtype='float64')

If you try a little harder, you could even have cases where the result has a different size, e.g.,

a = ImpreciseIndex([1, 2, 3]) a.tolerance = 0.5 b = ImpreciseIndex([1, 1.9, 2.1, 3]) b.tolerance = 0.5print(a.union(b)) # ImpreciseIndex([1.0, 2.0, 3.0], dtype='float64')print(b.union(a)) # ImpreciseIndex([1.0, 1.9, 2.1, 3.0], dtype='float64')

Maybe these aren't really problems in practice, but it's at least a little strange/surprising.

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