Comments (4)
Good idea! I can see how that could be useful in some cases. I'd welcome a pull request (especially if you write a couple of accompanying tests) — I can also add something like this in, though it might not be for a week or two.
I think it might be good to include a compact=False
keyword argument, too, to keep consistent with set
and to specify whether the endpoints of the interval will always be included as measurements.
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I made PR #41
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I have cleaned the PR to fix pep8 issues. Any chance for merge any time soon ?
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Thanks @sdementen! I'll merge it in sometime this week when I get a chance
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Related Issues (20)
- pd.NaT creates unexpected behavior in TimeSeries
- tests fail because of something with config
- TimeSeries._get_linear_interpolate() explicitly casts int to float HOT 2
- Result of to_bool is missing default value assignment HOT 2
- Merge fails with TypeError HOT 2
- will it work for multivariate time series prediction both regression and classification HOT 1
- Where is the folder called "data" containing the 40 times series ? HOT 1
- [... list of forty traces.TimeSeries ...] is not functioning. HOT 2
- Incorrect handling of Numpy array passed as times of measurements HOT 1
- No longer maintained? HOT 5
- `max` for distribution with `start` and `end` gives wrong result HOT 1
- add documentation for TimeSeries.plot
- add documentation for EventSeries
- Documentation failing HOT 1
- bool(trace) should handle default values
- use a specific value for the default when not given (e.g. NotGiven) to differentiate a default=None with a default not given HOT 2
- add default in __str__ when default is given
- support __abs__ on traces
- support all mathematical magic methods including __r*__ and __pos__/__neg__
- adding a constant to a trace fails
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