Comments (2)
Hi @CamDavidsonPilon! Thanks for continuing to explore Formulaic :).
I vaguely recall that dropping the index was useful when we do filtering out of null values, but the dataframe index does seem like a useful thing to keep intact in some circumstances. I'll take a closer look soon! I probably haven't noticed this in my own work much because I always cast directly to numpy values (and now formulaic can do it for me using output='numpy'
).
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Hi @CamDavidsonPilon ; at long last I added support for keeping track of indices, even when dropping null rows. You can see here for the implementation details. This will be in v0.2.1 which I will release imminently.
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Related Issues (20)
- drop both columns in dependent variable and design matrix when missings occur HOT 5
- DOC: Explicitly mention support for multiple variables on the left hand side HOT 3
- Terms not being evaluated in get_model_matrix() HOT 2
- 17 tests fail: ModuleNotFoundError: No module named 'interface_meta' HOT 2
- How can the encoding choices for one dataset be reused for another? HOT 3
- Intercept term breaks when RHS formula begins with a parentheses HOT 2
- How do I set the reference level for a categorical term? HOT 4
- Support for sympy >= 1.10 HOT 3
- ENH: Preserve variable order as they appear in formulas HOT 5
- 2 tests fail HOT 1
- Interaction between two categorical covariates sometimes switches order, causing error HOT 3
- Intercept is not added after being removed HOT 4
- Proposal: support columns representing multiple features HOT 3
- Formulaic struggles with NAs and `poly()` syntax HOT 3
- Escaped variables and functions HOT 3
- How to include structural zeros? HOT 1
- Retain Column Names for sparse model matrices HOT 4
- Formulaic not raising an exception when required fields are missing in the dataset HOT 2
- Allow formatting the categorical encoded variables HOT 4
- Throw error when formula has parameters that are not available HOT 2
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