vandas
This is a simple library to add type constraints to pandas Series. There are several reasons why you might want to do this:
- You want to ensure that the data in your Series is of a certain type.
- You want to ensure that the data in your Series is clean and consistent.
- You want to ensure that the data in your Series is correct and valid.
- You want to convert the data in your Series to a certain unit.
In the context of vantage6, this is useful for:
- Users can only select valid variables (types) when creating a task.
- Data extraction jobs can be validated against the expected types.
- The users has more context about the data they are working with.
- The algorithm can convert the data to the correct unit it requires.
- Analysis outputs can display the correct unit.
- Missing categories can now be counted as 0.
Release files for vantage6-types 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| vantage6_types-0.1.0.tar.gz | 10.5 kB | With provenance | Details for vantage6_types-0.1.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| vantage6_types-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for vantage6_types-0.1.0-py3-none-any.whl |
Total release size: 20.7 kB
Release files / vantage6_types-0.1.0.tar.gz
| Download URL | vantage6_types-0.1.0.tar.gz |
|---|---|
| Size | 10.5 kB |
| Tags | Source |
|
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| Download URL | vantage6_types-0.1.0-py3-none-any.whl |
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| Size | 10.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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