MP-003: Schema validator#4
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jayyaali95 wants to merge 13 commits intomainfrom
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viktorb-lyft
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Jun 3, 2025
| import pandas as pd | ||
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| class Column: |
viktorb-lyft
reviewed
Jun 3, 2025
| _allow_extra_columns = False | ||
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| meta_class_based_df = DataFrame({'days': [1,2,3], 'probability': [0.1, 0.5, 0.9], 'feature': ['a','b','c'], 'extra': [1,2,3]}, schema=TestSchemaMetaClassBase) | ||
| init_subclass_based_df = DataFrame({'days': [1,2,3], 'probability': [0.1, 0.5, 0.9], 'feature': ['a','b','c'], 'extra': [1,2,3]}, schema=TestSchemaInitSubclassBase) |
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This is a slightly different approach than what I had in mind. You are passing the scheme to the dataframe constructor, while it works for validating the dataframe itself, we cannot change the schema of the dataframe. e.g:
df = DataFrame()
helper_function1(DataFrame[Schema1])
helper_function2(DataFrame[Schema2])
Schema1 and schema2 can be completely different, they descirbe the assumptions helper funciton 1 and 2 are making on the dataframe. We want to be able to validate the same dataframe for both schemas.
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Implemented a schema validator using two approaches: one based on a metaclass, and the other leveraging the init_subclass method.