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pythonintermediate

Pandera DataFrame Schema Validation

Use Pandera to validate DataFrame schemas with type checks, value constraints, and custom checks.

python
import pandera as pa
import pandas as pd

schema = pa.DataFrameSchema(
    {
        'id':    pa.Column(int, pa.Check.greater_than(0)),
        'name':  pa.Column(str, pa.Check.str_length(1, 100)),
        'score': pa.Column(float, pa.Check.in_range(0.0, 100.0)),
        'tier':  pa.Column(str,  pa.Check.isin(['bronze', 'silver', 'gold'])),
    },
)

df = pd.DataFrame({'id':[1,2],'name':['Alice','Bob'],'score':[92.5,78.0],'tier':['gold','silver']})
validated = schema.validate(df)
print(validated)

Use Cases

  • pipeline input validation
  • data contracts
  • CI quality gates

Tags

#pandera#validation#schema#data-quality

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