python
12 lines · 1 tab
Dr. Elena Vasquez
Apr 2026
1 tab
import pandera as pa
from pandera.typing import Series
class ChurnTrainingSchema(pa.DataFrameModel):
customer_id: Series[int] = pa.Field(unique=True)
age: Series[int] = pa.Field(ge=18, le=100)
income: Series[float] = pa.Field(ge=0)
country: Series[str]
churned: Series[int] = pa.Field(isin=[0, 1])
validated_df = ChurnTrainingSchema.validate(df, lazy=True)
print(validated_df.head())
1 file · python
Explain with highlit
I use schema validation to stop bad data before it poisons training or inference. Pandera lets me express expectations around types, nullability, ranges, and uniqueness in code that can run in CI or orchestration jobs. This catches upstream breakage earlier than model metrics ever will.
Share this code
Here's the card — post it anywhere.
