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Function decorators for Pandas and Polars Dataframe column name and data type validation
Working with DataFrames often means passing them through multiple transformation functions, making it easy to lose track of their structure over time. Daffy adds runtime validation and documentation to your DataFrame operations through simple decorators. By declaring the expected columns and types in your function definitions, you can:
@df_in(columns=["price", "bedrooms", "location"])
@df_out(columns=["price_per_room", "price_category"])
def analyze_housing(houses_df):
# Transform raw housing data into price analysis
return analyzed_df
Like type hints for DataFrames, Daffy helps you catch structural mismatches early and keeps your data pipeline documentation synchronized with the code. Compatible with both Pandas and Polars.
"r/column_\d+/"
)Install with your favorite Python dependency manager:
pip install daffy
from daffy import df_in, df_out
@df_in(columns=["Brand", "Price"]) # Validate input DataFrame columns
@df_out(columns=["Brand", "Price", "Discount"]) # Validate output DataFrame columns
def apply_discount(cars_df):
cars_df = cars_df.copy()
cars_df["Discount"] = cars_df["Price"] * 0.1
return cars_df
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Function decorators for Pandas and Polars Dataframe column name and data type validation
We found that daffy demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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