Protect Your Data & ML Products from Low-Quality Data

The open-source framework for precision data testing for data scientists and ML engineers.

Install Union Pandera and get started

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$ pip install pandera

Build confidence in the quality of your data by defining schemas for complex data objects

Pandera provides a simple, flexible and extensible data-testing framework for validating not only your data, but also the functions that produce them.

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import pandas as pd
import pandera as pa

from pandera.typing import Series, DataFrame

# Define a schema
class Schema(pa.SchemaModel):
	item: Series[str] = pa.Field(isin=["apple", "orange"], coerce=True)
	price: Series[float] = pa.Field(gt=0, coerce=True)

# Validate at runtime
@pa.check_types(lazy=True)
def transform_data(data: DataFrame [Schema]):
	...

transform_data(
	pd.DataFrame.from_records([
		{"item": "apple", "price": 0.5}, 	# invalid item name
		{"item": "orange", "price": -1000}, 	# negative price
	])
)

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import hypothesis
import pandera as pa

from pandera.typing import Series, DataFrame

# Define an input schema
class Schema(pa.SchemaModel):
	item: Series[str] = pa.Field(isin= ["apple", "orange"], coerce=True)
	price: Series[float] = pa.Field(gt=0, coerce=True)

# Define an output schema
class OutputSchema(Schema) :
	item: Series[str] = pa.Field(isin=[ "apple"])

# Implement a function that filters out oranges
@pa.check_types (lazy=True)
def transform_data(data: DataFrame[Schema]) -> DataFrame [OutputSchema]:
	return data.query("item =='orange'") # 🐛 Incorrect implementation

# Test the function
@hypothesis.given(Schema.strategy(size=10))
def test_transform_data(data):
	transform_data(data)

# Run Unit Tests
test_transform_data()