Data Synthesis Strategies#

new in 0.6.0

pandera provides a utility for generating synthetic data purely from pandera schema or schema component objects. Under the hood, the schema metadata is collected to create a data-generating strategy using hypothesis, which is a property-based testing library.

Basic Usage#

Once you’ve defined a schema, it’s easy to generate examples:

import pandera as pa

schema = pa.DataFrameSchema(
    {
        "column1": pa.Column(int, pa.Check.eq(10)),
        "column2": pa.Column(float, pa.Check.eq(0.25)),
        "column3": pa.Column(str, pa.Check.eq("foo")),
    }
)
print(schema.example(size=3))

    column1  column2 column3
 0       10     0.25     foo
 1       10     0.25     foo
 2       10     0.25     foo

Note that here we’ve constrained the specific values in each column using Check s in order to make the data generation process deterministic for documentation purposes.

Usage in Unit Tests#

The example method is available for all schemas and schema components, and is primarily meant to be used interactively. It could be used in a script to generate test cases, but hypothesis recommends against doing this and instead using the strategy method to create a hypothesis strategy that can be used in pytest unit tests.

import hypothesis

def processing_fn(df):
    return df.assign(column4=df.column1 * df.column2)

@hypothesis.given(schema.strategy(size=5))
def test_processing_fn(dataframe):
    result = processing_fn(dataframe)
    assert "column4" in result

The above example is trivial, but you get the idea! Schema objects can create a strategy that can then be collected by a pytest runner. We could also run the tests explicitly ourselves, or run it as a unittest.TestCase. For more information on testing with hypothesis, see the hypothesis quick start guide.

A more practical example involves using schema transformations. We can modify the function above to make sure that processing_fn actually outputs the correct result:

out_schema = schema.add_columns({"column4": pa.Column(float)})

@pa.check_output(out_schema)
def processing_fn(df):
    return df.assign(column4=df.column1 * df.column2)

@hypothesis.given(schema.strategy(size=5))
def test_processing_fn(dataframe):
    processing_fn(dataframe)

Now the test_processing_fn simply becomes an execution test, raising a SchemaError if processing_fn doesn’t add column4 to the dataframe.

Strategies and Examples from DataFrame Models#

You can also use the class-based API to generate examples. Here’s the equivalent dataframe model for the above examples:

from pandera.typing import Series, DataFrame

class InSchema(pa.DataFrameModel):
    column1: Series[int] = pa.Field(eq=10)
    column2: Series[float] = pa.Field(eq=0.25)
    column3: Series[str] = pa.Field(eq="foo")

class OutSchema(InSchema):
    column4: Series[float]

@pa.check_types
def processing_fn(df: DataFrame[InSchema]) -> DataFrame[OutSchema]:
    return df.assign(column4=df.column1 * df.column2)

@hypothesis.given(InSchema.strategy(size=5))
def test_processing_fn(dataframe):
    processing_fn(dataframe)

Checks as Constraints#

As you may have noticed in the first example, Check s further constrain the data synthesized from a strategy. Without checks, the example method would simply generate any value of the specified type. You can specify multiple checks on a column and pandera should be able to generate valid data under those constraints.

schema_multiple_checks = pa.DataFrameSchema({
    "column1": pa.Column(
        float, checks=[
            pa.Check.gt(0),
            pa.Check.lt(1e10),
            pa.Check.notin([-100, -10, 0]),
        ]
     )
})

for _ in range(5):
    # generate 10 rows of the dataframe
    sample_data = schema_multiple_checks.example(size=3)

    # validate the sampled data
    schema_multiple_checks(sample_data)

One caveat here is that it’s up to you to define a set of checks that are jointly satisfiable. If not, an Unsatisfiable exception will be raised:

schema_multiple_checks = pa.DataFrameSchema({
    "column1": pa.Column(
        float, checks=[
            # nonsensical constraints
            pa.Check.gt(0),
            pa.Check.lt(-10),
        ]
     )
})

schema_multiple_checks.example(size=3)

Traceback (most recent call last):
...
Unsatisfiable: Unable to satisfy assumptions of hypothesis example_generating_inner_function.

Check Strategy Chaining#

If you specify multiple checks for a particular column, this is what happens under the hood:

  • The first check in the list is the base strategy, which hypothesis uses to generate data.

  • All subsequent checks filter the values generated by the previous strategy such that it fulfills the constraints of current check.

To optimize efficiency of the data-generation procedure, make sure to specify the most restrictive constraint of a column as the base strategy and build other constraints on top of it.

In-line Custom Checks#

One of the strengths of pandera is its flexibility with regard to defining custom checks on the fly:

schema_inline_check = pa.DataFrameSchema({
    "col": pa.Column(str, pa.Check(lambda s: s.isin({"foo", "bar"})))
})

One of the disadvantages of this is that the fallback strategy is to simply apply the check to the generated data, which can be highly inefficient. In this case, hypothesis will generate strings and try to find examples of strings that are in the set {"foo", "bar"}, which will be very slow and most likely raise an Unsatisfiable exception. To get around this limitation, you can register custom checks and define strategies that correspond to them.

Defining Custom Strategies via the strategy kwarg#

The Check constructor exposes a strategy keyword argument that allows you to define a data synthesis strategy that can work as a base strategy or chained strategy. For example, suppose you define a custom check that makes sure values in a column are in some specified range.

check = pa.Check(lambda x: x.between(0, 100))

You can then define a strategy for this check with:

def in_range_strategy(pandera_dtype, strategy=None):
    if strategy is None:
        # handle base strategy case
        return st.floats(min_value=min_val, max_value=max_val).map(
            # the map isn't strictly necessary, but shows an example of
            # using the pandera_dtype argument
            strategies.to_numpy_dtype(pandera_dtype).type
        )

    # handle chained strategy case
    return strategy.filter(lambda val: 0 <= val <= 10)

check = pa.Check(lambda x: x.between(0, 100), strategy=in_range_strategy)

Notice that the in_range_strategy function takes two arguments: pandera_dtype, and strategy. pandera_dtype is required, since this is almost always required information when generating data. The strategy argument is optional, where the default case assumes a base strategy, where the check is specified as the first one in the list of checks specified at the column- or dataframe- level.

Defining Custom Strategies via Check Registration#

All built-in Check s are associated with a data synthesis strategy. You can define your own data synthesis strategies by using the extensions API to register a custom check function with a corresponding strategy.