How to Avoid Pandera Doc Injection? about pandera HOT 5 OPEN

class ExampleSchema(pa.SchemaModel):
    """Schema to demonstrate doc injection."""

    Column1: sc.Integer = sc.IntegerF()
    Column2: sc.Str = sc.StrF()

class jane_dev.options.utils.doc_testing.ExampleSchema(*args, **kwargs)

   Bases: "pandera.api.pandas.model.DataFrameModel"

   Schema to demonstrate doc injection.

   Check if all columns in a dataframe have a column in the Schema.

   Parameters:
      * **check_obj** (*pd.DataFrame*) -- the dataframe to be
        validated.

      * **head** -- validate the first n rows. Rows overlapping with
        "tail" or "sample" are de-duplicated.

      * **tail** -- validate the last n rows. Rows overlapping with
        "head" or "sample" are de-duplicated.

      * **sample** -- validate a random sample of n rows. Rows
        overlapping with "head" or "tail" are de-duplicated.

      * **random_state** -- random seed for the "sample" argument.

      * **lazy** -- if True, lazily evaluates dataframe against all
        validation checks and raises a "SchemaErrors". Otherwise,
        raise "SchemaError" as soon as one occurs.

      * **inplace** -- if True, applies coercion to the object of
        validation, otherwise creates a copy of the data.

   Returns:
      validated "DataFrame"

   Raises:
      **SchemaError** -- when "DataFrame" violates built-in or custom
      checks.

   Example:
   Calling "schema.validate" returns the dataframe.

   >>> import pandas as pd
   >>> import pandera as pa
   >>>
   >>> df = pd.DataFrame({
   ...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],
   ...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]
   ... })
   >>>
   >>> schema_withchecks = pa.DataFrameSchema({
   ...     "probability": pa.Column(
   ...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),
   ...
   ...     # check that the "category" column contains a few discrete
   ...     # values, and the majority of the entries are dogs.
   ...     "category": pa.Column(
   ...         str, [
   ...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),
   ...             pa.Check(lambda s: (s == "dog").mean() > 0.5),
   ...         ]),
   ... })
   >>>
   >>> schema_withchecks.validate(df)[["probability", "category"]]
      probability category
   0         0.10      dog
   1         0.40      dog
   2         0.52      cat
   3         0.23     duck
   4         0.80      dog
   5         0.76      dog

   Column1: pandera.typing.pandas.Series[pandas.core.arrays.integer.Int64Dtype] = 'Column1'

   Column2: pandera.typing.pandas.Series[str] = 'Column2'

   class Config

      Bases: "pandera.api.pandas.model_config.BaseConfig"

      name: str | None = 'ExampleSchema'

         name of schema

class jane_dev.options.utils.doc_testing.ExampleSchema(*args, **kwargs)

   Bases: "pandera.api.pandas.model.DataFrameModel"

   Schema to demonstrate doc injection.

   Column1: pandera.typing.pandas.Series[pandas.core.arrays.integer.Int64Dtype] = 'Column1'

   Column2: pandera.typing.pandas.Series[str] = 'Column2'