Pandas (dagster-pandas)

The dagster_pandas library provides utilities for using pandas with Dagster and for implementing validation on pandas DataFrames. A good place to start with dagster_pandas is the validation guide.

dagster_pandas.create_dagster_pandas_dataframe_type(name, description=None, columns=None, event_metadata_fn=None, dataframe_constraints=None, loader=None, materializer=None)[source]

Constructs a custom pandas dataframe dagster type.

Parameters:

  • name (str) – Name of the dagster pandas type.

  • description (Optional[str]) – A markdown-formatted string, displayed in tooling.

  • columns (Optional[List[PandasColumn]]) – A list of PandasColumn objects which express dataframe column schemas and constraints.

  • event_metadata_fn (Optional[Callable[[], Union[Dict[str, Union[str, float, int, Dict, MetadataValue]], List[MetadataEntry]]]]) – A callable which takes your dataframe and returns a dict with string label keys and MetadataValue values. Can optionally return a List[MetadataEntry].

  • dataframe_constraints (Optional[List[DataFrameConstraint]]) – A list of objects that inherit from DataFrameConstraint. This allows you to express dataframe-level constraints.

  • loader (Optional[DagsterTypeLoader]) – An instance of a class that inherits from DagsterTypeLoader. If None, we will default to using dataframe_loader.

  • materializer (Optional[DagsterTypeMaterializer]) – An instance of a class that inherits from DagsterTypeMaterializer. If None, we will default to using dataframe_materializer.

class dagster_pandas.RowCountConstraint(num_allowed_rows, error_tolerance=0)[source]

A dataframe constraint that validates the expected count of rows.

Parameters:

  • num_allowed_rows (int) – The number of allowed rows in your dataframe.

  • error_tolerance (Optional[int]) – The acceptable threshold if you are not completely certain. Defaults to 0.

class dagster_pandas.StrictColumnsConstraint(strict_column_list, enforce_ordering=False)[source]

A dataframe constraint that validates column existence and ordering.

Parameters:

  • strict_column_list (List[str]) – The exact list of columns that your dataframe must have.

  • enforce_ordering (Optional[bool]) – If true, will enforce that the ordering of column names must match. Default is False.

class dagster_pandas.PandasColumn(name, constraints=None, is_required=None)[source]

The main API for expressing column level schemas and constraints for your custom dataframe types.

Parameters:

  • name (str) – Name of the column. This must match up with the column name in the dataframe you expect to receive.

  • is_required (Optional[bool]) – Flag indicating the optional/required presence of the column. If th column exists, the validate function will validate the column. Defaults to True.

  • constraints (Optional[List[Constraint]]) – List of constraint objects that indicate the validation rules for the pandas column.

dagster_pandas.DataFrame = <dagster._core.types.dagster_type.DagsterType object>

Define a type in dagster. These can be used in the inputs and outputs of ops.

Parameters:

  • type_check_fn (Callable[[TypeCheckContext, Any], [Union[bool, TypeCheck]]]) – The function that defines the type check. It takes the value flowing through the input or output of the op. If it passes, return either True or a TypeCheck with success set to True. If it fails, return either False or a TypeCheck with success set to False. The first argument must be named context (or, if unused, _, _context, or context_). Use required_resource_keys for access to resources.

  • key (Optional[str]) –

    The unique key to identify types programmatically. The key property always has a value. If you omit key to the argument to the init function, it instead receives the value of name. If neither key nor name is provided, a CheckError is thrown.

    In the case of a generic type such as List or Optional, this is generated programmatically based on the type parameters.

    For most use cases, name should be set and the key argument should not be specified.

  • name (Optional[str]) – A unique name given by a user. If key is None, key becomes this value. Name is not given in a case where the user does not specify a unique name for this type, such as a generic class.

  • description (Optional[str]) – A markdown-formatted string, displayed in tooling.

  • loader (Optional[DagsterTypeLoader]) – An instance of a class that inherits from DagsterTypeLoader and can map config data to a value of this type. Specify this argument if you will need to shim values of this type using the config machinery. As a rule, you should use the @dagster_type_loader decorator to construct these arguments.

  • materializer (Optional[DagsterTypeMaterializer]) – An instance of a class that inherits from DagsterTypeMaterializer and can persist values of this type. As a rule, you should use the @dagster_type_materializer decorator to construct these arguments.

  • required_resource_keys (Optional[Set[str]]) – Resource keys required by the type_check_fn.

  • is_builtin (bool) – Defaults to False. This is used by tools to display or filter built-in types (such as String, Int) to visually distinguish them from user-defined types. Meant for internal use.

  • kind (DagsterTypeKind) – Defaults to None. This is used to determine the kind of runtime type for InputDefinition and OutputDefinition type checking.

  • typing_type – Defaults to None. A valid python typing type (e.g. Optional[List[int]]) for the value contained within the DagsterType. Meant for internal use.