content: #  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at:
  #
  #       https://www.apache.org/licenses/LICENSE-2.0
  #
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  """Great Expectations data profiling standard step."""
  
  from typing import Any, Dict, Optional
  
  import pandas as pd
  from great_expectations.core import (  # type: ignore[import-untyped]
      ExpectationSuite,
  )
  
  from zenml import step
  from zenml.integrations.great_expectations.data_validators.ge_data_validator import (
      GreatExpectationsDataValidator,
  )
  
  
  @step
  def great_expectations_profiler_step(
      dataset: pd.DataFrame,
      expectation_suite_name: str,
      data_asset_name: Optional[str] = None,
      profiler_kwargs: Optional[Dict[str, Any]] = None,
      overwrite_existing_suite: bool = True,
  ) -> ExpectationSuite:
      """Infer data validation rules from a pandas dataset.
  
      Args:
          dataset: The dataset from which the expectation suite will be inferred.
          expectation_suite_name: The name of the expectation suite to infer.
          data_asset_name: The name of the data asset to profile.
          profiler_kwargs: A dictionary of keyword arguments to pass to the
              profiler.
          overwrite_existing_suite: Whether to overwrite an existing expectation
              suite.
  
      Returns:
          The generated Great Expectations suite.
      """
      data_validator = GreatExpectationsDataValidator.get_active_data_validator()
  
      return data_validator.data_profiling(
          dataset,
          expectation_suite_name=expectation_suite_name,
          data_asset_name=data_asset_name,
          profiler_kwargs=profiler_kwargs or {},
          overwrite_existing_suite=overwrite_existing_suite,
      )
  