Feature Proposal: clean_json functionality in the clean module

[yixuy]

Summary

Implement clean_json() functionality to parse and clean JSON file

Design-level Explanation

• [x] Investigate approaches for matching and validating JSON files.

• [x] Discuss output formats to be supported.

  Design-level Explanation Actions

  def clean_json(
  df: Union[pd.DataFrame, dd.DataFrame],
  col: str,
  fix_missing: str = "empty",
  split: bool = False,
  inplace: bool = False,
  report: bool = True,
  errors: str = "coerce",
  ) -> pd.DataFrame:
  """
  This function cleans JSON string.
  
  Parameters
  ----------
  df
      Pandas or Dask DataFrame.
  col
      Column name containing JSON.
  split
      If True, split a column containing a JSON into different
      columns containing individual components.
  inplace
      If True, delete the given column with dirty data. Else, create a new
      column with cleaned data.
  report
      If True, output the summary report. Else, no report is outputted.
  errors {'ignore', 'raise', 'coerce'}, default 'coerce'.
      * If 'raise', then invalid parsing will raise an exception.
      * If 'coerce', then invalid parsing will be set as NaN.
      * If 'ignore', then invalid parsing will return the input.
  """

  Design-level Explanation

def validate_json(x: Union[str, pd.Series]) -> Union[bool, pd.Series]: """ Function to validate JSON format.

Parameters
----------
x
    String or Pandas Series of JSON to be validated.
"""

Implementation-level Explanation

Rational and Alternatives

Prior Art

JSON Python Library can encode and decode preserve input and output order by default.

Future Possibilities

Implementation-level Actions

Additional Tasks

• [x] This task is put into a correct pipeline (Development Backlog or In Progress).
• [x] The label of this task is setting correctly.
• [x] The issue is assigned to the correct person.
• [x] The issue is linked to related Epic.

• [x] The documentation is changed accordingly.
• [x] Tests are added accordingly.