Welcome to Data Quality Whistler’s documentation!

DQ Analyzer

class dq_whistler.analyzer.DataQualityAnalyzer(data: Union[pyspark.sql.dataframe.DataFrame, pandas.core.frame.DataFrame], config: List[Dict[str, str]])

Analyzer class responsible for taking JSON dict and executing it on the columnar data

Parameters

• data (pyspark.sql.DataFrame | pandas.core.series.Series) – Dataframe/Series containing the data

• config (List[Dict[str, str]]) – The array of dicts containing config for each column

analyze() → str

Returns

JSON string containing stats for multiple columns

Return type

str

class dq_whistler.analyzer.NpEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)

default(obj)

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

Constraints

class dq_whistler.constraints.constraint.Constraint(constraint: Dict[str, str], column_name: str)

Defines the base Constraint class

constraint_name()

Returns

The name of the constraint

Return type

str

execute_check(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Dict[str, str]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dict containing the final output for one constraint Example Output:

{
    "name": "eq",
    "values", 5,
    "constraint_status": "failed/success",
    "invalid_count": 21,
    "invalid_values": [4, 6, 7, 1]
}

Return type

dict[str, str]

get_column_name()

Returns

The name of the column for which the Constraint instance was created

Return type

str

abstract get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe containing failed cases for a constraint

Return type

pyspark.sql.DataFrame

get_sample_invalid_values(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → List

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

A list containing the invalid values as per the given constraint

Return type

list

Numeric Constraints

class dq_whistler.constraints.number_type.Between(constraint: Dict[str, str], column_name: str)

Between constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"between",
          "values": [3, 4]
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is between [2, 8], then the dataframe will have rows where values are not in between [2, 8] (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.Equal(constraint: Dict[str, str], column_name: str)

Equal constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"eq",
          "values": 5
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint, for ex: if constraint is eq to 5, then the dataframe will have rows where values are != 5 (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.GreaterThan(constraint: Dict[str, str], column_name: str)

GreaterThan constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"gt",
          "values": 5
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is gt 5, then the dataframe will have rows where values are <= 5 (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.GreaterThanEqualTo(constraint: Dict[str, str], column_name: str)

GreaterThanEqualTo constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"gt_eq",
          "values": 5
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is gt_eq to 5, then the dataframe will have rows where values are < 5 (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.IsIn(constraint: Dict[str, str], column_name: str)

IsIn constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"is_in",
          "values": [1, 2, 3]
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is is_in [1, 2, 3], then the dataframe will have rows where values are in [1, 2, 3] (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.LessThan(constraint: Dict[str, str], column_name: str)

LessThan constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"lt",
          "values": 5
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is lt 5, then the dataframe will have rows where values are >= 5 (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.LessThanEqualTo(constraint: Dict[str, str], column_name: str)

LessThanEqualTo constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"lt_eq",
          "values": 5
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is lt_eq to 5, then the dataframe will have rows where the values are > 5 (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.NotBetween(constraint: Dict[str, str], column_name: str)

NotBetween constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"not_between",
          "values": [3, 5]
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is not_between [2,8], then the dataframe will have rows where values are in between [2, 8] (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.NotEqual(constraint: Dict[str, str], column_name: str)

NotEqual constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"nt_eq",
          "values": 5
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is nt_eq to 5, then the dataframe will have rows where values are = 5 (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.number_type.NotIn(constraint: Dict[str, str], column_name: str)

NotIn constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"not_in",
          "values": [1, 2, 3]
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is “not_in” [1, 2, 3], then the dataframe will have rows where values are in [1, 2, 3] (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

String Constraints

class dq_whistler.constraints.string_type.Contains(constraint: Dict[str, str], column_name: str)

Contains constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"contains",
          "values": "abc"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is contains "abc", then the dataframe will have rows where values does not contains "abc" (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.EndsWith(constraint: Dict[str, str], column_name: str)

EndsWith constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"ends_with",
          "values": "abc"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is ends_with "abc", then the dataframe will have rows where values does not ends with "abc" (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.Equal(constraint: Dict[str, str], column_name: str)

Equal constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"eq",
          "values": "abc"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is eq to "abc", then the dataframe will have rows where values are != "abc" (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.IsIn(constraint: Dict[str, str], column_name: str)

IsIn constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"is_in",
          "values": ["abc", "xyz"]
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is is_in ["abc", "xyz"], then the dataframe will have rows where values are not in ["abc", "xyz"] (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.NotContains(constraint: Dict[str, str], column_name: str)

NotContains constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"not_contains",
          "values": "abc"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is not_contains abc, then the dataframe will have rows where values contains "abc" (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.NotEndsWith(constraint: Dict[str, str], column_name: str)

NotEndsWith constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"not_ends_with",
          "values": "abc"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is not_ends_with "abc", then the dataframe will have rows where values ends with "abc" (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.NotEqual(constraint: Dict[str, str], column_name: str)

NotEqual constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"nt_eq",
          "values": "abc"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is nt_eq to "abc", then the dataframe will have rows where values are == "abc" (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.NotIn(constraint: Dict[str, str], column_name: str)

NotIn constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"not_in",
          "values": ["abc", "xyz"]
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is not_in ["abc", "xyz"], then the dataframe will have rows where values are in ["abc", "xyz"] (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.NotStartsWith(constraint: Dict[str, str], column_name: str)

NotStartsWith constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"not_starts_with",
          "values": "abc"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is not_starts_with "abc", then the dataframe will have rows where values starts with "abc" (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.Regex(constraint: Dict[str, str], column_name: str)

Regex constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"regex",
          "values": "^[A-Za-z]$"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is regex ^[A-Za-z]$, then the dataframe will have rows where values does not satisfies the regex ^[A-Za-z]$ (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

class dq_whistler.constraints.string_type.StartsWith(constraint: Dict[str, str], column_name: str)

StartsWith constraint class that extends the base Constraint class

Parameters

• constraint (Dict[str, str]) –

  The dict representing a constraint config

  {
          "name":"starts_with",
          "values": "abc"
  }
  

• column_name (str) – The name of the column for constraint check

get_failure_df(data_frame: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]) → Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series]

Parameters

data_frame (pyspark.sql.DataFrame | pandas.core.series.Series) – Column data

Returns

The dataframe with invalid cases as per the constraint for ex: if constraint is starts_with "abc", then the dataframe will have rows where values does not starts with "abc" (i.e only invalid cases)

Return type

pyspark.sql.DataFrame | pandas.core.series.Series

Profilers

class dq_whistler.profiler.column_profiler.ColumnProfiler(column_data: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series], config: Dict[str, Any])

Base class for column profiler

add_constraint(constraint: dq_whistler.constraints.constraint.Constraint)

Adds an instance of Constraint to the the parent list of constraints for this profiler

Parameters

constraint (dq_whistler.constraints.constraint.Constraint) – An instance of Constraint class

get_column_config() → Dict[str, Any]

Returns

The data quality config for the column

Return type

Dict[str, Any]

get_column_info() → str

Returns

The column info for which the instance has been created Sample output:

str({
    "fields":[
        {
            "metadata":{},
            "name":"col_name",
            "nullable":True,
            "type":"string"
        }
    ],
    "type":"struct"
})

Return type

str

get_constraints_config() → List[Dict[str, str]]

Returns

The array containing the constraints for the column

Return type

List[Dict[str, str]]

get_custom_constraint_check() → List[Dict[str, str]]

Returns

An array containing the output of each of the constraint for a column Sample Output:

[
    {
        "name": "eq",
        "values", 5,
        "constraint_status": "failed/success",
        "invalid_count": 21,
        "invalid_values": [4, 6, 7, 1]
    }...
]

Return type

List[Dict[str, str]]

get_null_count() → int

Returns

Count of null values in a column data

Return type

int

get_quality_score() → float

Returns

Overall quality score of a column

Return type

float

get_topn() → Dict[str, Any]

Returns

Dict containing the top 10 values along with their counts Sample Output:

{
    "value1": count1,
    "value2": count2
}

Return type

Dict[str, Any]

get_total_count() → int

Returns

Count of total values in a column data

Return type

int

get_unique_count() → int

Returns

Count of unique values in a column data

Return type

int

prepare_df_for_constraints() → None

Prepares a dataframe by doing pre validations

abstract run() → Dict[str, Any]

Returns

The final stats of the column containing null count, total count, regex count, invalid rows, quality score etc.

Return type

Dict[str, Any]

Numeric Profiler

class dq_whistler.profiler.number_profiler.NumberProfiler(column_data: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series], config: Dict[str, str])

Class for Numeric datatype profiler

get_max_value() → float

Returns

Max value of the column data

Return type

float

get_mean_value() → float

Returns

Mean value of the column data

Return type

float

get_min_value() → float

Returns

Min value of the column data

Return type

float

get_stddev_value() → float

Returns

Standard deviation value of the column value

Return type

float

run() → Dict[str, Any]

Returns

The final dict with all the metrics of a numeric column Example Output:

{
        "total_count": 100,
        "null_count": 50,
        "unique_count": 20,
        "topn_values": {"1": 24, "2": 25},
        "min": 2.0,
        "max": 30.0,
        "mean": 18.0,
        "stddev": 5.0,
        "quality_score": 0,
        "constraints": [
                {
                        "name": "eq",
                        "values", 5,
                        "constraint_status": "failed/success",
                        "invalid_count": 21,
                        "invalid_values": [4, 6, 7, 1]
                }
        ]
}

Return type

Dict[str, Any]

String Profiler

class dq_whistler.profiler.string_profiler.StringProfiler(column_data: Union[pyspark.sql.dataframe.DataFrame, pandas.core.series.Series], config: Dict[str, str])

Class for String datatype profiler

run() → Dict[str, Any]

Returns

The final dict with all the metrics of a string column Example Output:

{
        "total_count": 100,
        "null_count": 50,
        "unique_count": 20,
        "topn_values": {"abc": 24, "xyz": 25},
        "quality_score": 0,
        "constraints": [
                {
                        "name": "eq",
                        "values", "abc",
                        "constraint_status": "failed/success",
                        "invalid_count": 21,
                        "invalid_values": ["xy", "ab", "abcd"]
                }
        ]
}

Return type

Dict[str, Any]