Expect the specific provided column quantiles to be between a minimum value and a maximum value.

expect_column_quantile_values_to_be_between is a Column Aggregate Expectation.

For example:

# my_df.my_col = [1,2,2,3,3,3,4]
>>> my_df.expect_column_quantile_values_to_be_between(
    "my_col",
    {
        "quantiles": [0., 0.333, 0.6667, 1.],
        "value_ranges": [[0,1], [2,3], [3,4], [4,5]]
    }
)
{
  "success": True,
    "result": {
      "observed_value": {
        "quantiles: [0., 0.333, 0.6667, 1.],
        "values": [1, 2, 3, 4],
      }
      "element_count": 7,
      "missing_count": 0,
      "missing_percent": 0.0,
      "details": {
        "success_details": [true, true, true, true]
      }
    }
  }
}

expect_column_quantile_values_to_be_between can be computationally intensive for large datasets.

Args:

• column (str): The column name.
• quantile_ranges (dictionary with keys 'quantiles' and 'value_ranges'): Key 'quantiles' is an increasingly ordered list of desired quantile values (floats). Key 'value_ranges' is a list of 2-value lists that specify a lower and upper bound (inclusive) for the corresponding quantile (with [min, max] ordering). The length of the 'quantiles' list and the 'value_ranges' list must be equal.
• allow_relative_error (boolean or string): Whether to allow relative error in quantile communications on backends that support or require it.

Other Parameters:

• result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see result_format.
• include_config (boolean): If True, then include the expectation config as part of the result object.
• catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see catch_exceptions.
• meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see meta.

Returns:

• An ExpectationSuiteValidationResult

Exact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.

Notes:

• min_value and max_value are both inclusive.
• If min_value is None, then max_value is treated as an upper bound only
• If max_value is None, then min_value is treated as a lower bound only
• details.success_details field in the result object is customized for this expectation

See Also:

• expect_column_min_to_be_between
• expect_column_max_to_be_between
• expect_column_median_to_be_between