Gets the information necessary to create a box and whisker plot with outliers for a numeric column using the IQR method.


quantiles (dict[float -> float], optional) – A dictionary containing the quantiles for the data where the key indicates the quantile, and the value is the quantile’s value for the data. If no qantiles are provided, they will be computed from the data.


The minimum quantiles necessary for outlier detection using the IQR method are the first quartile (0.25) and third quartile (0.75). If these keys are missing from the quantiles dictionary, the following quantiles will be calculated: {0.0, 0.25, 0.5, 0.75, 1.0}, which correspond to {min, first quantile, median, third quantile, max}.


Returns a dictionary containing box plot information for the Series.

The following elements will be found in the dictionary:

  • low_bound (float): the lower bound below which outliers lay - to be used as a whisker

  • high_bound (float): the high bound above which outliers lay - to be used as a whisker

  • quantiles (list[float]): the quantiles used to determine the bounds.

    If quantiles were passed in, will contain all quantiles passed in. Otherwise, contains the five quantiles {0.0, 0.25, 0.5, 0.75, 1.0}.

  • low_values (list[float, int]): the values of the lower outliers

  • high_values (list[float, int]): the values of the upper outliers

  • low_indices (list[int]): the corresponding index values for each of the lower outliers

  • high_indices (list[int]): the corresponding index values for each of the upper outliers

Return type

(dict[str -> float,list[number]])