woodwork.column_accessor.WoodworkColumnAccessor.box_plot_dict#

WoodworkColumnAccessor.box_plot_dict(quantiles: Optional[Dict[int, int]] = None, include_indices_and_values: bool = True, ignore_zeros: bool = False)[source]#

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

Parameters:

  • 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 quantiles are provided, they will be computed from the data.

  • include_indices_and_values (bool, optional) – Whether or not the lists containing individual outlier values and their indices will be included in the returned dictionary. Defaults to True.

  • ignore_zeros (bool) – Whether to ignore 0 values (not NaN values) when calculating the box plot and outliers. Defaults to False.

Note

The minimum quantiles necessary for building a box plot using the IQR method are the minimum value (0.0 in the quantiles dict), first quartile (0.25), third quartile (0.75), and maximum value (1.0). If no quantiles are provided, 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:

Returns a dictionary containing box plot information for the Series.

The following elements will be found in the dictionary:

  • low_bound (float): the lowest data point in the dataset excluding any outliers - to be used as a whisker

  • high_bound (float): the highest point in the dataset excluding any outliers - 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], optional): the values of the lower outliers.

    Will not be included if include_indices_and_values is False.

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

    Will not be included if include_indices_and_values is False.

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

    Will not be included if include_indices_and_values is False.

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

    Will not be included if include_indices_and_values is False.

  • method (str): the method used to identify outliers, in this case box_plot

Return type:

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