validmind.BoxPlot
BoxPlot
@tags('tabular_data', 'visualization', 'data_quality')
@tasks('classification', 'regression', 'clustering')
defBoxPlot(dataset:validmind.vm_models.VMDataset,columns:Optional[List[str]]=None,group_by:Optional[str]=None,width:int=1800,height:int=1200,colors:Optional[List[str]]=None,show_outliers:bool=True,title_prefix:str='Box Plot of') → go.Figure:
Generates customizable box plots for numerical features in a dataset with optional grouping using Plotly.
Purpose
This test provides a flexible way to visualize the distribution of numerical features through interactive box plots, with optional grouping by categorical variables. Box plots are effective for identifying outliers, comparing distributions across groups, and understanding the spread and central tendency of the data.
Test Mechanism
The test creates interactive box plots for specified numerical columns (or all numerical columns if none specified). It supports various customization options including:
- Grouping by categorical variables
- Customizable colors and styling
- Outlier display options
- Interactive hover information
- Zoom and pan capabilities
Signs of High Risk
- Presence of many outliers indicating data quality issues
- Highly skewed distributions
- Large differences in variance across groups
- Unexpected patterns in grouped data
Strengths
- Clear visualization of distribution statistics (median, quartiles, outliers)
- Interactive Plotly plots with hover information and zoom capabilities
- Effective for comparing distributions across groups
- Handles missing values appropriately
- Highly customizable appearance
Limitations
- Limited to numerical features only
- May not be suitable for continuous variables with many unique values
- Visual interpretation may be subjective
- Less effective with very large datasets