validmind.HistogramPlot
HistogramPlot
@tags('tabular_data', 'visualization', 'data_quality')
@tasks('classification', 'regression', 'clustering')
defHistogramPlot(dataset:validmind.vm_models.VMDataset,columns:Optional[List[str]]=None,bins:Union[int, str, List]=30,color:str='steelblue',opacity:float=0.7,show_kde:bool=True,normalize:bool=False,log_scale:bool=False,title_prefix:str='Histogram of',width:int=1200,height:int=800,n_cols:int=2,vertical_spacing:float=0.15,horizontal_spacing:float=0.1) → go.Figure:
Generates customizable histogram plots for numerical features in a dataset using Plotly.
Purpose
This test provides a flexible way to visualize the distribution of numerical features in a dataset. It allows for extensive customization of the histogram appearance and behavior through parameters, making it suitable for various exploratory data analysis tasks.
Test Mechanism
The test creates histogram plots for specified numerical columns (or all numerical columns if none specified). It supports various customization options including:
- Number of bins or bin edges
- Color and opacity
- Kernel density estimation overlay
- Logarithmic scaling
- Normalization options
- Configurable subplot layout (columns and spacing)
Signs of High Risk
- Highly skewed distributions that may indicate data quality issues
- Unexpected bimodal or multimodal distributions
- Presence of extreme outliers
- Empty or sparse distributions
Strengths
- Highly customizable visualization options
- Interactive Plotly plots with zoom, pan, and hover capabilities
- Supports both single and multiple column analysis
- Provides insights into data distribution patterns
- Can handle different data types and scales
- Configurable subplot layout for better visualization
Limitations
- Limited to numerical features only
- Visual interpretation may be subjective
- May not be suitable for high-dimensional datasets
- Performance may degrade with very large datasets