Visualization

Statistical plotting and visualization utilities.

Plots

Publication-quality plot generation with metadata support.

Plotting utilities for statistical visualizations.

Creates publication-quality plots for insights.

class statqa.visualization.plots.PlotFactory(style='whitegrid', context='notebook', figsize=(8, 6), dpi=100)

Bases: object

Factory for creating statistical visualizations.

Parameters:

• style (Literal['whitegrid', 'darkgrid', 'white', 'dark', 'ticks'])

• context (Literal['paper', 'notebook', 'talk', 'poster'])

• figsize (tuple[int, int])

• dpi (int)

plot_univariate(data: Series, variable: Variable, output_path: str | Path | None = None, *, return_metadata: Literal[False] = False) → Figure

plot_univariate(data: Series, variable: Variable, output_path: str | Path | None = None, *, return_metadata: Literal[True]) → tuple[Figure, dict[str, Any]]

Create univariate plot (histogram or bar chart).

Parameters:

• data (Series) – Data series

• variable (Variable) – Variable metadata

• output_path (str | Path | None) – Optional path to save plot

• return_metadata (bool) – Whether to return plot metadata alongside figure

Returns:

Matplotlib figure, or tuple of (figure, metadata) if return_metadata=True

Return type:

Figure | tuple[Figure, dict[str, Any]]

plot_bivariate(data: DataFrame, var1: Variable, var2: Variable, output_path: str | Path | None = None, *, return_metadata: Literal[False] = False) → Figure

plot_bivariate(data: DataFrame, var1: Variable, var2: Variable, output_path: str | Path | None = None, *, return_metadata: Literal[True]) → tuple[Figure, dict[str, Any]]

Create bivariate plot (scatter, box, or heatmap).

Parameters:

• data (DataFrame) – DataFrame with both variables

• var1 (Variable) – First variable

• var2 (Variable) – Second variable

• output_path (str | Path | None) – Optional path to save plot

• return_metadata (bool) – Whether to return plot metadata alongside figure

Returns:

Matplotlib figure, or tuple of (figure, metadata) if return_metadata=True

Return type:

Figure | tuple[Figure, dict[str, Any]]

plot_temporal(data, time_var, value_var, group_var=None, output_path=None)

Create temporal trend plot.

Parameters:

• data (DataFrame) – DataFrame with time and value

• time_var (Variable) – Time variable

• value_var (Variable) – Value variable

• group_var (Variable | None) – Optional grouping variable

• output_path (str | Path | None) – Optional path to save plot

Returns:

Matplotlib figure

Return type:

Figure

close_all()

Close all open figures.

Return type:

None

Themes

Matplotlib/Seaborn theme configuration.

Consistent theming for visualizations.

statqa.visualization.themes.setup_theme(style='publication')

Set up matplotlib/seaborn theme.

Parameters:

style (Literal['publication', 'presentation', 'notebook']) – Theme style (‘publication’, ‘presentation’, ‘notebook’)

Raises:

ValueError – If style is not supported

Return type:

None