statsplotly package

statsplotly.barplot(data: DataFrame | dict[str, Sequence[ndarray[tuple[Any, ...], dtype[Any]]]] | ndarray[tuple[Any, ...], dtype[Any]], x: str | None = None, y: str | None = None, orientation: Literal['horizontal', 'vertical'] | None = None, slicer: str | None = None, slice_order: list[Any] | None = None, color: str | None = None, color_palette: list[str] | str | None = None, shared_coloraxis: bool = False, color_limits: Sequence[float] | None = None, logscale: float | None = None, colorbar: bool = True, text: str | None = None, axis: Literal['square', 'fixed_ratio', 'equal', 'id_line'] | None = None, opacity: float | None = None, barmode: Literal['stack', 'group', 'overlay', 'relative'] | None = None, error_bar: Literal['sem', 'iqr', 'std', 'geo_std', 'bootstrap'] | Callable[[Any], ndarray[tuple[Any, ...], dtype[Any]]] | None = None, aggregation_func: Literal['mean', 'geo_mean', 'count', 'median', 'percent', 'fraction', 'sum'] | Callable[[Any], float] | None = None, x_label: str | None = None, y_label: str | None = None, title: str | None = None, x_range: Sequence[float | str] | None = None, y_range: Sequence[float | str] | None = None, fig: Figure | None = None, row: int | None = None, col: int | None = None) → Figure

Draws a barplot across levels of categorical variable.

Parameters:

• data – A pandas.DataFrame-compatible structure of data

• x – The name of the x dimension column in data.

• y – The name of the y dimension column in data.

• orientation – A PlotOrientation value to force the orientation of the plot.

• slicer – The name of the column in data with values to slice the data : one trace is drawn for each level of the slicer dimension.

• slice_order – A list of identifiers to order and/or subset data slices specified by slicer.

• color – The name of the column in data with values to map onto the colormap.

• color_palette –

  • A string refering to a built-in plotly, seaborn or matplotlib colormap.

  • A list of CSS color names or HTML color codes.

  The color palette is used, by order of precedence :

  • To map color data specified by the color parameter onto the corresponding

  colormap. - To assign discrete colors to slices of data.

• shared_coloraxis – If True, colorscale limits are shared across slices of data.

• color_limits – A tuple specifying the (min, max) values of the colormap.

• logscale – A float specifying the log base to use for colorscaling.

• colorbar – If True, draws a colorbar.

• text – A string or the name of the column in data with values to appear in the hover

• columns. (tooltip. Column names can be concatenated with '+' to display values from multiple) – Ignored when aggregation_func is not None.

• axis – A AxisFormat value.

• opacity – A numeric value in the (0, 1) interval to specify bar opacity.

• barmode – A BarMode value.

• error_bar – A ErrorBarType value or a Callable taking the x or y dimension as input and returning a (dow, up) limit tuple.

• aggregation_func – A AggregationType value or a Callable taking the x or y dimension as input and returning a single value.

• x_label – A string to label the x_axis in place of the corresponding column name in data.

• y_label – A string to label the y_axis in place of the corresponding column name in data.

• title – A string for the title of the plot.

• x_range – A tuple defining the (min_range, max_range) of the x_axis.

• y_range – A tuple defining the (min_range, max_range) of the y_axis.

• fig – A plotly.graph_obj.Figure to add the plot to. Use in conjunction with row and col.

• row – An integer identifying the row to add the plot to.

• col – An integer identifying the column to add the plot to.

Returns:

A plotly.graph_obj.Figure.

statsplotly.catplot(data: DataFrame | dict[str, Sequence[ndarray[tuple[Any, ...], dtype[Any]]]] | ndarray[tuple[Any, ...], dtype[Any]], x: str | None = None, y: str | None = None, orientation: Literal['horizontal', 'vertical'] | None = None, slicer: str | None = None, slice_order: list[Any] | None = None, color: str | None = None, color_palette: list[str] | str | None = None, shared_coloraxis: bool = False, text: str | None = None, marker: str | None = None, axis: Literal['square', 'fixed_ratio', 'equal', 'id_line'] | None = None, opacity: str | float | None = None, plot_type: Literal['boxplot', 'violinplot', 'stripplot'] | None = None, jitter: float | None = None, normalizer: Literal['center', 'minmax', 'zscore'] | None = None, size: float = 6, x_label: str | None = None, y_label: str | None = None, title: str | None = None, x_range: Sequence[float | str] | None = None, y_range: Sequence[float | str] | None = None, fig: Figure | None = None, row: int | None = None, col: int | None = None) → Figure

Draws a stripplot/boxplot/violinplot across levels of a categorical variable.

Parameters:

• data – A pandas.DataFrame-compatible structure of data

• x – The name of the x dimension column in data.

• y – The name of the y dimension column in data.

• orientation – A PlotOrientation value to force the orientation of the plot.

• slicer – The name of the column in data with values to slice the data : one trace is drawn for each level of the slicer dimension.

• slice_order – A list of identifiers to order and/or subset data slices specified by slicer.

• color – The name of the column in data with values to map onto the colormap.

• color_palette –

  • A string refering to a built-in plotly, seaborn or matplotlib colormap.

  • A list of CSS color names or HTML color codes.

  The color palette is used, by order of precedence :

  • To map color data specified by the color parameter onto the corresponding

  colormap. - To assign discrete colors to slices of data.

• shared_coloraxis – If True, colorscale limits are shared across slices of data.

• text – A string or the name of the column in data with values to appear in the hover

• columns. (tooltip. Column names can be concatenated with '+' to display values from multiple)

• marker – A valid marker symbol or the name of the column in data with values to assign marker symbols.

• axis – A AxisFormat value.

• opacity – A numeric value in the (0, 1) interval or the name of the column in data with values to specify marker opacity.

• plot_type – A CategoricalPlotType value.

• jitter – A numeric value to specify jitter amount on the categorical dimension.

• normalizer – A NormalizationType value to normalize data on the continous dimension.

• size – A numeric value or the name of the column in data with values to assign mark sizes.

• x_label – A string to label the x_axis in place of the corresponding column name in data.

• y_label – A string to label the y_axis in place of the corresponding column name in data.

• title – A string for the title of the plot.

• x_range – A tuple defining the (min_range, max_range) of the x_axis.

• y_range – A tuple defining the (min_range, max_range) of the y_axis.

• fig – A plotly.graph_obj.Figure to add the plot to. Use in conjunction with row and col.

• row – An integer identifying the row to add the plot to.

• col – An integer identifying the column to add the plot to.

Returns:

A plotly.graph_obj.Figure.

statsplotly.distplot(data: DataFrame, x: str | None = None, y: str | None = None, slicer: str | None = None, slice_order: list[Any] | None = None, color_palette: list[str] | str | None = None, axis: Literal['square', 'fixed_ratio', 'equal', 'id_line'] | None = None, opacity: float | None = None, hist: bool = True, rug: bool | None = None, ecdf: bool | None = None, kde: bool | None = None, step: bool | None = None, equal_bins: bool | None = None, bins: Sequence[float] | int | str | None = None, cumulative: bool | None = None, histnorm: Literal['', 'percent', 'probability', 'probability density'] | None = None, central_tendency: Literal['mean', 'median', 'mode'] | None = None, vlines: dict[str, tuple[str, float]] | None = None, hlines: dict[str, tuple[str, float]] | None = None, barmode: Literal['stack', 'overlay'] | None = None, x_label: str | None = None, y_label: str | None = None, title: str | None = None, x_range: Sequence[float] | None = None, y_range: Sequence[float] | None = None, fig: Figure | None = None, row: int | None = None, col: int | None = None) → Figure

Draws the distribution of x (vertical histogram) or y (horizontal histograms) values.

Parameters:

• data – A pandas.DataFrame-compatible structure of data

• x – The name of the x dimension column in data. If not None, draws vertical histograms.

• y – The name of the y dimension column in data. If not None, draws horizontal histograms.

• slicer – The name of the column in data with values to slice the data : one trace is drawn for each level of the slicer dimension.

• slice_order – A list of identifiers to order and/or subset data slices specified by slicer.

• color_palette –

  • A string refering to a built-in plotly, seaborn or matplotlib colormap.

  • A list of CSS color names or HTML color codes.

  The color palette is used to assign discrete colors to slices of data.

• axis – A AxisFormat value.

• opacity – A numeric value in the (0, 1) interval to specify bar and line opacity.

• hist – If True, plot histogram bars.

• rug – If True, plot rug bars of the underlying data.

• ecdf – If True, plot the Empirical Cumulative Density Function.

• kde – If True, plot a line of a Kernel Density Estimation of the distribution.

• step – If True, plot a step histogram instead of a standard histogram bars.

• equal_bins – If True, uses the same bins for all slices in the data.

• bins – A string, integer, or sequence specifying the bins parameter for numpy.histogram().

• cumulative – If True, draws a cumulative histogram.

• histnorm – A HistogramNormType value.

• central_tendency – A CentralTendencyType value.

• vlines – A dictionary of {slice: (line_name, vertical_coordinates)} to draw vertical lines.

• hlines – A dictionary of {slice: (line_name, horizontal_coordinates)} to draw horizontal lines.

• barmode – A HistogramBarMode value.

• x_label – A string to label the x_axis in place of the corresponding column name in data.

• y_label – A string to label the y_axis in place of the corresponding column name in data.

• title – A string for the title of the plot.

• x_range – A tuple defining the (min_range, max_range) of the x_axis.

• y_range – A tuple defining the (min_range, max_range) of the y_axis.

• fig – A plotly.graph_obj.Figure to add the plot to. Use in conjunction with row and col.

• row – An integer identifying the row to add the plot to.

• col – An integer identifying the column to add the plot to.

Returns:

A plotly.graph_obj.Figure.

statsplotly.heatmap(data: DataFrame | dict[str, Sequence[ndarray[tuple[Any, ...], dtype[Any]]]] | ndarray[tuple[Any, ...], dtype[Any]], x: str | None = None, y: str | None = None, z: str | None = None, slicer: str | None = None, slice_order: list[Any] | None = None, color_palette: list[str] | str | None = None, shared_coloraxis: bool = False, color_limits: Sequence[float] | None = None, logscale: float | None = None, colorbar: bool = True, text: str | None = None, axis: Literal['square', 'fixed_ratio', 'equal', 'id_line'] | None = None, opacity: float | None = None, normalizer: Literal['center', 'minmax', 'zscore'] | None = None, x_label: str | None = None, y_label: str | None = None, z_label: str | None = None, title: str | None = None, x_range: Sequence[float | str] | None = None, y_range: Sequence[float | str] | None = None, fig: Figure | None = None, row: int | None = None, col: int | None = None) → Figure

Draws a heatmap.

Parameters:

• data – A pandas.DataFrame-compatible structure of data

• x – The name of the x dimension column in data.

• y – The name of the y dimension column in data.

• z – The name of the z dimension (i.e., color) column in data.

• slicer – The name of the column in data with values to slice the data : one trace is drawn for each level of the slicer dimension.

• slice_order – A list of identifiers to order and/or subset data slices specified by slicer.

• color_palette –

  • A string refering to a built-in plotly, seaborn or matplotlib colormap.

  • A list of CSS color names or HTML color codes.

  The color palette is used, by order of precedence :

  • To map color data specified by the color parameter onto the corresponding

  colormap. - To assign discrete colors to slices of data.

• shared_coloraxis – If True, colorscale limits are shared across slices of data.

• color_limits – A tuple specifying the (min, max) values of the colormap.

• logscale – A float specifying the log base to use for colorscaling.

• colorbar – If True, draws a colorbar.

• text – A string or the name of the column in data with values to appear in the hover

• columns. (tooltip. Column names can be concatenated with '+' to display values from multiple)

• axis – A AxisFormat value.

• opacity – A numeric value in the (0, 1) interval to specify heatmap opacity.

• normalizer – The normalizer for the z dimension. A NormalizationType value.

• x_label – A string to label the x_axis in place of the corresponding column name in data.

• y_label – A string to label the y_axis in place of the corresponding column name in data.

• z_label – A string to label the coloraxis in place of the corresponding column name in data .

• title – A string to label the resulting plot.

• x_range – A tuple defining the (min_range, max_range) of the x_axis.

• y_range – A tuple defining the (min_range, max_range) of the y_axis.

• fig – A plotly.graph_obj.Figure to add the plot to. Use in conjunction with row and col.

• row – An integer identifying the row to add the plot to.

• col – An integer identifying the colum to add the plot to.

Returns:

A plotly.graph_obj.Figure.

statsplotly.jointplot(data: DataFrame | dict[str, Sequence[ndarray[tuple[Any, ...], dtype[Any]]]] | ndarray[tuple[Any, ...], dtype[Any]], x: str | None = None, y: str | None = None, slicer: str | None = None, slice_order: list[Any] | None = None, color: str | None = None, color_palette: list[str] | str | None = None, shared_coloraxis: bool = False, color_limits: Sequence[float] | None = None, logscale: float | None = None, colorbar: bool = True, text: str | None = None, marker: str | None = None, mode: Literal['markers', 'lines', 'markers+lines', 'lines+text'] | None = 'markers', axis: Literal['square', 'fixed_ratio', 'equal', 'id_line'] | None = None, marginal_plot: Literal['x', 'y', 'all'] | None = 'all', kde_color_palette: list[str] | str = 'magma', hist: bool = True, rug: bool | None = None, ecdf: bool | None = None, kde: bool | None = None, step: bool | None = None, equal_bins_x: bool | None = None, equal_bins_y: bool | None = None, bins_x: Sequence[float] | int | str = 'scott', bins_y: Sequence[float] | int | str = 'scott', histnorm: Literal['', 'percent', 'probability', 'probability density'] | None = None, central_tendency: Literal['mean', 'median', 'mode'] | None = None, barmode: Literal['stack', 'overlay'] | None = None, plot_type: Literal['scatter', 'kde', 'scatter+kde', 'x_histmap', 'y_histmap', 'histogram'] = 'scatter', opacity: float = 0.8, jitter_x: float = 0, jitter_y: float = 0, normalizer_x: Literal['center', 'minmax', 'zscore'] | None = None, normalizer_y: Literal['center', 'minmax', 'zscore'] | None = None, shaded_error: str | None = None, error_x: str | None = None, error_y: str | None = None, fit: Literal['linear', 'exponential', 'inverse'] | None = None, size: float | str | None = None, x_label: str | None = None, y_label: str | None = None, title: str | None = None, x_range: Sequence[float | str] | None = None, y_range: Sequence[float | str] | None = None, fig: Figure | None = None, row: int | None = None, col: int | None = None) → Figure

Draws a plot of two variables with bivariate and univariate graphs.

Parameters:

• data – A pandas.DataFrame-compatible structure of data

• x – The name of the x dimension column in data.

• y – The name of the y dimension column in data.

• slicer – The name of the column in data with values to slice the data : one trace is drawn

• dimension. (for each level of the slicer)

• slice_order – A list of identifiers to order and/or subset data slices specified by slicer.

• color – The name of the column in data with values to map onto the colormap. Specifying a

• StatsPlotSpecificationError. (color along with marginal != None raises a)

• color_palette –

  • A string refering to a built-in plotly, seaborn or matplotlib colormap.

  • A list of CSS color names or HTML color codes.

  The color palette is used, by order of precedence :

  • To map color data specified by the color parameter onto the corresponding

  colormap. - To assign discrete colors to slices of data.

• shared_coloraxis – If True, colorscale limits are shared across slices of data.

• color_limits – A tuple specifying the (min, max) values of the colormap.

• logscale – A float specifying the log base to use for colorscaling.

• colorbar – If True, draws a colorbar.

• text – A string or the name of the column in data with values to appear in the hover

• columns. (tooltip. Column names can be concatenated with '+' to display values from multiple)

• marker – A valid marker symbol or the name of the column in data with values to assign

• symbols. (marker)

• mode – A TraceMode value.

• axis – A AxisFormat value.

• opacity – A numeric value in the (0, 1) interval to specify bar and line opacity.

• marginal_plot – A MarginalPlotDimension value.

• kde_color_palette – The color_palette for the Kernel Density Estimation map.

• hist – If True, plot histogram bars.

• rug – If True, plot rug bars of the underlying data.

• ecdf – If True, plot the Empirical Cumulative Density Function.

• kde – If True, plot a line of a Kernel Density Estimation of the distribution.

• step – If True, plot a step histogram instead of a standard histogram bars.

• equal_bins_x – If True, uses the same bins for the x dimension of all slices in the data.

• equal_bins_y – If True, uses the same bins for the y dimension of all slices in the data.

• bins_x – A string, integer, or sequence specifying the bins parameter for the x dimension for numpy.histogram().

• bins_y – A string, integer, or sequence specifying the bins parameter for the y dimension for numpy.histogram().

• histnorm – A HistogramNormType value.

• central_tendency – A CentralTendencyType value.

• barmode – A BarMode value.

• plot_type – A JointplotType value.

• opacity – A numeric value in the (0, 1) interval to specify marker opacity.

• jitter_x – A numeric value to specify jitter amount on the x dimension.

• jitter_y – A numeric value to specify jitter amount on the y dimension.

• normalizer_x – A NormalizationType value to normalize the x dimension.

• normalizer_y – A NormalizationType value to normalize the y dimension.

• shaded_error – The name of the column in data with values to plot continuous error shade.

• error_x – The name of the column in data with values to plot error bar in the x dimension .

• error_y – The name of the column in data with values to plot error bar in the y dimension .

• fit – A RegressionType value. Computes and plot the

• regression. (corresponding)

• size – A numeric value or the name of the column in data with values to assign mark sizes.

• x_label – A string to label the x_axis in place of the corresponding column name in data.

• y_label – A string to label the y_axis in place of the corresponding column name in data.

• title – A string for the title of the plot.

• x_range – A tuple defining the (min_range, max_range) of the x_axis.

• y_range – A tuple defining the (min_range, max_range) of the y_axis.

• fig – A plotly.graph_obj.Figure to add the plot to. Use in conjunction with row and col.

• row – An integer identifying the row to add the plot to.

• col – An integer identifying the column to add the plot to.

Returns:

A plotly.graph_obj.Figure.

statsplotly.plot(data: DataFrame | dict[str, Sequence[ndarray[tuple[Any, ...], dtype[Any]]]] | ndarray[tuple[Any, ...], dtype[Any]], x: str | None = None, y: str | None = None, z: str | None = None, slicer: str | None = None, slice_order: list[Any] | None = None, color: str | None = None, color_palette: list[str] | str | None = None, shared_coloraxis: bool = False, color_limits: Sequence[float] | None = None, logscale: float | None = None, colorbar: bool = True, text: str | None = None, marker: str | None = None, mode: Literal['markers', 'lines', 'markers+lines', 'lines+text'] | None = None, axis: Literal['square', 'fixed_ratio', 'equal', 'id_line'] | None = None, opacity: str | float | None = None, jitter_x: float = 0, jitter_y: float = 0, jitter_z: float = 0, normalizer_x: Literal['center', 'minmax', 'zscore'] | None = None, normalizer_y: Literal['center', 'minmax', 'zscore'] | None = None, normalizer_z: Literal['center', 'minmax', 'zscore'] | None = None, shaded_error: str | None = None, error_x: str | None = None, error_y: str | None = None, error_z: str | None = None, fit: Literal['linear', 'exponential', 'inverse'] | None = None, size: float | str | None = None, x_label: str | None = None, y_label: str | None = None, z_label: str | None = None, title: str | None = None, x_range: Sequence[float | str] | None = None, y_range: Sequence[float | str] | None = None, z_range: Sequence[float | str] | None = None, fig: Figure | None = None, row: int | None = None, col: int | None = None, secondary_y: bool = False) → Figure

Draws a line/scatter plot across levels of a categorical variable.

Parameters:

• data – A pandas.DataFrame-compatible structure of data

• x – The name of the x dimension column in data.

• y – The name of the y dimension column in data.

• z – The name of the z dimension column in data.

• slicer – The name of the column in data with values to slice the data : one trace is drawn

• dimension. (for each level of the slicer)

• slice_order – A list of identifiers to order and/or subset data slices specified by slicer.

• color – The name of the column in data with values to map onto the colormap.

• color_palette –

  • A string refering to a built-in plotly, seaborn or matplotlib colormap.

  • A list of CSS color names or HTML color codes.

  The color palette is used, by order of precedence :

  • To map color data specified by the color parameter onto the corresponding

  colormap. - To assign discrete colors to slices of data.

• shared_coloraxis – If True, colorscale limits are shared across slices of data.

• color_limits – A tuple specifying the (min, max) values of the colormap.

• logscale – A float specifying the log base to use for colorscaling.

• colorbar – If True, draws a colorbar.

• text – A string or the name of the column in data with values to appear in the hover

• columns. (tooltip. Column names can be concatenated with '+' to display values from multiple)

• marker – A valid marker symbol or the name of the column in data with values to assign marker symbols.

• mode – A TraceMode value.

• axis – A AxisFormat value.

• opacity – A numeric value in the (0, 1) interval or the name of the column in data with values to specify marker opacity.

• jitter_x – A numeric value to specify jitter amount on the x dimension.

• jitter_y – A numeric value to specify jitter amount on the y dimension.

• jitter_z – A numeric value to specify jitter amount on the z dimension.

• normalizer_x – A NormalizationType value for the x dimension.

• normalizer_y – A NormalizationType value for the y dimension.

• normalizer_z – A NormalizationType value for the z dimension.

• shaded_error – The name of the column in data with values to plot continuous error shade.

• error_x – The name of the column in data with values to plot error bar in the x dimension .

• error_y – The name of the column in data with values to plot error bar in the y dimension .

• error_z – The name of the column in data with values to plot error bar in the z dimension .

• fit – A RegressionType value. Computes and plot the corresponding regression.

• size – A numeric value or the name of the column in data with values to assign mark sizes.

• x_label – A string to label the x_axis in place of the corresponding column name in data.

• y_label – A string to label the y_axis in place of the corresponding column name in data.

• z_label – A string to label the z_axis in place of the corresponding column name in data.

• title – A string for the title of the plot.

• x_range – A tuple defining the (min_range, max_range) of the x_axis.

• y_range – A tuple defining the (min_range, max_range) of the y_axis.

• z_range – A tuple defining the (min_range, max_range) of the z_axis.

• fig – A plotly.graph_obj.Figure to add the plot to. Use in conjunction with row and col.

• row – An integer identifying the row to add the plot to.

• col – An integer identifying the column to add the plot to.

• secondary_y – If True, plot on a secondary y_axis of the fig object.

Returns:

A plotly.graph_obj.Figure.

Subpackages

• statsplotly.plot_objects package
  • Subpackages
    • statsplotly.plot_objects.layout package
      • BarLayout
      • CategoricalLayout
      • ColorAxis
      • HeatmapLayout
      • HistogramLayout
      • ScatterLayout
      • SceneLayout
  • Submodules
  • statsplotly.plot_objects.trace module
    • BarTrace
      • BarTrace.error_x
      • BarTrace.error_y
      • BarTrace.hoverinfo
      • BarTrace.marker
      • BarTrace.orientation
      • BarTrace.text
      • BarTrace.textposition
      • BarTrace.build_trace()
      • BarTrace.model_post_init()
    • BaseTrace
      • BaseTrace.legendgroup
      • BaseTrace.name
      • BaseTrace.opacity
      • BaseTrace.showlegend
      • BaseTrace.x
      • BaseTrace.y
      • BaseTrace.build_trace()
      • BaseTrace.get_error_bars()
    • BoxTrace
      • BoxTrace.boxmean
      • BoxTrace.build_trace()
      • BoxTrace.model_post_init()
    • ContourTrace
      • ContourTrace.colorscale
      • ContourTrace.hoverinfo
      • ContourTrace.ncontours
      • ContourTrace.reversescale
      • ContourTrace.showscale
      • ContourTrace.build_trace()
      • ContourTrace.model_post_init()
    • EcdfTrace
      • EcdfTrace.hoverinfo
      • EcdfTrace.line
      • EcdfTrace.mode
      • EcdfTrace.build_trace()
      • EcdfTrace.model_post_init()
    • HeatmapTrace
      • HeatmapTrace.colorbar
      • HeatmapTrace.colorscale
      • HeatmapTrace.hoverinfo
      • HeatmapTrace.build_histmap_trace()
      • HeatmapTrace.build_trace()
      • HeatmapTrace.model_post_init()
    • Histogram2dTrace
      • Histogram2dTrace.coloraxis
      • Histogram2dTrace.colorbar
      • Histogram2dTrace.colorscale
      • Histogram2dTrace.histnorm
      • Histogram2dTrace.hoverinfo
      • Histogram2dTrace.marker
      • Histogram2dTrace.xbins
      • Histogram2dTrace.ybins
      • Histogram2dTrace.build_trace()
      • Histogram2dTrace.model_post_init()
    • HistogramLineTrace
      • HistogramLineTrace.hoverinfo
      • HistogramLineTrace.line
      • HistogramLineTrace.mode
      • HistogramLineTrace.showlegend
      • HistogramLineTrace.build_trace()
      • HistogramLineTrace.model_post_init()
    • HistogramTrace
      • HistogramTrace.cumulative
      • HistogramTrace.histnorm
      • HistogramTrace.hoverinfo
      • HistogramTrace.marker
      • HistogramTrace.xbins
      • HistogramTrace.build_trace()
      • HistogramTrace.model_post_init()
    • KdeTrace
      • KdeTrace.hoverinfo
      • KdeTrace.line
      • KdeTrace.mode
      • KdeTrace.build_trace()
      • KdeTrace.model_post_init()
    • RugTrace
      • RugTrace.hoverinfo
      • RugTrace.line
      • RugTrace.mode
      • RugTrace.showlegend
      • RugTrace.text
      • RugTrace.build_trace()
      • RugTrace.model_post_init()
    • Scatter3DTrace
      • Scatter3DTrace.error_z
      • Scatter3DTrace.hoverinfo
      • Scatter3DTrace.z
      • Scatter3DTrace.build_trace()
      • Scatter3DTrace.model_post_init()
    • ScatterTrace
      • ScatterTrace.hoverinfo
      • ScatterTrace.line
      • ScatterTrace.build_id_line()
      • ScatterTrace.build_regression_trace()
      • ScatterTrace.build_trace()
      • ScatterTrace.model_post_init()
    • ShadedTrace
      • ShadedTrace.fill
      • ShadedTrace.fillcolor
      • ShadedTrace.hoverinfo
      • ShadedTrace.line
      • ShadedTrace.build_lower_error_trace()
      • ShadedTrace.build_upper_error_trace()
      • ShadedTrace.model_post_init()
    • StepHistogramTrace
      • StepHistogramTrace.hoverinfo
      • StepHistogramTrace.line
      • StepHistogramTrace.mode
      • StepHistogramTrace.build_trace()
      • StepHistogramTrace.model_post_init()
    • StripTrace
      • StripTrace.mode
      • StripTrace.build_trace()
      • StripTrace.model_post_init()
    • ViolinTrace
      • ViolinTrace.meanline_visible
      • ViolinTrace.scalemode
      • ViolinTrace.build_trace()
      • ViolinTrace.model_post_init()
• statsplotly.plot_specifiers package
  • Subpackages
    • statsplotly.plot_specifiers.color package
      • ColorSpecifier
      • HistogramColorSpecifier
      • rgb_string_array_from_colormap()
      • set_rgb_alpha()
    • statsplotly.plot_specifiers.data package
      • AggregationSpecifier
      • AggregationTraceData
      • AggregationType
      • CentralTendencyType
      • DataDimension
      • DataHandler
      • DataPointer
      • DataProcessor
      • DataTypes
      • ErrorBarType
      • HistogramNormType
      • NormalizationType
      • RegressionType
      • SliceTraceType
      • TraceData
      • Submodules
      • statsplotly.plot_specifiers.data.statistics module
    • statsplotly.plot_specifiers.figure package
      • HistogramPlot
      • JointplotPlot
      • SharedGridAxis
      • SubplotGridFormatter
      • create_fig()
    • statsplotly.plot_specifiers.layout package
      • AxesSpecifier
      • AxisFormat
      • AxisType
      • BarMode
      • ColoraxisReference
      • HistogramBarMode
      • LegendSpecifier
      • PlotAxis
      • add_update_menu()
      • adjust_jointplot_legends()
      • set_horizontal_colorbar()
  • Submodules
  • statsplotly.plot_specifiers.common module
    • register_statsplotly_template()
    • smart_legend()
    • smart_title()
  • statsplotly.plot_specifiers.trace module
    • CategoricalPlotSpecifier
      • CategoricalPlotSpecifier.plot_type
      • CategoricalPlotSpecifier.get_category_strip_map()
      • CategoricalPlotSpecifier.validate_model
      • CategoricalPlotSpecifier.validate_plot_type
    • CategoricalPlotType
      • CategoricalPlotType.BOX
      • CategoricalPlotType.STRIP
      • CategoricalPlotType.VIOLIN
    • HistogramSpecifier
      • HistogramSpecifier.bin_edges
      • HistogramSpecifier.bins
      • HistogramSpecifier.central_tendency
      • HistogramSpecifier.cumulative
      • HistogramSpecifier.data_type
      • HistogramSpecifier.dimension
      • HistogramSpecifier.ecdf
      • HistogramSpecifier.hist
      • HistogramSpecifier.histnorm
      • HistogramSpecifier.kde
      • HistogramSpecifier.rug
      • HistogramSpecifier.step
      • HistogramSpecifier.check_bins
      • HistogramSpecifier.check_cumulative
      • HistogramSpecifier.check_dimension
      • HistogramSpecifier.check_histnorm
      • HistogramSpecifier.check_parameter_consistency
      • HistogramSpecifier.compute_ecdf()
      • HistogramSpecifier.compute_histogram()
      • HistogramSpecifier.get_distribution_max_value()
      • HistogramSpecifier.get_histogram_bin_edges()
      • HistogramSpecifier.density
    • JointplotSpecifier
      • JointplotSpecifier.histogram_specifier
      • JointplotSpecifier.marginal_plot
      • JointplotSpecifier.plot_type
      • JointplotSpecifier.scatter_specifier
      • JointplotSpecifier.check_scatter_specifier
      • JointplotSpecifier.compute_histmap()
      • JointplotSpecifier.histogram2d()
      • JointplotSpecifier.plot_kde
      • JointplotSpecifier.plot_scatter
      • JointplotSpecifier.plot_x_distribution
      • JointplotSpecifier.plot_y_distribution
    • JointplotType
      • JointplotType.HISTOGRAM
      • JointplotType.KDE
      • JointplotType.SCATTER
      • JointplotType.SCATTER_KDE
      • JointplotType.X_HISTMAP
      • JointplotType.Y_HISTMAP
    • MarginalPlotDimension
      • MarginalPlotDimension.ALL
      • MarginalPlotDimension.X
      • MarginalPlotDimension.Y
    • OrientedPlotSpecifier
      • OrientedPlotSpecifier.data_types
      • OrientedPlotSpecifier.prefered_orientation
      • OrientedPlotSpecifier.anchor_dimension
      • OrientedPlotSpecifier.anchored_dimension
      • OrientedPlotSpecifier.orientation
    • PlotOrientation
      • PlotOrientation.HORIZONTAL
      • PlotOrientation.VERTICAL
    • ScatterSpecifier
      • ScatterSpecifier.mode
      • ScatterSpecifier.regression_type
      • ScatterSpecifier.validate_mode
    • TraceMode
      • TraceMode.LINES
      • TraceMode.LINES_TEXT
      • TraceMode.MARKERS
      • TraceMode.MARKERS_LINES

Submodules

statsplotly.constants module

statsplotly.exceptions module

Custom exceptions.

exception statsplotly.exceptions.StatsPlotMissingImplementationError

Bases: Exception

exception statsplotly.exceptions.StatsPlotSpecificationError

Bases: ValueError

Raises when plot arguments are incompatibles.

exception statsplotly.exceptions.UnsupportedColormapError

Bases: Exception

Raises when colormap is not supported.

statsplotly.types module

statsplotly.utils module

pydantic model statsplotly.utils.SubplotGridFormatter

Bases: _SubplotGridValidator

Wraps a Plotly Figure with methods to format the subplot grid.

fig

A plotly.graph_objects.Figure with a subplot grid.

Fields:

Validators:

check_common_xaxes_ticks(col_idx: int) → bool

check_common_yaxes_ticks(row_idx: int) → bool

set_common_axis_limit(shared_grid_axis: Literal['cols', 'rows', 'all'] = 'all', plot_axis: Literal['xaxis', 'yaxis', 'coloraxis'] | None = None, common_range: bool = True, link_axes: bool = False) → SubplotGridFormatter

Set common axis limits along a shared grid axis, optionally linking the axes.

Parameters:

• shared_grid_axis – A SharedGridAxis value.

• plot_axis –

  A PlotAxis value.

  • Defaults to YAXIS when shared_grid_axis = rows.

  • Defaults to XAXIS when shared_grid_axis = cols.

  • Raises a StatsPlotSpecificationError when None and shared_grid_axis = all.

• common_range – If True (default), set a common range for the axes targeted by plot_axis.

• link_axes – If True (default False), link the axes targeted by plot_axis.

Returns:

A SubplotGridFormatter instance.

set_common_coloraxis(shared_grid_axis: SharedGridAxis) → SubplotGridFormatter

Set a common coloraxis along a shared grid axis.

Parameters:

shared_grid_axis – A SharedGridAxis value.

Returns:

A SubplotGridFormatter instance.

set_suplotgrid_titles(grid_axis: GridAxis) → Callable[[...], Any]

tidy_axes() → None

Removes titles and ticks of linked axes in a subplot grid.

tidy_subplots(title: str | None = None, no_legend: bool = False, row_titles: Sequence[str] | None = None, col_titles: Sequence[str] | None = None) → SubplotGridFormatter

Tidy a subplot grid by removing redundant axis titles and optionally adding annotations.

Parameters:

• title – A string for the figure title.

• no_legend – If True, hides the legend.

• row_titles – A list of string the size of the row dimension specifying a title for each row.

• col_titles – A list of string the size of the column dimension specifying a title for each column.

Returns:

A SubplotGridFormatter instance.

statsplotly.utils.rgb_string_array_from_colormap(n_colors: int, color_palette: str | list[str] | list[tuple[float, float, float]] | Colormap | None) → list[str]

Returns a list of RGB string given n_colors and a color_palette reference.

This function attempts to extract RGB color values from built-in Plotly, Seaborn and finally Matplotlib colormaps.