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TablePlotter

A class that contains plotting methods for a table.

Parameters:

Name Type Description Default
table Table The table to plot. -

Examples:

pipeline example {
    val table = Table({"test": [1, 2, 3]});
    val plotter = table.plot;
}
Stub code in TablePlotter.sdsstub 

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class TablePlotter(
    table: Table
) {
    /**
     * Create a box plot for every numerical column.
     *
     * @param theme The color theme of the plot. Default is "light".
     *
     * @result plot The box plot(s) as an image.
     *
     * @example
     * pipeline example {
     *     val table = Table({"a":[1, 2], "b": [3, 42]});
     *     val image = table.plot.boxPlots();
     * }
     */
    @Pure
    @PythonName("box_plots")
    @Category(DataScienceCategory.DataExplorationQVisualization)
    fun boxPlots(
        theme: literal<"dark", "light"> = "light"
    ) -> plot: Image

    /**
     * Create a violin plot for every numerical column.
     *
     * @param theme The color theme of the plot. Default is "light".
     *
     * @result plot The violin plot(s) as an image.
     *
     * @example
     * pipeline example {
     *     val table = Table({"a": [1, 2], "b": [3, 42]});
     *     val image = table.plot.violinPlots();
     * }
     */
    @Pure
    @PythonName("violin_plots")
    @Category(DataScienceCategory.DataExplorationQVisualization)
    fun violinPlots(
        theme: literal<"dark", "light"> = "light"
    ) -> plot: Image

    /**
     * Plot a correlation heatmap for all numerical columns of this `Table`.
     *
     * @param theme The color theme of the plot. Default is "light".
     *
     * @result plot The plot as an image.
     *
     * @example
     * pipeline example {
     *     val table = Table({"temperature": [10, 15, 20, 25, 30], "sales": [54, 74, 90, 206, 210]});
     *     val image = table.plot.correlationHeatmap();
     * }
     */
    @Pure
    @PythonName("correlation_heatmap")
    @Category(DataScienceCategory.DataExplorationQVisualization)
    fun correlationHeatmap(
        theme: literal<"dark", "light"> = "light"
    ) -> plot: Image

    /**
     * Plot a histogram for every column.
     *
     * @param maxBinCount The maximum number of bins to use in the histogram. Default is 10.
     * @param theme The color theme of the plot. Default is "light".
     *
     * @result plot The plot as an image.
     *
     * @example
     * pipeline example {
     *     val table = Table({"a": [2, 3, 5, 1], "b": [54, 74, 90, 2014]});
     *     val image = table.plot.histograms();
     * }
     */
    @Pure
    @Category(DataScienceCategory.DataExplorationQVisualization)
    fun histograms(
        @PythonName("max_bin_count") const maxBinCount: Int = 10,
        theme: literal<"dark", "light"> = "light"
    ) -> plot: Image where {
        maxBinCount > 0
    }

    /**
     * Create a line plot for two columns in the table.
     *
     * @param xName The name of the column to be plotted on the x-axis.
     * @param yNames The name(s) of the column(s) to be plotted on the y-axis.
     * @param showConfidenceInterval Whether a confidence interval is shown.
     * @param theme The color theme of the plot. Default is "light".
     *
     * @result plot The plot as an image.
     *
     * @example
     * pipeline example {
     *     val table = Table(
     *         {
     *             "a": [1, 2, 3, 4, 5],
     *             "b": [2, 3, 4, 5, 6],
     *         }
     *     );
     *     val image = table.plot.linePlot("a", ["b"]);
     * }
     */
    @Pure
    @PythonName("line_plot")
    @Category(DataScienceCategory.DataExplorationQVisualization)
    fun linePlot(
        @PythonName("x_name") xName: String,
        @PythonName("y_names") yNames: List<String>,
        @PythonName("show_confidence_interval") showConfidenceInterval: Boolean = true,
        theme: literal<"dark", "light"> = "light"
    ) -> plot: Image

    /**
     * Create a scatter plot for two columns in the table.
     *
     * @param xName The name of the column to be plotted on the x-axis.
     * @param yNames The name(s) of the column(s) to be plotted on the y-axis.
     * @param theme The color theme of the plot. Default is "light".
     *
     * @result plot The plot as an image.
     *
     * @example
     * pipeline example {
     *     val table = Table(
     *         {
     *             "a": [1, 2, 3, 4, 5],
     *             "b": [2, 3, 4, 5, 6],
     *         }
     *     );
     *     val image = table.plot.scatterPlot("a", ["b"]);
     * }
     */
    @Pure
    @PythonName("scatter_plot")
    @Category(DataScienceCategory.DataExplorationQVisualization)
    fun scatterPlot(
        @PythonName("x_name") xName: String,
        @PythonName("y_names") yNames: List<String>,
        theme: literal<"dark", "light"> = "light"
    ) -> plot: Image

    /**
     * Create a moving average plot for the y column and plot it by the x column in the table.
     *
     * @param xName The name of the column to be plotted on the x-axis.
     * @param yName The name of the column to be plotted on the y-axis.
     * @param windowSize The size of the moving average window
     * @param theme The color theme of the plot. Default is "light".
     *
     * @result plot The plot as an image.
     *
     * @example
     * pipeline example {
     *     val table = Table(
     *         {
     *             "a": [1, 2, 3, 4, 5],
     *             "b": [2, 3, 4, 5, 6],
     *         }
     *     );
     *     val image = table.plot.movingAveragePlot("a", "b", windowSize = 2);
     * }
     */
    @Pure
    @PythonName("moving_average_plot")
    @Category(DataScienceCategory.DataExplorationQVisualization)
    fun movingAveragePlot(
        @PythonName("x_name") xName: String,
        @PythonName("y_name") yName: String,
        @PythonName("window_size") windowSize: Int,
        theme: literal<"dark", "light"> = "light"
    ) -> plot: Image

    /**
     * Create a 2D histogram for two columns in the table.
     *
     * @param xName The name of the column to be plotted on the x-axis.
     * @param yName The name of the column to be plotted on the y-axis.
     * @param xMaxBinCount The maximum number of bins to use in the histogram for the x-axis. Default is 10.
     * @param yMaxBinCount The maximum number of bins to use in the histogram for the y-axis. Default is 10.
     * @param theme The color theme of the plot. Default is "light".
     *
     * @result plot The plot as an image.
     *
     * @example
     * pipeline example {
     *     val table = Table(
     *         {
     *             "a": [1, 2, 3, 4, 5],
     *             "b": [2, 3, 4, 5, 6],
     *         }
     *     );
     *     val image = table.plot.histogram2d("a", "b");
     * }
     */
    @Pure
    @PythonName("histogram_2d")
    @Category(DataScienceCategory.DataExplorationQVisualization)
    fun histogram2d(
        @PythonName("x_name") xName: String,
        @PythonName("y_name") yName: String,
        @PythonName("x_max_bin_count") xMaxBinCount: Int = 10,
        @PythonName("y_max_bin_count") yMaxBinCount: Int = 10,
        theme: literal<"dark", "light"> = "light"
    ) -> plot: Image
}

boxPlots

Create a box plot for every numerical column.

Parameters:

Name Type Description Default
theme literal<"dark", "light"> The color theme of the plot. Default is "light". "light"

Results:

Name Type Description
plot Image The box plot(s) as an image.

Examples:

pipeline example {
    val table = Table({"a":[1, 2], "b": [3, 42]});
    val image = table.plot.boxPlots();
}
Stub code in TablePlotter.sdsstub 

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@Pure
@PythonName("box_plots")
@Category(DataScienceCategory.DataExplorationQVisualization)
fun boxPlots(
    theme: literal<"dark", "light"> = "light"
) -> plot: Image

correlationHeatmap

Plot a correlation heatmap for all numerical columns of this Table.

Parameters:

Name Type Description Default
theme literal<"dark", "light"> The color theme of the plot. Default is "light". "light"

Results:

Name Type Description
plot Image The plot as an image.

Examples:

pipeline example {
    val table = Table({"temperature": [10, 15, 20, 25, 30], "sales": [54, 74, 90, 206, 210]});
    val image = table.plot.correlationHeatmap();
}
Stub code in TablePlotter.sdsstub 

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@Pure
@PythonName("correlation_heatmap")
@Category(DataScienceCategory.DataExplorationQVisualization)
fun correlationHeatmap(
    theme: literal<"dark", "light"> = "light"
) -> plot: Image

histogram2d

Create a 2D histogram for two columns in the table.

Parameters:

Name Type Description Default
xName String The name of the column to be plotted on the x-axis. -
yName String The name of the column to be plotted on the y-axis. -
xMaxBinCount Int The maximum number of bins to use in the histogram for the x-axis. Default is 10. 10
yMaxBinCount Int The maximum number of bins to use in the histogram for the y-axis. Default is 10. 10
theme literal<"dark", "light"> The color theme of the plot. Default is "light". "light"

Results:

Name Type Description
plot Image The plot as an image.

Examples:

pipeline example {
    val table = Table(
        {
            "a": [1, 2, 3, 4, 5],
            "b": [2, 3, 4, 5, 6],
        }
    );
    val image = table.plot.histogram2d("a", "b");
}
Stub code in TablePlotter.sdsstub 

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@Pure
@PythonName("histogram_2d")
@Category(DataScienceCategory.DataExplorationQVisualization)
fun histogram2d(
    @PythonName("x_name") xName: String,
    @PythonName("y_name") yName: String,
    @PythonName("x_max_bin_count") xMaxBinCount: Int = 10,
    @PythonName("y_max_bin_count") yMaxBinCount: Int = 10,
    theme: literal<"dark", "light"> = "light"
) -> plot: Image

histograms

Plot a histogram for every column.

Parameters:

Name Type Description Default
maxBinCount Int The maximum number of bins to use in the histogram. Default is 10. 10
theme literal<"dark", "light"> The color theme of the plot. Default is "light". "light"

Results:

Name Type Description
plot Image The plot as an image.

Examples:

pipeline example {
    val table = Table({"a": [2, 3, 5, 1], "b": [54, 74, 90, 2014]});
    val image = table.plot.histograms();
}
Stub code in TablePlotter.sdsstub 

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@Pure
@Category(DataScienceCategory.DataExplorationQVisualization)
fun histograms(
    @PythonName("max_bin_count") const maxBinCount: Int = 10,
    theme: literal<"dark", "light"> = "light"
) -> plot: Image where {
    maxBinCount > 0
}

linePlot

Create a line plot for two columns in the table.

Parameters:

Name Type Description Default
xName String The name of the column to be plotted on the x-axis. -
yNames List<String> The name(s) of the column(s) to be plotted on the y-axis. -
showConfidenceInterval Boolean Whether a confidence interval is shown. true
theme literal<"dark", "light"> The color theme of the plot. Default is "light". "light"

Results:

Name Type Description
plot Image The plot as an image.

Examples:

pipeline example {
    val table = Table(
        {
            "a": [1, 2, 3, 4, 5],
            "b": [2, 3, 4, 5, 6],
        }
    );
    val image = table.plot.linePlot("a", ["b"]);
}
Stub code in TablePlotter.sdsstub 

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@Pure
@PythonName("line_plot")
@Category(DataScienceCategory.DataExplorationQVisualization)
fun linePlot(
    @PythonName("x_name") xName: String,
    @PythonName("y_names") yNames: List<String>,
    @PythonName("show_confidence_interval") showConfidenceInterval: Boolean = true,
    theme: literal<"dark", "light"> = "light"
) -> plot: Image

movingAveragePlot

Create a moving average plot for the y column and plot it by the x column in the table.

Parameters:

Name Type Description Default
xName String The name of the column to be plotted on the x-axis. -
yName String The name of the column to be plotted on the y-axis. -
windowSize Int The size of the moving average window -
theme literal<"dark", "light"> The color theme of the plot. Default is "light". "light"

Results:

Name Type Description
plot Image The plot as an image.

Examples:

pipeline example {
    val table = Table(
        {
            "a": [1, 2, 3, 4, 5],
            "b": [2, 3, 4, 5, 6],
        }
    );
    val image = table.plot.movingAveragePlot("a", "b", windowSize = 2);
}
Stub code in TablePlotter.sdsstub 

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@Pure
@PythonName("moving_average_plot")
@Category(DataScienceCategory.DataExplorationQVisualization)
fun movingAveragePlot(
    @PythonName("x_name") xName: String,
    @PythonName("y_name") yName: String,
    @PythonName("window_size") windowSize: Int,
    theme: literal<"dark", "light"> = "light"
) -> plot: Image

scatterPlot

Create a scatter plot for two columns in the table.

Parameters:

Name Type Description Default
xName String The name of the column to be plotted on the x-axis. -
yNames List<String> The name(s) of the column(s) to be plotted on the y-axis. -
theme literal<"dark", "light"> The color theme of the plot. Default is "light". "light"

Results:

Name Type Description
plot Image The plot as an image.

Examples:

pipeline example {
    val table = Table(
        {
            "a": [1, 2, 3, 4, 5],
            "b": [2, 3, 4, 5, 6],
        }
    );
    val image = table.plot.scatterPlot("a", ["b"]);
}
Stub code in TablePlotter.sdsstub 

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@Pure
@PythonName("scatter_plot")
@Category(DataScienceCategory.DataExplorationQVisualization)
fun scatterPlot(
    @PythonName("x_name") xName: String,
    @PythonName("y_names") yNames: List<String>,
    theme: literal<"dark", "light"> = "light"
) -> plot: Image

violinPlots

Create a violin plot for every numerical column.

Parameters:

Name Type Description Default
theme literal<"dark", "light"> The color theme of the plot. Default is "light". "light"

Results:

Name Type Description
plot Image The violin plot(s) as an image.

Examples:

pipeline example {
    val table = Table({"a": [1, 2], "b": [3, 42]});
    val image = table.plot.violinPlots();
}
Stub code in TablePlotter.sdsstub 

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@Pure
@PythonName("violin_plots")
@Category(DataScienceCategory.DataExplorationQVisualization)
fun violinPlots(
    theme: literal<"dark", "light"> = "light"
) -> plot: Image