Visualization

statista.time_series.visualization

Visualization mixin for TimeSeries.

Visualization

Bases: _TimeSeriesStub

Visualization methods for TimeSeries.

Source code in src/statista/time_series/visualization.py

class Visualization(_TimeSeriesStub):
    """Visualization methods for TimeSeries."""

    def box_plot(
        self, mean: bool = False, notch: bool = False, **kwargs
    ) -> tuple[Figure, Axes]:
        """a box and whisker plot.

        The box extends from the first quartile (Q1) to the third quartile (Q3) of the data, with a line at the median.
        The whiskers extend from the box to the farthest data point lying within 1.5x the inter-quartile range (IQR)
        from the box.
        Flier points are those past the end of the whiskers. See https://en.wikipedia.org/wiki/Box_plot for reference.

        The box plot can give the following insights:
            - Summary of Distribution: A box plot provides a graphical summary of the distribution of data based on five
                summary statistics: the minimum, first quartile (Q1), median, third quartile (Q3), and maximum.
            - Outliers: It highlights outliers, which are data points that fall significantly above or below the rest of
                the data. Outliers are typically shown as individual points beyond the "whiskers" of the box plot.
            - Central Tendency: The line inside the box indicates the median (50th percentile), giving insight into the
                central tendency of the data.
            - Spread and Skewness: The length of the box (interquartile range, IQR) shows the spread of the middle 50% of
                the data, while the position of the median line within the box can suggest skewness.

                      Q1-1.5IQR   Q1   median  Q3   Q3+1.5IQR
                                   |-----:-----|
                   o      |--------|     :     |--------|    o  o
                                   |-----:-----|
                 flier             <----------->            fliers
                                        IQR

        Use Case:
            - Useful for quickly comparing the distribution of the time series data and identifying any anomalies or
                outliers.

        Args:
            mean: bool, optional, default is False.
                Whether to show the mean value in the box plot.
            notch: bool, optional, default is False.
                    Whether to draw a notched boxplot (`True`), or a rectangular
                    boxplot (`False`).  The notches represent the confidence interval
                    (CI) around the median.  The documentation for *bootstrap*
                    describes how the locations of the notches are computed by
                    default, but their locations may also be overridden by setting the
                    *conf_intervals* parameter.
            **kwargs: dict, optional
                fig: matplotlib.figure.Figure, optional
                    Existing figure to plot on. If None, a new figure is created.
                ax: matplotlib.axes.Axes, optional
                    Existing axes to plot on. If None, a new axes is created.
                grid: bool, optional, Default is False.
                    Whether to show grid lines.
                color: dict, optional, default is None.
                    Colors to use for the plot elements. Default is None.
                    ```color = {"boxes", "#27408B"}```
                title: str, optional
                    Title of the plot.
                xlabel: str, optional
                    Label for the x-axis.
                ylabel: str, optional
                    Label for the y-axis.

        Returns:
            fig: matplotlib.figure.Figure
                The figure object containing the plot.
            ax: matplotlib.axes.Axes
                The axes object containing the plot.

        Examples:
            - Plot the box plot for a 1D time series:
                ```python
                >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
                >>> fig, ax = ts.box_plot()  # doctest: +SKIP

                ```
                ![box_plot_1d](./../_images/time_series/box_plot_1d.png)

            - Plot the box plot for a multiple time series:
                ```python
                >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
                >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
                >>> fig, ax = ts_2d.box_plot(mean=True, grid=True)  # doctest: +SKIP

                ```
                ![box_plot_2d](./../_images/time_series/box_plot_2d.png)
                ```python
                >>> fig, ax = ts_2d.box_plot(grid=True, mean=True, color={"boxes": "#DC143C"})  # doctest: +SKIP

                ```
                ![box_plot_color](./../_images/time_series/box_plot_color.png)
                ```python
                >>> fig, ax = ts_2d.box_plot(xlabel='Custom X', ylabel='Custom Y', title='Custom Box Plot')  # doctest: +SKIP

                ```
                ![box_plot_axes-label](./../_images/time_series/box_plot_axes-label.png)
                ```python
                >>> fig, ax = ts_2d.box_plot(notch=True)  # doctest: +SKIP

                ```
                ![box_plot_notch](./../_images/time_series/box_plot_notch.png)
        """
        fig, ax = self._get_ax_fig(**kwargs)
        kwargs.pop("fig", None)
        kwargs.pop("ax", None)
        color = kwargs.get("color", None)
        data = [self[col].dropna() for col in self.columns]
        ax.boxplot(
            data,
            notch=notch,
            patch_artist=True,
            showmeans=mean,
            meanprops=BOX_MEAN_PROP,
            boxprops={
                "facecolor": (
                    "#27408B" if color is None else color.get("boxes", "#27408B")
                )
            },
        )
        ax = self._adjust_axes_labels(
            ax,
            self.columns,
            **kwargs,
        )

        plt.show()
        return fig, ax

    @staticmethod
    def calculate_whiskers(data: np.ndarray | list, q1: float, q3: float):
        """Calculate the upper and lower whiskers for a box plot.

        Args:
            data: np.ndarray
                Input array of data.
            q1: float
                first quartile
            q3: float
                third quartile

        Returns:
            lower_wisker: float
                Lower whisker value.
            upper_wisker: float
                Upper whisker value.
        """
        inter_quartile = q3 - q1
        upper_whisker = q3 + inter_quartile * 1.5
        upper_whisker = np.clip(upper_whisker, q3, data[-1])

        lower_whisker = q1 - inter_quartile * 1.5
        lower_whisker = np.clip(lower_whisker, data[0], q1)
        return lower_whisker, upper_whisker

    def violin(
        self,
        mean: bool = True,
        median: bool = False,
        extrema: bool = True,
        side: Literal["both", "low", "high"] = "both",
        spacing: int = 0,
        **kwargs,
    ) -> tuple[Figure, Axes]:
        """
        Plots a violin plot of the time series data.

        Args:
            mean: bool, optional, default is True.
                Whether to show the means in the violin plot.
            median: bool, optional, default is False.
                Whether to show the median in the violin plot.
            extrema: bool, optional, default is False.
                Whether to show the minima and maxima in the violin plot.
            side: {'both', 'low', 'high'}, default: 'both'
                'both' plots standard violins. 'low'/'high' only
                plots the side below/above the position value.
            spacing: int, optional, default is 0.
                The spacing (number of ticks) between the violins.
            **kwargs: dict, optional
                fig: matplotlib.figure.Figure, optional
                    Existing figure to plot on. If None, a new figure is created.
                ax: matplotlib.axes.Axes, optional
                    Existing axes to plot on. If None, a new axes is created.
                grid: bool, optional
                    Whether to show grid lines. Default is True.
                color: dict, optional, default is None.
                    Colors to use for the plot elements. Default is None.
                    ```color = {"face", "#27408B", "edge", "#DC143C", "alpha", 0.7}```
                title: str, optional
                    Title of the plot. Default is 'Box Plot'.
                xlabel: str, optional
                    Label for the x-axis. Default is 'Index'.
                ylabel: str, optional
                    Label for the y-axis. Default is 'Value'.

        Returns:
            fig: matplotlib.figure.Figure
                The figure object containing the plot.
            ax: matplotlib.axes.Axes
                The axes object containing the plot.

        Examples:
            - Plot the box plot for a 1D time series:
                ```python
                >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
                >>> fig, ax = ts.violin()  # doctest: +SKIP

                ```
                ![violin_1d](./../_images/time_series/violin_1d.png)

            - Plot the box plot for a multiple time series:
                ```python
                >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
                >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
                >>> fig, ax = ts_2d.violin()  # doctest: +SKIP

                ```
                ![violin_2d](./../_images/time_series/violin_2d.png)

            - you can control the spacing between the violins using the `spacing` parameter:
                ```python
                >>> fig, ax = ts_2d.violin(spacing=2)  # doctest: +SKIP

                ```
                ![violin_2d_spacing](./../_images/time_series/violin_2d_spacing.png)

            - You can change the title, xlabel, and ylabel using the respective parameters:
                ```python
                >>> fig, ax = ts_2d.violin(xlabel='Random Data', ylabel='Custom Y', title='Custom Box Plot')  # doctest: +SKIP

                ```
                ![violin_labels_titles](./../_images/time_series/violin_labels_titles.png)

            - You can display the means, medians, and extrema using the respective parameters:
                ```python
                >>> fig, ax = ts_2d.violin(mean=True, median=True, extrema=True)  # doctest: +SKIP

                ```
                ![violin_means_medians_extrema](./../_images/time_series/violin_means_medians_extrema.png)

            - You can display the violins on the low side only using the `side` parameter:
                ```python
                >>> fig, ax = ts_2d.violin(side='low')  # doctest: +SKIP

                ```
                ![violin_low_side](./../_images/time_series/violin_low_side.png)
        """
        fig, ax = self._get_ax_fig(**kwargs)
        # kwargs.pop("fig", None)

        # positions where violins are plotted (1, 3, 5, ...)ing labels
        positions = np.arange(1, len(self.columns) * (spacing + 1) + 1, spacing + 1)

        violin_data = [self[col].dropna().values for col in self.columns]
        violin_parts = ax.violinplot(
            violin_data,
            showmeans=mean,
            showmedians=median,
            showextrema=extrema,
            side=side,
            positions=positions,
        )
        color = kwargs.get("color") if "color" in kwargs else VIOLIN_PROP

        for pc in violin_parts["bodies"]:  # type: ignore[attr-defined]
            pc.set_facecolor(color.get("face"))  # type: ignore[union-attr]
            pc.set_edgecolor(color.get("edge"))  # type: ignore[union-attr]
            pc.set_alpha(color.get("alpha"))  # type: ignore[union-attr]

        ax.xaxis.set_ticks(positions)
        # remove the ax from the kwargs to avoid passing it to the adjust_axes_labels method twice
        kwargs.pop("ax", None)
        ax = self._adjust_axes_labels(
            ax,
            self.columns,
            **kwargs,
        )

        plt.show()
        return fig, ax

    def raincloud(
        self,
        overlay: bool = True,
        violin_width: float = 0.4,
        scatter_offset: float = 0.15,
        boxplot_width: float = 0.1,
        order: list[str] = None,
        **kwargs,
    ) -> tuple[Figure, Axes]:
        """RainCloud plot.

        Args:
            overlay: bool, optional, default is True.
                Whether to overlay the plots or display them side-by-side.
            violin_width: float, optional, default is 0.4.
                Width of the violins.
            scatter_offset: float, optional, default is 0.15.
                Offset for the scatter plot.
            boxplot_width: float, optional, default is
                Width of the box plot.
            order: list, optional, default is None.
                Order of the plots. Default is ['violin', 'scatter', 'box'].
            **kwargs: dict, optional
                fig: matplotlib.figure.Figure, optional
                    Existing figure to plot on. If None, a new figure is created.
                ax: matplotlib.axes.Axes, optional
                    Existing axes to plot on. If None, a new axes is created.
                grid: bool, optional
                    Whether to show grid lines. Default is True.
                color: dict, optional, default is None.
                    Colors to use for the plot elements. Default is None.
                    ```color = {"boxes", "#27408B"}```
                title: str, optional
                    Title of the plot. Default is 'Box Plot'.
                xlabel: str, optional
                    Label for the x-axis. Default is 'Index'.
                ylabel: str, optional
                    Label for the y-axis. Default is 'Value'.

        Returns:
            fig: matplotlib.figure.Figure
                The figure object containing the plot.
            ax: matplotlib.axes.Axes
                The axes object containing the plot.

        Examples:
            - Plot the raincloud plot for a 1D time series, and use the `overlay` parameter to overlay the plots:
                ```python
                >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
                >>> fig, ax = ts.raincloud()  # doctest: +SKIP

                ```
                ![raincloud_1d](./../_images/time_series/raincloud_1d.png)

                ```python
                >>> fig, ax = ts.raincloud(overlay=False)  # doctest: +SKIP

                ```
                ![raincloud-overlay-false](./../_images/time_series/raincloud-overlay-false.png)

            - Plot the box plot for a multiple time series:
                ```python
                >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
                >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
                >>> fig, ax = ts_2d.raincloud(mean=True, grid=True)  # doctest: +SKIP

                ```
        """
        fig, ax = self._get_ax_fig(**kwargs)
        kwargs.pop("fig", None)
        kwargs.pop("ax", None)
        if order is None:
            order = ["violin", "scatter", "box"]

        n_groups = len(self.columns)
        positions = np.arange(1, n_groups + 1)

        # Dictionary to map plot types to the functions
        plot_funcs = {
            "violin": lambda pos, d: ax.violinplot(
                [d],
                positions=[pos],
                showmeans=False,
                showmedians=False,
                showextrema=False,
                widths=violin_width,
            ),
            "scatter": lambda pos, d: ax.scatter(
                np.random.normal(pos, 0.04, size=len(d)),
                d,
                alpha=0.6,
                color="black",
                s=10,
                edgecolor="white",
                linewidth=0.5,
            ),
            "box": lambda pos, d: ax.boxplot(
                [d],
                positions=[pos],
                widths=boxplot_width,
                vert=True,
                patch_artist=True,
                boxprops={"facecolor": "lightblue", "color": "blue"},
                medianprops={"color": "red"},
            ),
        }

        # Plot elements according to the specified order and selected plots
        # for i, d in enumerate(data):
        for i in range(len(self.columns)):
            if self.ndim == 1:
                d = self.values
            else:
                d = self.values[:, i]
            base_pos = positions[i]
            if overlay:
                for plot_type in order:
                    plot_funcs[plot_type](base_pos, d)
            else:
                for j, plot_type in enumerate(order):
                    offset = (j - 1) * scatter_offset
                    plot_funcs[plot_type](base_pos + offset, d)

        # Customize the appearance of violins if they are included
        if "violin" in order:
            for (
                pc
            ) in (
                ax.collections
            ):  # all polygons created by violinplot are in ax.collections
                if isinstance(pc, PolyCollection):
                    pc.set_facecolor("skyblue")
                    pc.set_edgecolor("blue")
                    pc.set_alpha(0.3)
                    pc.set_linewidth(1)
                    pc.set_linestyle("-")

        # Set x-tick labels
        ax.set_xticks(positions)
        ax = self._adjust_axes_labels(
            ax,
            self.columns,
            **kwargs,
        )

        # Add grid lines for better readability
        # ax.yaxis.grid(True)

        # Display the plot
        plt.show()
        return fig, ax

    def histogram(
        self, bins=10, **kwargs
    ) -> tuple[np.ndarray, np.ndarray, Figure, Axes]:
        """
        Plots a histogram of the time series data.

        Args:
            bins : int, optional, default is 10.
                Number of histogram bins.
            **kwargs: dict, optional
                fig: matplotlib.figure.Figure, optional
                    Existing figure to plot on. If None, a new figure is created.
                ax: matplotlib.axes.Axes, optional
                    Existing axes to plot on. If None, a new axes is created.
                grid: bool, optional
                    Whether to show grid lines. Default is True.
                color: str, optional, default is None.
                    Colors to use for the plot elements.
                title: str, optional
                    Title of the plot. Default is 'Box Plot'.
                xlabel: str, optional
                    Label for the x-axis. Default is 'Index'.
                ylabel: str, optional
                    Label for the y-axis. Default is 'Value'.
                title_fontsize: int, optional
                    Font size of the title.
                label_fontsize: int, optional
                    Font size of the title and labels.
                tick_fontsize: int, optional
                    Font size of the tick labels.
                xtick_labels: list[str], optional
                    Labels for the x-axis ticks.
                legend: list[str], optional
                    Legend to display in the plot.
                legend_fontsize: int, optional
                    Font size of the legend.

        Returns:
            fig : matplotlib.figure.Figure
                The figure object containing the plot.
            ax : matplotlib.axes.Axes
                The axes object containing the plot.
            n_values : np.ndarray
                The number of values in each histogram bin.
            bin_edges : np.ndarray
                The edges of the bins. Length nbins + 1 (nbins left edges and right
                edge of last bin).  Always a single array even when multiple data
                sets are passed in.

        Examples:
            - Plot the box plot for a 1D time series:
                ```python
                >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
                >>> n_values, bin_edges, fig, ax = ts.histogram()  # doctest: +SKIP
                >>> print(n_values) #doctest: +SKIP
                [ 5.  8. 11. 12. 14. 17. 15.  9.  4.  5.]
                >>> print(bin_edges) #doctest: +SKIP
                [-2.41934673 -1.9628219  -1.50629707 -1.04977224 -0.5932474  -0.13672257
                  0.31980226  0.77632709  1.23285192  1.68937676  2.14590159]
                ```
                ![histogram](./../_images/time_series/histogram.png)

            - Plot the box plot for a multiple time series:
                ```python
                >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
                >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
                >>> n_values, bin_edges, fig, ax = ts_2d.histogram(legend=['A', 'B', 'C', 'D'])  # doctest: +SKIP
                >>> print(n_values) #doctest: +SKIP
                [[ 0.  7.  9. 12. 20. 20. 19.  7.  5.  1.]
                 [ 1.  1.  9. 12. 20. 25. 13. 14.  5.  0.]
                 [ 5.  4. 11. 10. 18. 23. 13.  9.  4.  3.]
                 [ 1.  2. 11. 18. 16. 20. 13. 11.  6.  2.]]
                >>> print(bin_edges) #doctest: +SKIP
                [-2.76976813 -2.22944508 -1.68912202 -1.14879896 -0.6084759  -0.06815285
                  0.47217021  1.01249327  1.55281633  2.09313939  2.63346244]
                ```
                ![histogram-2d](./../_images/time_series/histogram-2d.png)
        """
        # plt.style.use('ggplot')

        fig, ax = self._get_ax_fig(**kwargs)

        color = kwargs.get("color") if "color" in kwargs else VIOLIN_PROP
        if len(self.columns) > 1 and not isinstance(color.get("face"), list):  # type: ignore[union-attr]
            color = None
            warnings.warn(
                "Multiple columns detected. Please provide a list of colors for each column, Otherwise the given"
                "color will be ignored."
            )
        n_values, bin_edges, _ = ax.hist(
            self.values,
            bins=bins,
            color=color.get("face") if color else None,  # type: ignore[union-attr,arg-type]
            edgecolor=color.get("edge") if color else None,  # type: ignore[union-attr]
            alpha=color.get("alpha") if color else None,  # type: ignore[union-attr]
        )

        kwargs.pop("ax", None)
        kwargs["legend"] = (
            kwargs.get("legend")
            if kwargs.get("legend") is not None
            else self.columns.to_list()
        )

        ax = self._adjust_axes_labels(
            ax,
            kwargs.get("xtick_labels"),
            **kwargs,
        )

        plt.show()
        return n_values, bin_edges, fig, ax  # type: ignore[return-value]

    def density(self, **kwargs) -> tuple[Figure, Axes]:
        """
        Plots a density (KDE) plot of the time series data.

        - KDE is a non-parametric method for estimating the probability density function of a random variable.
        - It provides a smoothed estimate of the underlying probability distribution based on observed data points
        - This function uses Gaussian kernels and includes automatic bandwidth determination

        Args:
            **kwargs: dict, optional
                color (str, optional):
                    Color of the density line. Default is 'blue'.
                fig (matplotlib.figure.Figure, optional):
                    Existing figure to plot on. If None, a new figure is created.
                ax (matplotlib.axes.Axes, optional):
                    Existing axes to plot on. If None, a new axes is created.
                grid (bool, optional):
                    Whether to show grid lines. Default is False.
                color (dict, optional):
                    Colors to use for the plot elements. Default is None.
                    ```color = {"boxes", "#27408B"}```
                title (str, optional):
                    Title of the plot.
                xlabel (str, optional):
                    Label for the x-axis.
                ylabel (str, optional):
                    Label for the y-axis.

        Returns:
            fig (matplotlib.figure.Figure):
                The figure object containing the plot.
            ax (matplotlib.axes.Axes):
                The axes object containing the plot.

        Examples:
            - Plot the KDE density plot for a 1D time series:
                ```python
                >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
                >>> fig, ax = ts.density(title='Density Plot', xlabel='Random Values', ylabel='KDE density')  # doctest: +SKIP

                ```
                ![density-1d](./../_images/time_series/density-1d.png)

            - Plot the KDE density plot for a 2D time series:
                ```python
                >>> ts = TimeSeries(np.random.randn(100, 4))  # doctest: +SKIP
                >>> fig, ax = ts.density(title='Density Plot', xlabel='Random Values', ylabel='KDE density')  # doctest: +SKIP

                ```
                ![density-2d](./../_images/time_series/density-2d.png)
        """
        fig, ax = self._get_ax_fig(**kwargs)
        color = kwargs.get("color", None)
        self[self.columns.to_list()].plot(kind="density", ax=ax, color=color)
        kwargs.pop("ax", None)
        ax = self._adjust_axes_labels(
            ax,
            kwargs.get("xtick_labels"),
            **kwargs,
        )

        plt.show()

        return fig, ax

    def rolling_statistics(self, window=10, **kwargs) -> tuple[Figure, Axes]:
        """
        Plots the rolling mean and standard deviation of the time series data.

        Args:
            window : int, optional, default is 10.
                The window size for the rolling statistics.
            **kwargs: dict, optional
                fig: matplotlib.figure.Figure, optional
                    Existing figure to plot on. If None, a new figure is created.
                ax: matplotlib.axes.Axes, optional
                    Existing axes to plot on. If None, a new axes is created.
                grid: bool, optional
                    Whether to show grid lines. Default is True.
                color: str, optional, default is None.
                    Colors to use for the plot elements.
                title: str, optional
                    Title of the plot. Default is 'Rolling Mean & Standard Deviation'.
                xlabel: str, optional
                    Label for the x-axis. Default is 'Index'.
                ylabel: str, optional
                    Label for the y-axis. Default is 'Value'.
                title_fontsize: int, optional
                    Font size of the title.
                label_fontsize: int, optional
                    Font size of the title and labels.
                tick_fontsize: int, optional
                    Font size of the tick labels.
                xtick_labels: list[str], optional
                    Labels for the x-axis ticks.
                legend: list[str], optional
                    Legend to display in the plot.
                legend_fontsize: int, optional
                    Font size of the legend.

        Returns:
            fig : matplotlib.figure.Figure
                The figure object containing the plot.
            ax : matplotlib.axes.Axes
                The axes object containing the plot.

        Examples:
            - Plot the rolling average and standard deviation for a 1D time series:
                ```python
                >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
                >>> fig, ax = ts.rolling_statistics(  # doctest: +SKIP
                ...    window=20, title='Rolling Statistics', xlabel='Random Values', ylabel='Random Y',
                ...    legend=['Rolling Mean', 'Rolling Std']
                ... )

                ```
                ![rolling-statistics](./../_images/time_series/rolling-statistics.png)

            - Plot the rolling average and standard deviation for a 2D time series:
                ```python
                >>> ts = TimeSeries(np.random.randn(100, 3))  # doctest: +SKIP
                >>> fig, ax = ts.rolling_statistics(  # doctest: +SKIP
                ...    window=10, title='Rolling Statistics', xlabel='Random Values', ylabel='Random Y',
                ... )

                ```
                ![rolling-statistics-2d](./../_images/time_series/rolling-statistics-2d.png)
        """
        fig, ax = self._get_ax_fig(**kwargs)

        rolling_mean = self[self.columns].rolling(window=window).mean()
        rolling_std = self[self.columns].rolling(window=window).std()

        ax.plot(self.index, rolling_mean, label="Rolling Mean", color="blue")
        ax.plot(self.index, rolling_std, label="Rolling Std", color="red")
        kwargs.pop("ax", None)
        ax = self._adjust_axes_labels(
            ax,
            kwargs.get("xtick_labels"),
            **kwargs,
        )
        plt.show()
        return fig, ax

box_plot(mean=False, notch=False, **kwargs)

a box and whisker plot.

The box extends from the first quartile (Q1) to the third quartile (Q3) of the data, with a line at the median. The whiskers extend from the box to the farthest data point lying within 1.5x the inter-quartile range (IQR) from the box. Flier points are those past the end of the whiskers. See https://en.wikipedia.org/wiki/Box_plot for reference.

The box plot can give the following insights

• Summary of Distribution: A box plot provides a graphical summary of the distribution of data based on five summary statistics: the minimum, first quartile (Q1), median, third quartile (Q3), and maximum.
• Outliers: It highlights outliers, which are data points that fall significantly above or below the rest of the data. Outliers are typically shown as individual points beyond the "whiskers" of the box plot.
• Central Tendency: The line inside the box indicates the median (50th percentile), giving insight into the central tendency of the data.
• Spread and Skewness: The length of the box (interquartile range, IQR) shows the spread of the middle 50% of the data, while the position of the median line within the box can suggest skewness.

    Q1-1.5IQR   Q1   median  Q3   Q3+1.5IQR
                 |-----:-----|
  

  o |--------| : |--------| o o |-----:-----| flier <-----------> fliers IQR

Use Case

• Useful for quickly comparing the distribution of the time series data and identifying any anomalies or outliers.

Parameters:

Name	Type	Description	Default
mean	bool	bool, optional, default is False. Whether to show the mean value in the box plot.	False
notch	bool	bool, optional, default is False. Whether to draw a notched boxplot (True), or a rectangular boxplot (False). The notches represent the confidence interval (CI) around the median. The documentation for bootstrap describes how the locations of the notches are computed by default, but their locations may also be overridden by setting the conf_intervals parameter.	False
**kwargs		dict, optional fig: matplotlib.figure.Figure, optional Existing figure to plot on. If None, a new figure is created. ax: matplotlib.axes.Axes, optional Existing axes to plot on. If None, a new axes is created. grid: bool, optional, Default is False. Whether to show grid lines. color: dict, optional, default is None. Colors to use for the plot elements. Default is None. color = {"boxes", "#27408B"} title: str, optional Title of the plot. xlabel: str, optional Label for the x-axis. ylabel: str, optional Label for the y-axis.	{}

Returns:

Name	Type	Description
fig	Figure	matplotlib.figure.Figure The figure object containing the plot.
ax	Axes	matplotlib.axes.Axes The axes object containing the plot.

Examples:

• Plot the box plot for a 1D time series:

  >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
  >>> fig, ax = ts.box_plot()  # doctest: +SKIP
  

  

• Plot the box plot for a multiple time series:

  >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
  >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
  >>> fig, ax = ts_2d.box_plot(mean=True, grid=True)  # doctest: +SKIP
  

  

  >>> fig, ax = ts_2d.box_plot(grid=True, mean=True, color={"boxes": "#DC143C"})  # doctest: +SKIP
  

  

  >>> fig, ax = ts_2d.box_plot(xlabel='Custom X', ylabel='Custom Y', title='Custom Box Plot')  # doctest: +SKIP
  

  

  >>> fig, ax = ts_2d.box_plot(notch=True)  # doctest: +SKIP
  

  

Source code in src/statista/time_series/visualization.py

def box_plot(
    self, mean: bool = False, notch: bool = False, **kwargs
) -> tuple[Figure, Axes]:
    """a box and whisker plot.

    The box extends from the first quartile (Q1) to the third quartile (Q3) of the data, with a line at the median.
    The whiskers extend from the box to the farthest data point lying within 1.5x the inter-quartile range (IQR)
    from the box.
    Flier points are those past the end of the whiskers. See https://en.wikipedia.org/wiki/Box_plot for reference.

    The box plot can give the following insights:
        - Summary of Distribution: A box plot provides a graphical summary of the distribution of data based on five
            summary statistics: the minimum, first quartile (Q1), median, third quartile (Q3), and maximum.
        - Outliers: It highlights outliers, which are data points that fall significantly above or below the rest of
            the data. Outliers are typically shown as individual points beyond the "whiskers" of the box plot.
        - Central Tendency: The line inside the box indicates the median (50th percentile), giving insight into the
            central tendency of the data.
        - Spread and Skewness: The length of the box (interquartile range, IQR) shows the spread of the middle 50% of
            the data, while the position of the median line within the box can suggest skewness.

                  Q1-1.5IQR   Q1   median  Q3   Q3+1.5IQR
                               |-----:-----|
               o      |--------|     :     |--------|    o  o
                               |-----:-----|
             flier             <----------->            fliers
                                    IQR

    Use Case:
        - Useful for quickly comparing the distribution of the time series data and identifying any anomalies or
            outliers.

    Args:
        mean: bool, optional, default is False.
            Whether to show the mean value in the box plot.
        notch: bool, optional, default is False.
                Whether to draw a notched boxplot (`True`), or a rectangular
                boxplot (`False`).  The notches represent the confidence interval
                (CI) around the median.  The documentation for *bootstrap*
                describes how the locations of the notches are computed by
                default, but their locations may also be overridden by setting the
                *conf_intervals* parameter.
        **kwargs: dict, optional
            fig: matplotlib.figure.Figure, optional
                Existing figure to plot on. If None, a new figure is created.
            ax: matplotlib.axes.Axes, optional
                Existing axes to plot on. If None, a new axes is created.
            grid: bool, optional, Default is False.
                Whether to show grid lines.
            color: dict, optional, default is None.
                Colors to use for the plot elements. Default is None.
                ```color = {"boxes", "#27408B"}```
            title: str, optional
                Title of the plot.
            xlabel: str, optional
                Label for the x-axis.
            ylabel: str, optional
                Label for the y-axis.

    Returns:
        fig: matplotlib.figure.Figure
            The figure object containing the plot.
        ax: matplotlib.axes.Axes
            The axes object containing the plot.

    Examples:
        - Plot the box plot for a 1D time series:
            ```python
            >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
            >>> fig, ax = ts.box_plot()  # doctest: +SKIP

            ```
            ![box_plot_1d](./../_images/time_series/box_plot_1d.png)

        - Plot the box plot for a multiple time series:
            ```python
            >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
            >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
            >>> fig, ax = ts_2d.box_plot(mean=True, grid=True)  # doctest: +SKIP

            ```
            ![box_plot_2d](./../_images/time_series/box_plot_2d.png)
            ```python
            >>> fig, ax = ts_2d.box_plot(grid=True, mean=True, color={"boxes": "#DC143C"})  # doctest: +SKIP

            ```
            ![box_plot_color](./../_images/time_series/box_plot_color.png)
            ```python
            >>> fig, ax = ts_2d.box_plot(xlabel='Custom X', ylabel='Custom Y', title='Custom Box Plot')  # doctest: +SKIP

            ```
            ![box_plot_axes-label](./../_images/time_series/box_plot_axes-label.png)
            ```python
            >>> fig, ax = ts_2d.box_plot(notch=True)  # doctest: +SKIP

            ```
            ![box_plot_notch](./../_images/time_series/box_plot_notch.png)
    """
    fig, ax = self._get_ax_fig(**kwargs)
    kwargs.pop("fig", None)
    kwargs.pop("ax", None)
    color = kwargs.get("color", None)
    data = [self[col].dropna() for col in self.columns]
    ax.boxplot(
        data,
        notch=notch,
        patch_artist=True,
        showmeans=mean,
        meanprops=BOX_MEAN_PROP,
        boxprops={
            "facecolor": (
                "#27408B" if color is None else color.get("boxes", "#27408B")
            )
        },
    )
    ax = self._adjust_axes_labels(
        ax,
        self.columns,
        **kwargs,
    )

    plt.show()
    return fig, ax

calculate_whiskers(data, q1, q3) staticmethod

Calculate the upper and lower whiskers for a box plot.

Parameters:

Name	Type	Description	Default
data	ndarray | list	np.ndarray Input array of data.	required
q1	float	float first quartile	required
q3	float	float third quartile	required

Returns:

Name	Type	Description
lower_wisker		float Lower whisker value.
upper_wisker		float Upper whisker value.

Source code in src/statista/time_series/visualization.py

@staticmethod
def calculate_whiskers(data: np.ndarray | list, q1: float, q3: float):
    """Calculate the upper and lower whiskers for a box plot.

    Args:
        data: np.ndarray
            Input array of data.
        q1: float
            first quartile
        q3: float
            third quartile

    Returns:
        lower_wisker: float
            Lower whisker value.
        upper_wisker: float
            Upper whisker value.
    """
    inter_quartile = q3 - q1
    upper_whisker = q3 + inter_quartile * 1.5
    upper_whisker = np.clip(upper_whisker, q3, data[-1])

    lower_whisker = q1 - inter_quartile * 1.5
    lower_whisker = np.clip(lower_whisker, data[0], q1)
    return lower_whisker, upper_whisker

violin(mean=True, median=False, extrema=True, side='both', spacing=0, **kwargs)

Plots a violin plot of the time series data.

Parameters:

Name	Type	Description	Default
mean	bool	bool, optional, default is True. Whether to show the means in the violin plot.	True
median	bool	bool, optional, default is False. Whether to show the median in the violin plot.	False
extrema	bool	bool, optional, default is False. Whether to show the minima and maxima in the violin plot.	True
side	Literal['both', 'low', 'high']	{'both', 'low', 'high'}, default: 'both' 'both' plots standard violins. 'low'/'high' only plots the side below/above the position value.	'both'
spacing	int	int, optional, default is 0. The spacing (number of ticks) between the violins.	0
**kwargs		dict, optional fig: matplotlib.figure.Figure, optional Existing figure to plot on. If None, a new figure is created. ax: matplotlib.axes.Axes, optional Existing axes to plot on. If None, a new axes is created. grid: bool, optional Whether to show grid lines. Default is True. color: dict, optional, default is None. Colors to use for the plot elements. Default is None. color = {"face", "#27408B", "edge", "#DC143C", "alpha", 0.7} title: str, optional Title of the plot. Default is 'Box Plot'. xlabel: str, optional Label for the x-axis. Default is 'Index'. ylabel: str, optional Label for the y-axis. Default is 'Value'.	{}

Returns:

Name	Type	Description
fig	Figure	matplotlib.figure.Figure The figure object containing the plot.
ax	Axes	matplotlib.axes.Axes The axes object containing the plot.

Examples:

• Plot the box plot for a 1D time series:

  >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
  >>> fig, ax = ts.violin()  # doctest: +SKIP
  

  

• Plot the box plot for a multiple time series:

  >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
  >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
  >>> fig, ax = ts_2d.violin()  # doctest: +SKIP
  

  

• you can control the spacing between the violins using the spacing parameter:

  >>> fig, ax = ts_2d.violin(spacing=2)  # doctest: +SKIP
  

  

• You can change the title, xlabel, and ylabel using the respective parameters:

  >>> fig, ax = ts_2d.violin(xlabel='Random Data', ylabel='Custom Y', title='Custom Box Plot')  # doctest: +SKIP
  

  

• You can display the means, medians, and extrema using the respective parameters:

  >>> fig, ax = ts_2d.violin(mean=True, median=True, extrema=True)  # doctest: +SKIP
  

  

• You can display the violins on the low side only using the side parameter:

  >>> fig, ax = ts_2d.violin(side='low')  # doctest: +SKIP
  

  

Source code in src/statista/time_series/visualization.py

def violin(
    self,
    mean: bool = True,
    median: bool = False,
    extrema: bool = True,
    side: Literal["both", "low", "high"] = "both",
    spacing: int = 0,
    **kwargs,
) -> tuple[Figure, Axes]:
    """
    Plots a violin plot of the time series data.

    Args:
        mean: bool, optional, default is True.
            Whether to show the means in the violin plot.
        median: bool, optional, default is False.
            Whether to show the median in the violin plot.
        extrema: bool, optional, default is False.
            Whether to show the minima and maxima in the violin plot.
        side: {'both', 'low', 'high'}, default: 'both'
            'both' plots standard violins. 'low'/'high' only
            plots the side below/above the position value.
        spacing: int, optional, default is 0.
            The spacing (number of ticks) between the violins.
        **kwargs: dict, optional
            fig: matplotlib.figure.Figure, optional
                Existing figure to plot on. If None, a new figure is created.
            ax: matplotlib.axes.Axes, optional
                Existing axes to plot on. If None, a new axes is created.
            grid: bool, optional
                Whether to show grid lines. Default is True.
            color: dict, optional, default is None.
                Colors to use for the plot elements. Default is None.
                ```color = {"face", "#27408B", "edge", "#DC143C", "alpha", 0.7}```
            title: str, optional
                Title of the plot. Default is 'Box Plot'.
            xlabel: str, optional
                Label for the x-axis. Default is 'Index'.
            ylabel: str, optional
                Label for the y-axis. Default is 'Value'.

    Returns:
        fig: matplotlib.figure.Figure
            The figure object containing the plot.
        ax: matplotlib.axes.Axes
            The axes object containing the plot.

    Examples:
        - Plot the box plot for a 1D time series:
            ```python
            >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
            >>> fig, ax = ts.violin()  # doctest: +SKIP

            ```
            ![violin_1d](./../_images/time_series/violin_1d.png)

        - Plot the box plot for a multiple time series:
            ```python
            >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
            >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
            >>> fig, ax = ts_2d.violin()  # doctest: +SKIP

            ```
            ![violin_2d](./../_images/time_series/violin_2d.png)

        - you can control the spacing between the violins using the `spacing` parameter:
            ```python
            >>> fig, ax = ts_2d.violin(spacing=2)  # doctest: +SKIP

            ```
            ![violin_2d_spacing](./../_images/time_series/violin_2d_spacing.png)

        - You can change the title, xlabel, and ylabel using the respective parameters:
            ```python
            >>> fig, ax = ts_2d.violin(xlabel='Random Data', ylabel='Custom Y', title='Custom Box Plot')  # doctest: +SKIP

            ```
            ![violin_labels_titles](./../_images/time_series/violin_labels_titles.png)

        - You can display the means, medians, and extrema using the respective parameters:
            ```python
            >>> fig, ax = ts_2d.violin(mean=True, median=True, extrema=True)  # doctest: +SKIP

            ```
            ![violin_means_medians_extrema](./../_images/time_series/violin_means_medians_extrema.png)

        - You can display the violins on the low side only using the `side` parameter:
            ```python
            >>> fig, ax = ts_2d.violin(side='low')  # doctest: +SKIP

            ```
            ![violin_low_side](./../_images/time_series/violin_low_side.png)
    """
    fig, ax = self._get_ax_fig(**kwargs)
    # kwargs.pop("fig", None)

    # positions where violins are plotted (1, 3, 5, ...)ing labels
    positions = np.arange(1, len(self.columns) * (spacing + 1) + 1, spacing + 1)

    violin_data = [self[col].dropna().values for col in self.columns]
    violin_parts = ax.violinplot(
        violin_data,
        showmeans=mean,
        showmedians=median,
        showextrema=extrema,
        side=side,
        positions=positions,
    )
    color = kwargs.get("color") if "color" in kwargs else VIOLIN_PROP

    for pc in violin_parts["bodies"]:  # type: ignore[attr-defined]
        pc.set_facecolor(color.get("face"))  # type: ignore[union-attr]
        pc.set_edgecolor(color.get("edge"))  # type: ignore[union-attr]
        pc.set_alpha(color.get("alpha"))  # type: ignore[union-attr]

    ax.xaxis.set_ticks(positions)
    # remove the ax from the kwargs to avoid passing it to the adjust_axes_labels method twice
    kwargs.pop("ax", None)
    ax = self._adjust_axes_labels(
        ax,
        self.columns,
        **kwargs,
    )

    plt.show()
    return fig, ax

raincloud(overlay=True, violin_width=0.4, scatter_offset=0.15, boxplot_width=0.1, order=None, **kwargs)

RainCloud plot.

Parameters:

Name	Type	Description	Default
overlay	bool	bool, optional, default is True. Whether to overlay the plots or display them side-by-side.	True
violin_width	float	float, optional, default is 0.4. Width of the violins.	0.4
scatter_offset	float	float, optional, default is 0.15. Offset for the scatter plot.	0.15
boxplot_width	float	float, optional, default is Width of the box plot.	0.1
order	list[str]	list, optional, default is None. Order of the plots. Default is ['violin', 'scatter', 'box'].	None
**kwargs		dict, optional fig: matplotlib.figure.Figure, optional Existing figure to plot on. If None, a new figure is created. ax: matplotlib.axes.Axes, optional Existing axes to plot on. If None, a new axes is created. grid: bool, optional Whether to show grid lines. Default is True. color: dict, optional, default is None. Colors to use for the plot elements. Default is None. color = {"boxes", "#27408B"} title: str, optional Title of the plot. Default is 'Box Plot'. xlabel: str, optional Label for the x-axis. Default is 'Index'. ylabel: str, optional Label for the y-axis. Default is 'Value'.	{}

Returns:

Name	Type	Description
fig	Figure	matplotlib.figure.Figure The figure object containing the plot.
ax	Axes	matplotlib.axes.Axes The axes object containing the plot.

Examples:

• Plot the raincloud plot for a 1D time series, and use the overlay parameter to overlay the plots:

  >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
  >>> fig, ax = ts.raincloud()  # doctest: +SKIP
  

  

  >>> fig, ax = ts.raincloud(overlay=False)  # doctest: +SKIP
  

  

• Plot the box plot for a multiple time series:

  >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
  >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
  >>> fig, ax = ts_2d.raincloud(mean=True, grid=True)  # doctest: +SKIP
  

Source code in src/statista/time_series/visualization.py

def raincloud(
    self,
    overlay: bool = True,
    violin_width: float = 0.4,
    scatter_offset: float = 0.15,
    boxplot_width: float = 0.1,
    order: list[str] = None,
    **kwargs,
) -> tuple[Figure, Axes]:
    """RainCloud plot.

    Args:
        overlay: bool, optional, default is True.
            Whether to overlay the plots or display them side-by-side.
        violin_width: float, optional, default is 0.4.
            Width of the violins.
        scatter_offset: float, optional, default is 0.15.
            Offset for the scatter plot.
        boxplot_width: float, optional, default is
            Width of the box plot.
        order: list, optional, default is None.
            Order of the plots. Default is ['violin', 'scatter', 'box'].
        **kwargs: dict, optional
            fig: matplotlib.figure.Figure, optional
                Existing figure to plot on. If None, a new figure is created.
            ax: matplotlib.axes.Axes, optional
                Existing axes to plot on. If None, a new axes is created.
            grid: bool, optional
                Whether to show grid lines. Default is True.
            color: dict, optional, default is None.
                Colors to use for the plot elements. Default is None.
                ```color = {"boxes", "#27408B"}```
            title: str, optional
                Title of the plot. Default is 'Box Plot'.
            xlabel: str, optional
                Label for the x-axis. Default is 'Index'.
            ylabel: str, optional
                Label for the y-axis. Default is 'Value'.

    Returns:
        fig: matplotlib.figure.Figure
            The figure object containing the plot.
        ax: matplotlib.axes.Axes
            The axes object containing the plot.

    Examples:
        - Plot the raincloud plot for a 1D time series, and use the `overlay` parameter to overlay the plots:
            ```python
            >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
            >>> fig, ax = ts.raincloud()  # doctest: +SKIP

            ```
            ![raincloud_1d](./../_images/time_series/raincloud_1d.png)

            ```python
            >>> fig, ax = ts.raincloud(overlay=False)  # doctest: +SKIP

            ```
            ![raincloud-overlay-false](./../_images/time_series/raincloud-overlay-false.png)

        - Plot the box plot for a multiple time series:
            ```python
            >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
            >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
            >>> fig, ax = ts_2d.raincloud(mean=True, grid=True)  # doctest: +SKIP

            ```
    """
    fig, ax = self._get_ax_fig(**kwargs)
    kwargs.pop("fig", None)
    kwargs.pop("ax", None)
    if order is None:
        order = ["violin", "scatter", "box"]

    n_groups = len(self.columns)
    positions = np.arange(1, n_groups + 1)

    # Dictionary to map plot types to the functions
    plot_funcs = {
        "violin": lambda pos, d: ax.violinplot(
            [d],
            positions=[pos],
            showmeans=False,
            showmedians=False,
            showextrema=False,
            widths=violin_width,
        ),
        "scatter": lambda pos, d: ax.scatter(
            np.random.normal(pos, 0.04, size=len(d)),
            d,
            alpha=0.6,
            color="black",
            s=10,
            edgecolor="white",
            linewidth=0.5,
        ),
        "box": lambda pos, d: ax.boxplot(
            [d],
            positions=[pos],
            widths=boxplot_width,
            vert=True,
            patch_artist=True,
            boxprops={"facecolor": "lightblue", "color": "blue"},
            medianprops={"color": "red"},
        ),
    }

    # Plot elements according to the specified order and selected plots
    # for i, d in enumerate(data):
    for i in range(len(self.columns)):
        if self.ndim == 1:
            d = self.values
        else:
            d = self.values[:, i]
        base_pos = positions[i]
        if overlay:
            for plot_type in order:
                plot_funcs[plot_type](base_pos, d)
        else:
            for j, plot_type in enumerate(order):
                offset = (j - 1) * scatter_offset
                plot_funcs[plot_type](base_pos + offset, d)

    # Customize the appearance of violins if they are included
    if "violin" in order:
        for (
            pc
        ) in (
            ax.collections
        ):  # all polygons created by violinplot are in ax.collections
            if isinstance(pc, PolyCollection):
                pc.set_facecolor("skyblue")
                pc.set_edgecolor("blue")
                pc.set_alpha(0.3)
                pc.set_linewidth(1)
                pc.set_linestyle("-")

    # Set x-tick labels
    ax.set_xticks(positions)
    ax = self._adjust_axes_labels(
        ax,
        self.columns,
        **kwargs,
    )

    # Add grid lines for better readability
    # ax.yaxis.grid(True)

    # Display the plot
    plt.show()
    return fig, ax

histogram(bins=10, **kwargs)

Plots a histogram of the time series data.

Parameters:

Name	Type	Description	Default
bins		int, optional, default is 10. Number of histogram bins.	required
**kwargs		dict, optional fig: matplotlib.figure.Figure, optional Existing figure to plot on. If None, a new figure is created. ax: matplotlib.axes.Axes, optional Existing axes to plot on. If None, a new axes is created. grid: bool, optional Whether to show grid lines. Default is True. color: str, optional, default is None. Colors to use for the plot elements. title: str, optional Title of the plot. Default is 'Box Plot'. xlabel: str, optional Label for the x-axis. Default is 'Index'. ylabel: str, optional Label for the y-axis. Default is 'Value'. title_fontsize: int, optional Font size of the title. label_fontsize: int, optional Font size of the title and labels. tick_fontsize: int, optional Font size of the tick labels. xtick_labels: list[str], optional Labels for the x-axis ticks. legend: list[str], optional Legend to display in the plot. legend_fontsize: int, optional Font size of the legend.	{}

Returns:

Name	Type	Description
fig	ndarray	matplotlib.figure.Figure The figure object containing the plot.
ax	ndarray	matplotlib.axes.Axes The axes object containing the plot.
n_values	Figure	np.ndarray The number of values in each histogram bin.
bin_edges	Axes	np.ndarray The edges of the bins. Length nbins + 1 (nbins left edges and right edge of last bin). Always a single array even when multiple data sets are passed in.

Examples:

• Plot the box plot for a 1D time series:

  >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
  >>> n_values, bin_edges, fig, ax = ts.histogram()  # doctest: +SKIP
  >>> print(n_values) #doctest: +SKIP
  [ 5.  8. 11. 12. 14. 17. 15.  9.  4.  5.]
  >>> print(bin_edges) #doctest: +SKIP
  [-2.41934673 -1.9628219  -1.50629707 -1.04977224 -0.5932474  -0.13672257
    0.31980226  0.77632709  1.23285192  1.68937676  2.14590159]
  

  

• Plot the box plot for a multiple time series:

  >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
  >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
  >>> n_values, bin_edges, fig, ax = ts_2d.histogram(legend=['A', 'B', 'C', 'D'])  # doctest: +SKIP
  >>> print(n_values) #doctest: +SKIP
  [[ 0.  7.  9. 12. 20. 20. 19.  7.  5.  1.]
   [ 1.  1.  9. 12. 20. 25. 13. 14.  5.  0.]
   [ 5.  4. 11. 10. 18. 23. 13.  9.  4.  3.]
   [ 1.  2. 11. 18. 16. 20. 13. 11.  6.  2.]]
  >>> print(bin_edges) #doctest: +SKIP
  [-2.76976813 -2.22944508 -1.68912202 -1.14879896 -0.6084759  -0.06815285
    0.47217021  1.01249327  1.55281633  2.09313939  2.63346244]
  

  

Source code in src/statista/time_series/visualization.py

def histogram(
    self, bins=10, **kwargs
) -> tuple[np.ndarray, np.ndarray, Figure, Axes]:
    """
    Plots a histogram of the time series data.

    Args:
        bins : int, optional, default is 10.
            Number of histogram bins.
        **kwargs: dict, optional
            fig: matplotlib.figure.Figure, optional
                Existing figure to plot on. If None, a new figure is created.
            ax: matplotlib.axes.Axes, optional
                Existing axes to plot on. If None, a new axes is created.
            grid: bool, optional
                Whether to show grid lines. Default is True.
            color: str, optional, default is None.
                Colors to use for the plot elements.
            title: str, optional
                Title of the plot. Default is 'Box Plot'.
            xlabel: str, optional
                Label for the x-axis. Default is 'Index'.
            ylabel: str, optional
                Label for the y-axis. Default is 'Value'.
            title_fontsize: int, optional
                Font size of the title.
            label_fontsize: int, optional
                Font size of the title and labels.
            tick_fontsize: int, optional
                Font size of the tick labels.
            xtick_labels: list[str], optional
                Labels for the x-axis ticks.
            legend: list[str], optional
                Legend to display in the plot.
            legend_fontsize: int, optional
                Font size of the legend.

    Returns:
        fig : matplotlib.figure.Figure
            The figure object containing the plot.
        ax : matplotlib.axes.Axes
            The axes object containing the plot.
        n_values : np.ndarray
            The number of values in each histogram bin.
        bin_edges : np.ndarray
            The edges of the bins. Length nbins + 1 (nbins left edges and right
            edge of last bin).  Always a single array even when multiple data
            sets are passed in.

    Examples:
        - Plot the box plot for a 1D time series:
            ```python
            >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
            >>> n_values, bin_edges, fig, ax = ts.histogram()  # doctest: +SKIP
            >>> print(n_values) #doctest: +SKIP
            [ 5.  8. 11. 12. 14. 17. 15.  9.  4.  5.]
            >>> print(bin_edges) #doctest: +SKIP
            [-2.41934673 -1.9628219  -1.50629707 -1.04977224 -0.5932474  -0.13672257
              0.31980226  0.77632709  1.23285192  1.68937676  2.14590159]
            ```
            ![histogram](./../_images/time_series/histogram.png)

        - Plot the box plot for a multiple time series:
            ```python
            >>> data_2d = np.random.randn(100, 4)  # doctest: +SKIP
            >>> ts_2d = TimeSeries(data_2d, columns=['A', 'B', 'C', 'D'])  # doctest: +SKIP
            >>> n_values, bin_edges, fig, ax = ts_2d.histogram(legend=['A', 'B', 'C', 'D'])  # doctest: +SKIP
            >>> print(n_values) #doctest: +SKIP
            [[ 0.  7.  9. 12. 20. 20. 19.  7.  5.  1.]
             [ 1.  1.  9. 12. 20. 25. 13. 14.  5.  0.]
             [ 5.  4. 11. 10. 18. 23. 13.  9.  4.  3.]
             [ 1.  2. 11. 18. 16. 20. 13. 11.  6.  2.]]
            >>> print(bin_edges) #doctest: +SKIP
            [-2.76976813 -2.22944508 -1.68912202 -1.14879896 -0.6084759  -0.06815285
              0.47217021  1.01249327  1.55281633  2.09313939  2.63346244]
            ```
            ![histogram-2d](./../_images/time_series/histogram-2d.png)
    """
    # plt.style.use('ggplot')

    fig, ax = self._get_ax_fig(**kwargs)

    color = kwargs.get("color") if "color" in kwargs else VIOLIN_PROP
    if len(self.columns) > 1 and not isinstance(color.get("face"), list):  # type: ignore[union-attr]
        color = None
        warnings.warn(
            "Multiple columns detected. Please provide a list of colors for each column, Otherwise the given"
            "color will be ignored."
        )
    n_values, bin_edges, _ = ax.hist(
        self.values,
        bins=bins,
        color=color.get("face") if color else None,  # type: ignore[union-attr,arg-type]
        edgecolor=color.get("edge") if color else None,  # type: ignore[union-attr]
        alpha=color.get("alpha") if color else None,  # type: ignore[union-attr]
    )

    kwargs.pop("ax", None)
    kwargs["legend"] = (
        kwargs.get("legend")
        if kwargs.get("legend") is not None
        else self.columns.to_list()
    )

    ax = self._adjust_axes_labels(
        ax,
        kwargs.get("xtick_labels"),
        **kwargs,
    )

    plt.show()
    return n_values, bin_edges, fig, ax  # type: ignore[return-value]

density(**kwargs)

Plots a density (KDE) plot of the time series data.

• KDE is a non-parametric method for estimating the probability density function of a random variable.
• It provides a smoothed estimate of the underlying probability distribution based on observed data points
• This function uses Gaussian kernels and includes automatic bandwidth determination

Parameters:

Name	Type	Description	Default
**kwargs		dict, optional color (str, optional): Color of the density line. Default is 'blue'. fig (matplotlib.figure.Figure, optional): Existing figure to plot on. If None, a new figure is created. ax (matplotlib.axes.Axes, optional): Existing axes to plot on. If None, a new axes is created. grid (bool, optional): Whether to show grid lines. Default is False. color (dict, optional): Colors to use for the plot elements. Default is None. color = {"boxes", "#27408B"} title (str, optional): Title of the plot. xlabel (str, optional): Label for the x-axis. ylabel (str, optional): Label for the y-axis.	{}

Returns:

Name	Type	Description
fig	Figure	The figure object containing the plot.
ax	Axes	The axes object containing the plot.

Examples:

• Plot the KDE density plot for a 1D time series:

  >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
  >>> fig, ax = ts.density(title='Density Plot', xlabel='Random Values', ylabel='KDE density')  # doctest: +SKIP
  

  

• Plot the KDE density plot for a 2D time series:

  >>> ts = TimeSeries(np.random.randn(100, 4))  # doctest: +SKIP
  >>> fig, ax = ts.density(title='Density Plot', xlabel='Random Values', ylabel='KDE density')  # doctest: +SKIP
  

  

Source code in src/statista/time_series/visualization.py

def density(self, **kwargs) -> tuple[Figure, Axes]:
    """
    Plots a density (KDE) plot of the time series data.

    - KDE is a non-parametric method for estimating the probability density function of a random variable.
    - It provides a smoothed estimate of the underlying probability distribution based on observed data points
    - This function uses Gaussian kernels and includes automatic bandwidth determination

    Args:
        **kwargs: dict, optional
            color (str, optional):
                Color of the density line. Default is 'blue'.
            fig (matplotlib.figure.Figure, optional):
                Existing figure to plot on. If None, a new figure is created.
            ax (matplotlib.axes.Axes, optional):
                Existing axes to plot on. If None, a new axes is created.
            grid (bool, optional):
                Whether to show grid lines. Default is False.
            color (dict, optional):
                Colors to use for the plot elements. Default is None.
                ```color = {"boxes", "#27408B"}```
            title (str, optional):
                Title of the plot.
            xlabel (str, optional):
                Label for the x-axis.
            ylabel (str, optional):
                Label for the y-axis.

    Returns:
        fig (matplotlib.figure.Figure):
            The figure object containing the plot.
        ax (matplotlib.axes.Axes):
            The axes object containing the plot.

    Examples:
        - Plot the KDE density plot for a 1D time series:
            ```python
            >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
            >>> fig, ax = ts.density(title='Density Plot', xlabel='Random Values', ylabel='KDE density')  # doctest: +SKIP

            ```
            ![density-1d](./../_images/time_series/density-1d.png)

        - Plot the KDE density plot for a 2D time series:
            ```python
            >>> ts = TimeSeries(np.random.randn(100, 4))  # doctest: +SKIP
            >>> fig, ax = ts.density(title='Density Plot', xlabel='Random Values', ylabel='KDE density')  # doctest: +SKIP

            ```
            ![density-2d](./../_images/time_series/density-2d.png)
    """
    fig, ax = self._get_ax_fig(**kwargs)
    color = kwargs.get("color", None)
    self[self.columns.to_list()].plot(kind="density", ax=ax, color=color)
    kwargs.pop("ax", None)
    ax = self._adjust_axes_labels(
        ax,
        kwargs.get("xtick_labels"),
        **kwargs,
    )

    plt.show()

    return fig, ax

rolling_statistics(window=10, **kwargs)

Plots the rolling mean and standard deviation of the time series data.

Parameters:

Name	Type	Description	Default
window		int, optional, default is 10. The window size for the rolling statistics.	required
**kwargs		dict, optional fig: matplotlib.figure.Figure, optional Existing figure to plot on. If None, a new figure is created. ax: matplotlib.axes.Axes, optional Existing axes to plot on. If None, a new axes is created. grid: bool, optional Whether to show grid lines. Default is True. color: str, optional, default is None. Colors to use for the plot elements. title: str, optional Title of the plot. Default is 'Rolling Mean & Standard Deviation'. xlabel: str, optional Label for the x-axis. Default is 'Index'. ylabel: str, optional Label for the y-axis. Default is 'Value'. title_fontsize: int, optional Font size of the title. label_fontsize: int, optional Font size of the title and labels. tick_fontsize: int, optional Font size of the tick labels. xtick_labels: list[str], optional Labels for the x-axis ticks. legend: list[str], optional Legend to display in the plot. legend_fontsize: int, optional Font size of the legend.	{}

Returns:

Name	Type	Description
fig	Figure	matplotlib.figure.Figure The figure object containing the plot.
ax	Axes	matplotlib.axes.Axes The axes object containing the plot.

Examples:

• Plot the rolling average and standard deviation for a 1D time series:

  >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
  >>> fig, ax = ts.rolling_statistics(  # doctest: +SKIP
  ...    window=20, title='Rolling Statistics', xlabel='Random Values', ylabel='Random Y',
  ...    legend=['Rolling Mean', 'Rolling Std']
  ... )
  

  

• Plot the rolling average and standard deviation for a 2D time series:

  >>> ts = TimeSeries(np.random.randn(100, 3))  # doctest: +SKIP
  >>> fig, ax = ts.rolling_statistics(  # doctest: +SKIP
  ...    window=10, title='Rolling Statistics', xlabel='Random Values', ylabel='Random Y',
  ... )
  

  

Source code in src/statista/time_series/visualization.py

def rolling_statistics(self, window=10, **kwargs) -> tuple[Figure, Axes]:
    """
    Plots the rolling mean and standard deviation of the time series data.

    Args:
        window : int, optional, default is 10.
            The window size for the rolling statistics.
        **kwargs: dict, optional
            fig: matplotlib.figure.Figure, optional
                Existing figure to plot on. If None, a new figure is created.
            ax: matplotlib.axes.Axes, optional
                Existing axes to plot on. If None, a new axes is created.
            grid: bool, optional
                Whether to show grid lines. Default is True.
            color: str, optional, default is None.
                Colors to use for the plot elements.
            title: str, optional
                Title of the plot. Default is 'Rolling Mean & Standard Deviation'.
            xlabel: str, optional
                Label for the x-axis. Default is 'Index'.
            ylabel: str, optional
                Label for the y-axis. Default is 'Value'.
            title_fontsize: int, optional
                Font size of the title.
            label_fontsize: int, optional
                Font size of the title and labels.
            tick_fontsize: int, optional
                Font size of the tick labels.
            xtick_labels: list[str], optional
                Labels for the x-axis ticks.
            legend: list[str], optional
                Legend to display in the plot.
            legend_fontsize: int, optional
                Font size of the legend.

    Returns:
        fig : matplotlib.figure.Figure
            The figure object containing the plot.
        ax : matplotlib.axes.Axes
            The axes object containing the plot.

    Examples:
        - Plot the rolling average and standard deviation for a 1D time series:
            ```python
            >>> ts = TimeSeries(np.random.randn(100))  # doctest: +SKIP
            >>> fig, ax = ts.rolling_statistics(  # doctest: +SKIP
            ...    window=20, title='Rolling Statistics', xlabel='Random Values', ylabel='Random Y',
            ...    legend=['Rolling Mean', 'Rolling Std']
            ... )

            ```
            ![rolling-statistics](./../_images/time_series/rolling-statistics.png)

        - Plot the rolling average and standard deviation for a 2D time series:
            ```python
            >>> ts = TimeSeries(np.random.randn(100, 3))  # doctest: +SKIP
            >>> fig, ax = ts.rolling_statistics(  # doctest: +SKIP
            ...    window=10, title='Rolling Statistics', xlabel='Random Values', ylabel='Random Y',
            ... )

            ```
            ![rolling-statistics-2d](./../_images/time_series/rolling-statistics-2d.png)
    """
    fig, ax = self._get_ax_fig(**kwargs)

    rolling_mean = self[self.columns].rolling(window=window).mean()
    rolling_std = self[self.columns].rolling(window=window).std()

    ax.plot(self.index, rolling_mean, label="Rolling Mean", color="blue")
    ax.plot(self.index, rolling_std, label="Rolling Std", color="red")
    kwargs.pop("ax", None)
    ax = self._adjust_axes_labels(
        ax,
        kwargs.get("xtick_labels"),
        **kwargs,
    )
    plt.show()
    return fig, ax