--- title: Error Bars --- > [!WARNING] > Legacy documentation: This documentation applies to **Legacy Deephaven Enterprise only** and does not apply to Core+. > [!NOTE] > For Core+ workers, see Community Core documentation for [legacy API plotting methods](/core/docs/how-to-guides/plotting/api-plotting). Error bar plots, which visually indicate the uncertainties in a dataset, are useful in determining whether the variability in your data is statistically significant. For instance, if your plot shows average values, a short error bar shows that the average value is more certain because the values are concentrated, while a long error bar shows a greater amount of uncertainty because the values are spread out, and thus less reliable. The error bars typically represent standard deviation or a particular confidence interval. Error bar plotting is available for: - [XY Series plots](./legacy-plotting.md#xy-series) - [Category plots](./legacy-plotting.md#category) ## XY Series Plots with Error Bars XY Series plots with error bars can be created from data sourced from a table or an array. There are three methods available: - `errorBarX()` is used to create an XY series plot with error bars drawn in the same direction as the X axis (horizontally). - `errorBarY()` is used to create an XY series plot with error bars drawn in the same direction as the Y axis (vertically). - `errorBarXY()` is used to create an XY series plot with error bars drawn both horizontal and vertically. ### `errorBarX()` #### Sourced from a table When creating an errorBarX plot using data sourced from a table, the following syntax can be used: `.errorBarX("SeriesName", source, "x", "y", "xLow", "xHigh").show()` - `errorBarX` is the method used to create an errorBarX plot. - `"SeriesName"` is the name (as a string) you want to use to identify the series on the plot itself. - `source` is the table that holds the data to be used for the plot. - `"x"` is the name of the column of data to be used for the X value. - `"y"` is the name of the column of data to be used for the Y value. - `"xLow"` is the name of the column of data to be used for the low error value on the X axis. - `"xHigh"` is the name of the column of data to be used for the high error value on the X axis. - `show` tells Deephaven to draw the plot in the console. #### Sourced from an array When creating an errorBarX plot using data sourced from an array, the following syntax can be used: `.errorBarX("SeriesName", [x], [y], [xLow], [xHigh]).show` - `errorBarX` is the method used to create an errorBarX chart. - `"SeriesName"` is the name (as a string) you want to use to identify the series on the chart itself. - `"[x]"` is the array containing the data to be used for the X value. - `"[y]"` is the array containing the data to be used for the Y value. - `"[xLow]"` is the array containing the data to be used for the low error value on the X axis. - `"[xHigh]"` is the array containing the data to be used for the high error value on the X axis. - `show` tells Deephaven to draw the plot in the console. ### `errorBarY()` #### Sourced from a table When creating an errorBarY plot using data sourced from a table, the following syntax can be used: `.errorBarY("SeriesName", source, "x", "y", "yLow", "yHigh").show()` - `errorBarY` is the method used to create an errorBarY plot. - `"SeriesName"` is the name (as a string) you want to use to identify the series on the plot itself. - `source` is the table that holds the data to be used for the plot. - `"x"` is the name of the column of data to be used for the X value. - `"y"` is the name of the column of data to be used for the Y value. - `"yLow"` is the name of the column of data to be used for the low error value on the Y axis. - `"yHigh"` is the name of the column of data to be used for the high error value on the Y axis. - `show` tells Deephaven to draw the plot in the console. #### Sourced from an array When creating an errorBarY plot using data sourced from an array, the following syntax can be used: `.errorBarY("SeriesName", [x], [y], [yLow], [yHigh]).show()` - `errorBarY` is the method used to create an errorBarY plot. - `"SeriesName"` is the name (as a string) you want to use to identify the series on the chart itself. - `"[x]"` is the array containing the data to be used for the X value. - `"[y]"` is the array containing the data to be used for the Y value. - `"[yLow]"` is the array containing the data to be used for the low error value on the Y axis. - `"[yHigh]"` is the array containing the data to be used for the high error value on the Y axis. - `show` tells Deephaven to draw the plot in the console. #### Example The query shown in the example below will plot the standard deviation in the value of Google trades every 20 minutes. The resulting plot will be plotted as a line graph with error bars running vertically. ```python skip-test from deephaven import * # source the data t_EB = ( db.t("LearnDeephaven", "StockTrades") .where("Date = `2017-08-23`", "USym = `GOOG`") .updateView("TimeBin=upperBin(Timestamp, 20 * MINUTE)") .where("isBusinessTime(TimeBin)") ) # calculate standard deviations for the upper and lower error values t_EB_StdDev = t_EB.by( caf.AggCombo(caf.AggAvg("AvgPrice = Last"), caf.AggStd("StdPrice = Last")), "TimeBin", ) # plot the data ebY_Trades = Plot.errorBarY( "Trades: GOOG", t_EB_StdDev.update( "AvgPriceLow = AvgPrice - StdPrice", "AvgPriceHigh = AvgPrice + StdPrice" ), "TimeBin", "AvgPrice", "AvgPriceLow", "AvgPriceHigh", ).show() ``` ```groovy //source the data t_EB = db.t("LearnDeephaven", "StockTrades") .where("Date = `2017-08-23`", "USym = `GOOG`") .updateView("TimeBin=upperBin(Timestamp, 20 * MINUTE)") .where("isBusinessTime(TimeBin)") //calculate standard deviations for the upper and lower error values t_EB_StdDev = t_EB.by(AggCombo(AggAvg("AvgPrice = Last"), AggStd("StdPrice = Last")), "TimeBin") //plot the data ebY_Trades = errorBarY("Trades: GOOG", t_EB_StdDev.update("AvgPriceLow = AvgPrice - StdPrice", "AvgPriceHigh = AvgPrice + StdPrice"), "TimeBin", "AvgPrice", "AvgPriceLow", "AvgPriceHigh") .show() ``` > [!NOTE] > The Python query imports both the `Plot` and `ComboAggregateFactory` modules. The first code block: - accesses the StockTrades table in the LearnDeephaven namespace; - filters it to include only the data for August 23, 2017, and only when the USym is GOOG; - downsamples (bins) the data for 20-minute intervals; - and filters again to include only the data that occurs within business hours. The second code block: - calculates the average of the values in the Last column and the standard deviation of the values in the Last column, - and stores these results in new columns, AvgPrice and StdPrice respectively. The third code block: - creates an errorBarY plot named ebY_Trades, - names the series "Trades: GOOG", - sources the data from the t_EB_StdDev table, - creates two columns in that table using the `update` method: - AvgPriceLow is calculated by subtracting the standard deviation of the price from the average. - AvgPriceHigh is calculated by adding the average price to the standard deviation. - uses data from the TimeBin column for the X value, - uses data from the AvgPrice column as the Y value, - uses data from the AvgPriceLow column for the low error value on the Y axis, - uses data from the AvgPriceHigh column for the high error value on the Y axis, - and finally, the `show` method tells Deephaven to present the plot in the Deephaven console. ![img](../../assets/plotting/errorbar.png) ### `errorBarXY()` #### Sourced from a table When creating an errorBarXY plot using data sourced from a table, the following syntax can be used: `.errorBarXY("SeriesName", source, "x", "xLow", "xHigh", "y", "yLow", "yHigh").show()` - `errorBarXY` is the method used to create an errorBarXY plot. - `"SeriesName"` is the name (as a string) you want to use to identify the series on the plot itself. - `source` is the table that holds the data to be used for the plot. - `"x"` is the name of the column of data to be used for the X value. - `"xLow"` is the name of the column of data to be used for the low error value on the X axis. - `"xHigh"` is the name of the column of data to be used for the high error value on the X axis. - `"y"` is the name of the column of data to be used for the Y value. - `"yLow"` is the name of the column of data to be used for the low error value on the Y axis. - `"yHigh"` is the name of the column of data to be used for the high error value on the Y axis. - `show` tells Deephaven to draw the plot in the console. #### Sourced from an array When creating an errorBarX Plot using data sourced from an array, the following syntax can be used: `.errorBarXY("SeriesName", "[x]", [xLow], [xHigh], "[y]", [yLow], [yHigh]).show()` - `errorBarX` is the method used to create an errorBarX chart. - `"SeriesName"` is the name (as a string) you want to use to identify the series on the chart itself. - `"[x]"` is the array containing the data to be used for the X value. - `"[xLow]"` is the array containing the data to be used for low error value on the X axis. - `"[xHigh]"` is the array containing the data to be used for high error value on the X axis. - `"[y]"` is the array containing the data to be used for the Y value. - `"[yLow]"` is the array containing the data to be used for the low error value on the Y axis. - `"[yHigh]"` is the array containing the data to be used for the high error value on the Y axis - `show` tells Deephaven to draw the plot in the console. ## Customize Error Bar Color Users can choose a specific color for Error Bars using the `errorBarColor` method. > [!NOTE] > At this time, this feature is only supported in Deephaven Classic. The following query produces green error bars in a Deephaven Classic (Swing) console: ```python skip-test from deephaven import * # source the data t_EB = ( db.t("LearnDeephaven", "StockTrades") .where("Date = `2017-08-23`", "USym = `GOOG`") .updateView("TimeBin=upperBin(Timestamp, 20 * MINUTE)") .where("isBusinessTime(TimeBin)") ) # calculate standard deviations for the upper and lower error values t_EB_StdDev = t_EB.by( caf.AggCombo(caf.AggAvg("AvgPrice = Last"), caf.AggStd("StdPrice = Last")), "TimeBin", ) # plot the data ebY_Trades = ( Plot.errorBarY( "Trades: GOOG", t_EB_StdDev.update( "AvgPriceLow = AvgPrice - StdPrice", "AvgPriceHigh = AvgPrice + StdPrice" ), "TimeBin", "AvgPrice", "AvgPriceLow", "AvgPriceHigh", ) .errorBarColor("green") .show() ) ``` ```groovy //source the data t_EB = db.t("LearnDeephaven", "StockTrades") .where("Date = `2017-08-23`", "USym = `GOOG`") .updateView("TimeBin=upperBin(Timestamp, 20 * MINUTE)") .where("isBusinessTime(TimeBin)") //calculate standard deviations for the upper and lower error values t_EB_StdDev = t_EB.by(AggCombo(AggAvg("AvgPrice = Last"), AggStd("StdPrice = Last")), "TimeBin") //plot the data ebY_Trades = errorBarY("Trades: GOOG", t_EB_StdDev.update("AvgPriceLow = AvgPrice - StdPrice", "AvgPriceHigh = AvgPrice + StdPrice"), "TimeBin", "AvgPrice", "AvgPriceLow", "AvgPriceHigh") .errorBarColor("green") .show() ``` ![img](../../assets/plotting/errorbar2.png) > [!NOTE] > See > [Assigning Colors in Deephaven](../formatting/color-formatting.md) ## Category Plots with Error Bars Category plots with error bars can be created from data sourced from a table or an array. The method is: - `catErrorBar()` This creates a Category Plot with the discrete values on the X axis, numerical values on the Y axis. Error bars can only run vertically. ### `catErrorBar` #### Sourced from a table When data is sourced from a table, the following syntax can be used: `.catErrorBar("SeriesName", source, "x", "y", "yLow", "yHigh").show()` - `catErrorBarX` is the method used to create a category plot with vertical error bars. - `"SeriesName"` is the name (as a string) you want to use to identify the series on the plot itself. - `source` is the table that holds the data you want to plot. - `"x"` is the name of the column of data to be used for the X value. - `"y"` is the name of the column of data to be used for the Y value. - `"yLow"` is the name of the column of data to be used for the low error value. - `"yHigh"` is the name of the column of data to be used for the high error value. - `show` tells Deephaven to draw the plot in the console. The query shown in the example below will plot the standard deviation in the value of Google trades every 20 minutes: ```python skip-test from deephaven import * # source the data t_cat_EB = ( db.t("LearnDeephaven", "StockTrades") .where("Date = `2017-08-23`", "USym = `GOOG`") .updateView("TimeBin=upperBin(Timestamp, 20 * MINUTE)") .where("isBusinessTime(TimeBin)") ) # calculate standard deviations for the upper and lower error values t_cat_EB_StdDev = t_cat_EB.by( caf.AggCombo(caf.AggAvg("AvgPrice = Last"), caf.AggStd("StdPrice = Last")), "TimeBin", ) # plot the data eb_CatTrades = Plot.catErrorBar( "Trades: GOOG", t_cat_EB_StdDev.update( "AvgPriceLow = AvgPrice - StdPrice", "AvgPriceHigh = AvgPrice + StdPrice" ), "TimeBin", "AvgPrice", "AvgPriceLow", "AvgPriceHigh", ).show() ``` ```groovy // source the data t_cat_EB = db.t("LearnDeephaven", "StockTrades") .where("Date = `2017-08-23`", "USym = `GOOG`") .updateView("TimeBin=upperBin(Timestamp, 20 * MINUTE)") .where("isBusinessTime(TimeBin)") // calculate standard deviations for the upper and lower error values t_cat_EB_StdDev = t_cat_EB.by(AggCombo(AggAvg("AvgPrice = Last"), AggStd("StdPrice = Last")), "TimeBin") // plot the data eb_CatTrades = catErrorBar("Trades: GOOG", t_cat_EB_StdDev.update("AvgPriceLow = AvgPrice - StdPrice", "AvgPriceHigh = AvgPrice + StdPrice"), "TimeBin", "AvgPrice", "AvgPriceLow", "AvgPriceHigh") .show() ``` The first code block: - gathers the data for the plot from the StockTrades table in the LearnDeephaven namespace, - filters it to include only the data for August 23, 2017, and only when the USym is GOOG, - downsamples (bins) the data for 20 minute intervals, - then filters again to include only the data that occurs within business hours. The second code block: - calculates the average of the values in the Last column and the standard deviation of the values in the Last column, - and stores the results will appear in new columns, AvgPrice and StdPrice respectively. The third code block: - creates a category error bar plot named eb_CatTrades, - names the series "Trades: GOOG", - creates two columns using the `update` method: - AvgPriceLow is calculated by subtracting the standard deviation of the price from the average. - AvgPriceHigh is calculated by adding the average price to the standard deviation. - uses data from the TimeBin column for the X value, - uses data from the AvgPrice column as the Y value, - uses data from the AvgPriceLow column for the low error value on the Y axis, - uses data from the AvgPriceHigh column for the high error value on the Y axis, - and finally the `show` method tells Deephaven to present the plot in the Deephaven console. ![img](../../assets/plotting/errorbar3.png) #### Sourced from an array When data is sourced from an array, the following syntax can be used: `.catErrorBar("SeriesName", [x], [y], [yLow], [yHigh]).show()` - `catErrorBarX` is the method used to create a category plot with vertical error bars. - `"SeriesName"` is the name (as a string) you want to use to identify the series on the plot itself. - `"[x]"` is the name of the column of data to be used for the X value. - `"[y]"` is the name of the column of data to be used for the Y value. - `"[yLow]"` is the name of the column of data to be used for the low error value on the Y axis. - `"[yHigh]` is the name of the column of data to be used for the high error value on the Y axis. - `show` tells Deephaven to draw the plot in the console. ## Related documentation - [Component Formatting](./component-formatting.md) - [Visual Formatting](./visual-formatting.md)