lets_plot.geom_errorbar(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, color_by=None, **other_args)#

Display error bars defined by the upper and lower values.


Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or Pandas or Polars DataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

statstr, default=’identity’

The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).

positionstr or FeatureSpec, default=’identity’

Position adjustment. Either a position adjustment name: ‘dodge’, ‘dodgev’, ‘jitter’, ‘nudge’, ‘jitterdodge’, ‘fill’, ‘stack’ or ‘identity’, or the result of calling a position adjustment function (e.g., position_dodge() etc.).

show_legendbool, default=True

False - do not show legend for this layer.


Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).


Result of the call to the layer_tooltips() function. Specify appearance, style and content.

color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’

Define the color aesthetic for the geometry.


Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.


Geom object specification.


geom_errorbar() represents a vertical interval, defined by x, ymin, ymax, or a horizontal interval, defined by y, xmin, xmax.

geom_errorbar() understands the following aesthetics mappings:

  • x or y: x-axis or y-axis coordinates for vertical or horizontal error bar, respectively.

  • ymin or xmin: lower bound for vertical or horizontal error bar, respectively.

  • ymax or xmax: upper bound for vertical or horizontal error bar, respectively.

  • alpha : transparency level of a layer. Accept values between 0 and 1.

  • color (colour) : color of the geometry lines. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

  • size : line width. Define bar line width.

  • width or height : size of the whiskers of vertical or horizontal bar, respectively. Typically range between 0 and 1. Values that are greater than 1 lead to overlapping of the bars.

  • linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.


1from lets_plot import *
3data = {
4    'x': ['a', 'b', 'c', 'd'],
5    'ymin': [5, 7, 3, 5],
6    'ymax': [8, 11, 6, 9],
8ggplot(data, aes(x='x')) + \
9    geom_errorbar(aes(ymin='ymin', ymax='ymax'))

 1import numpy as np
 2import pandas as pd
 3from lets_plot import *
 6n = 1000
 7x = np.random.randint(10, size=n)
 8y = np.sqrt(x) + np.random.normal(scale=.3, size=n)
 9df = pd.DataFrame({'x': x, 'y': y})
10err_df = df.groupby('x').agg({'y': ['min', 'max']}).reset_index()
11err_df.columns = ['x', 'ymin', 'ymax']
12ggplot() + \
13    geom_errorbar(aes(x='x', ymin='ymin', ymax='ymax'), \
14                  data=err_df, width=.5, color='red') + \
15    geom_jitter(aes(x='x', y='y'), data=df, width=.2, size=1) + \
16    scale_x_continuous(breaks=list(range(10)))

 1from lets_plot import *
 3data = {
 4    'xmin': [0.2, 4.6, 1.6, 3.5],
 5    'xmax': [1.5, 5.3, 3.0, 4.4],
 6    'y': ['a', 'a', 'b', 'b'],
 7    'c': ['gr1', 'gr2', 'gr1', 'gr2']
 9ggplot(data) + \
10    geom_errorbar(aes(y='y', xmin='xmin', xmax='xmax', color='c'), height=0.1, size=2)