lets_plot.geom_pointrange

lets_plot.geom_pointrange(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, fatten=None, **other_args)

Add a vertical line defined by upper and lower value with midpoint at y location.

Parameters
mappingFeatureSpec

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 DataFrame 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

Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

show_legendbool, default=True

False - do not show legend for this layer.

samplingFeatureSpec

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

tooltipslayer_tooltips

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

fattenfloat, default=5.0

A multiplicative factor applied to size of the middle point.

other_args

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.

Returns
LayerSpec

Geom object specification.

Notes

geom_pointrange() represents a vertical interval, defined by x, ymin, ymax. The mid-point is defined by y.

geom_pointrange() understands the following aesthetics mappings:

  • x : x-axis coordinates.

  • y : position of mid-point.

  • ymin : lower bound for error bar.

  • ymax : upper bound for error bar.

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

  • color (colour) : color of the geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

  • fill : color of geometry filling.

  • size : line width, size of mid-point.

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

  • shape : shape of the mid-point, an integer from 0 to 25.

Examples

 1from lets_plot import *
 2LetsPlot.setup_html()
 3data = {
 4    'x': ['a', 'b', 'c', 'd'],
 5    'ymin': [5, 7, 3, 5],
 6    'y': [6.5, 9, 4.5, 7],
 7    'ymax': [8, 11, 6, 9],
 8}
 9ggplot(data, aes(x='x', y='y')) + \
10    geom_pointrange(aes(ymin='ymin', ymax='ymax'))


 1import numpy as np
 2import pandas as pd
 3from lets_plot import *
 4LetsPlot.setup_html()
 5n = 800
 6cat_list = {c: np.random.uniform(3) for c in 'abcdefgh'}
 7np.random.seed(42)
 8x = np.random.choice(list(cat_list.keys()), n)
 9y = np.array([cat_list[c] for c in x]) + np.random.normal(size=n)
10df = pd.DataFrame({'x': x, 'y': y})
11err_df = df.groupby('x').agg({'y': ['min', 'mean', 'max']}).reset_index()
12err_df.columns = ['x', 'ymin', 'ymean', 'ymax']
13ggplot(err_df, aes(x='x', y='ymean')) + \
14    geom_pointrange(aes(ymin='ymin', ymax='ymax'), \
15                    show_legend=False, fatten=10, shape=4, \
16                    color='red', size=1)