![]() annotate ( 'local max', xy = ( 2, 1 ), xycoords = 'data', xytext = ( 0.01. This example places the text coordinates in fractional axes coordinates:įig, ax = plt. Pixels are discussed in further detail in Plotting in physical coordinates. Meaning that they are a physical unit measuring 1/72 of an inch. The following strings are also valid arguments for textcoordsįor physical coordinate systems (points or pixels) the origin is theīottom-left of the figure or axes. (0, 0) is lower left of axes and (1, 1) is upper right ![]() (0, 0) is lower left of figure and (1, 1) is upper right Pixels from the lower left corner of the figure Points from the lower left corner of the figure Xycoords and textcoords (default is 'data') System of xy and xytext with one of the following strings for There are a variety of otherĬoordinate systems one can choose - you can specify the coordinate In this example, both the xy (arrow tip) and xytext locations ![]() annotate ( 'local max', xy = ( 2, 1 ), xytext = ( 3, 1.5 ), arrowprops = dict ( facecolor = 'black', shrink = 0.05 )) ax. Import numpy as np import matplotlib.pyplot as plt fig, ax = plt. In an annotation, there are two points to consider: the location of the dataīeing annotated xy and the location of the annotation text xytext. Placing Artist at anchored Axes locations Placing text annotations relative to data Provide as much flexibility in positioning and styling as annotate. Text can also be used for simple text annotation, but does not Axes.annotate also provides an optional arrowįrom the text to the data and this arrow can be styled in various ways. Positioning data and annotations relative to each other and a variety of x_range ), "hwy", source = df, alpha = 0.To download the full example code Annotations #Īnnotations are graphical elements, often pieces of text, that explain, addĬontext to, or otherwise highlight some portion of the visualized data.Īnnotate supports a number of coordinate systems for flexibly circle ( jitter ( "class", 0.3, range = p. quantile ( 0.20 ) source = ColumnDataSource ( data = dict ( base = classes, upper = upper, lower = lower )) error = Whisker ( base = "base", upper = "upper", lower = "lower", source = source, level = "annotation", line_width = 2 ) error. unique ())) p = figure ( height = 400, x_range = classes, background_fill_color = "#efefef", title = "Car class vs HWY mpg with quintile ranges" ) p. grid_line_color = None show ( p )įrom bokeh.models import ColumnDataSource, Whisker from otting import figure, show from 2 import autompg2 as df from ansform import factor_cmap, jitter classes = list ( sorted ( df. line ( "Date", "Sunrise", source = daylight_warsaw_2013, color = 'orange', legend_label = "Sunrise" ) dst_start = Span ( location = dt ( 2013, 3, 31, 2, 0, 0 ), dimension = 'height', line_color = '#009E73', line_width = 5 ) p. line ( "Date", "Sunset", source = daylight_warsaw_2013, color = 'navy', legend_label = "Sunset" ) p. add_layout ( band ) show ( p )įrom datetime import datetime as dt from bokeh.models import Span from otting import figure, show from import daylight_warsaw_2013 p = figure ( height = 350, x_axis_type = "datetime", y_axis_type = "datetime", title = "2013 Sunrise and Sunset in Warsaw with DST dates marked", y_axis_label = "Time of Day", background_fill_color = "#fafafa" ,) p. line ( "x", "y_mean", line_dash = ( 10, 7 ), line_width = 2, source = source ) band = Band ( base = "x", lower = "lower", upper = "upper", source = source, fill_alpha = 0.3, fill_color = "gray", line_color = "black" ) p. scatter ( x = "x", y = "y", marker = "dot", size = 10, alpha = 0.4, source = source ) p. reset_index ()) p = figure ( tools = "", toolbar_location = None, x_range = ( 40, 160 )) p. ![]() DataFrame ( data = dict ( x = x, y = y )). ![]() Import numpy as np import pandas as pd from bokeh.models import Band, ColumnDataSource from otting import figure, show # Create some random data x = np. ![]()
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