![]() You need to load the Bokeh js and css in your template, and render the components created by Bokeh. You can just import Bokeh into you views.py. vbar_stack ( regions, x = 'x', width = 0.9, alpha = 0.5, color =, source = source, legend_label = regions ) p. You don't need to run a Bokeh server to use Bokeh in DJANGO. ![]() orientation = "horizontal" show ( p )įrom bokeh.models import ColumnDataSource, FactorRange from otting import figure, show factors = regions = source = ColumnDataSource ( data = dict ( x = factors, east =, west =, )) p = figure ( x_range = FactorRange ( * factors ), height = 250, toolbar_location = None, tools = "" ) p. vbar ( x = dodge ( 'fruits', 0.25, range = p. vbar ( x = dodge ( 'fruits', 0.0, range = p. vbar ( x = dodge ( 'fruits', - 0.25, range = p. However, lets look at what we are doing here, there are 125 images that represent the plot - one for each tick combination of any of the sliders. It will take a few seconds to generate and the file size would be 1.5Mb - it will smaller without resources included. You dont need to run a Bokeh server to use Bokeh in DJANGO. The example below shows a sequence of simpleįrom bokeh.models import ColumnDataSource from bokeh.palettes import GnBu3, OrRd3 from otting import figure, show fruits = years = exports = source = ColumnDataSource ( data = data ) p = figure ( x_range = fruits, y_range = ( 0, 10 ), title = "Fruit Counts by Year", height = 350, toolbar_location = None, tools = "" ) p. In this situation there are 5x5x5125 states. To create a basic bar chart, use the hbar() (horizontal bars) or vbar() This section will demonstrate how to draw a variety ofĭifferent categorical bar charts. ![]() The length of this bar along the continuous axis corresponds toīar charts may also be stacked or grouped together according to hierarchical The values associated with each category are represented by drawing a bar for BarĬharts are useful when there is one value to plot for each category. Bar charts have one categorical axis and one continuous axis. One of the most common ways to handle categorical data is to present it in aīar chart. Present several kinds of common plot types for categorical data. Months_by_quarter = ĭepending on the structure of your data, you can use different kinds of charts:īar charts, categorical heatmaps, jitter plots, and others.
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