![]() To future-proof thisĬurrent usage of this method, wrap the return value in list. Mapping property names to property objects. In a future version of Bokeh, this method will return a dictionary Other_attr ( str) – The property on other to link togetherĪttr_selector ( Union ) – The index to link an item in a subscriptable attr Other ( Model) – A Bokeh model to link to self.attr Parameters :Īttr ( str) – The name of a Bokeh property on this model This is a convenience method that simplifies adding aĬustomJS callback to update one Bokeh model Link two Bokeh model properties using JavaScript. True, if properties are structurally equal, otherwise False js_link ( attr : str, other : Model, other_attr : str, attr_selector : int | str | None = None ) → None # ![]() Other ( HasProps) – the other instance to compare to Returns : destroy ( ) → None #Ĭlean up references to the document and property equals ( other : HasProps ) → bool # List of property descriptors in the order of definition. Set classmethod descriptors ( ) → list ] # Names of DataSpec properties Return type : Properties defined on any parent classes. This method always traverses the class hierarchy and includes classmethod dataspecs ( ) → dict #Ĭollect the names of all DataSpec properties on this class. Mutable containers or child models will not be duplicated. This creates a shallow clone of the original model, i.e. Property_values ( dict) – theme values to use in place of defaults Returns : Other instances to save memory (so neither the caller nor the The passed-in dictionary may be kept around as-is and shared with apply_theme ( property_values : dict ) → None #Īpply a set of theme values which will be used rather thanĭefaults, but will not override application-set values. That are provided, nor are the tags used directly by Bokeh for any No uniqueness guarantees or other conditions are enforced on any tags circle ( x = quarters, y = quarterly, size = 10, line_color = line_color, fill_color = "white", line_width = 3 ) p. line ( x = quarters, y = quarterly, color = line_color, line_width = 3 ) p. ![]() vbar ( x = months, top = monthly, width = 0.8, fill_color = fill_color, fill_alpha = 0.8, line_color = line_color, line_width = 1.2 ) quarterly = p. orientation = "horizontal" show ( p )įrom bokeh.models import FactorRange from bokeh.palettes import TolPRGn4 from otting import figure, show quarters = ( "Q1", "Q2", "Q3", "Q4" ) months = ( ( "Q1", "jan" ), ( "Q1", "feb" ), ( "Q1", "mar" ), ( "Q2", "apr" ), ( "Q2", "may" ), ( "Q2", "jun" ), ( "Q3", "jul" ), ( "Q3", "aug" ), ( "Q3", "sep" ), ( "Q4", "oct" ), ( "Q4", "nov" ), ( "Q4", "dec" ), ) fill_color, line_color = TolPRGn4 p = figure ( x_range = FactorRange ( * months ), height = 500, tools = "", background_fill_color = "#fafafa", toolbar_location = None ) monthly = p. vbar_stack ( regions, x = 'x', width = 0.9, alpha = 0.5, color =, source = source, legend_label = regions ) p. 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. 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. 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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