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bokeh.charts

Chart Options

See the options available as input to all Charts in Chart Defaults. Each of these can be set at a global level with the shared defaults object, or can be passed as kwargs to each Chart.

Charts

Area

Area(data=None, x=None, y=None, **kws)[source]

Create an area chart using AreaBuilder to render the geometry from values.

Parameters:

In addition the the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:includes glyph renderers that generate the area glyphs
Return type:Chart

Examples:

from bokeh.charts import Area, show, output_file

# create some example data
data = dict(
    python=[2, 3, 7, 5, 26, 221, 44, 233, 254, 265, 266, 267, 120, 111],
    pypy=[12, 33, 47, 15, 126, 121, 144, 233, 254, 225, 226, 267, 110, 130],
    jython=[22, 43, 10, 25, 26, 101, 114, 203, 194, 215, 201, 227, 139, 160],
)

area = Area(data, title="Area Chart", legend="top_left",
            xlabel='time', ylabel='memory')

output_file('area.html')
show(area)

Bar

Bar(data, label=None, values=None, color=None, stack=None, group=None, agg='sum', xscale='categorical', yscale='linear', xgrid=False, ygrid=True, continuous_range=None, **kw)[source]

Create a Bar chart using BarBuilder render the geometry from values, cat and stacked.

Parameters:
  • data (Accepted Charts Data Formats) – the data source for the chart.
  • label (list(str) or str, optional) – list of string representing the categories. (Defaults to None)
  • values (str, optional) – iterable 2d representing the data series values matrix.
  • color (str or list(str) or ~bokeh.charts._attributes.ColorAttr) – string color, string column name, list of string columns or a custom ColorAttr, which replaces the default ColorAttr for the builder.
  • stack (list(str) or str, optional) – columns to use for stacking. (Defaults to False, so grouping is assumed)
  • group (list(str) or str, optional) – columns to use for grouping.
  • agg (str) – how to aggregate the values. (Defaults to ‘sum’, or only label is provided, then performs a count)
  • continuous_range (Range1d, optional) – Custom continuous_range to be used. (Defaults to None)

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:includes glyph renderers that generate bars
Return type:Chart

Examples

from bokeh.charts import Bar, output_file, show
from bokeh.layouts import row

# best support is with data in a format that is table-like
data = {
    'sample': ['1st', '2nd', '1st', '2nd', '1st', '2nd'],
    'interpreter': ['python', 'python', 'pypy', 'pypy', 'jython', 'jython'],
    'timing': [-2, 5, 12, 40, 22, 30]
}

# x-axis labels pulled from the interpreter column, stacking labels from sample column
bar = Bar(data, values='timing', label='interpreter', stack='sample', agg='mean',
          title="Python Interpreter Sampling", legend='top_right', plot_width=400)

# table-like data results in reconfiguration of the chart with no data manipulation
bar2 = Bar(data, values='timing', label=['interpreter', 'sample'],
           agg='mean', title="Python Interpreters", plot_width=400)

output_file("stacked_bar.html")
show(row(bar, bar2))

BoxPlot

BoxPlot(data, label=None, values=None, color=None, group=None, xscale='categorical', yscale='linear', xgrid=False, ygrid=True, continuous_range=None, **kw)[source]

Create a BoxPlot chart containing one or more boxes from table-like data.

Create a boxplot chart using BoxPlotBuilder to render the glyphs from input data and specification. This primary use case for the boxplot is to depict the distribution of a variable by providing summary statistics for it. This boxplot is particularly useful at comparing distributions between categorical variables.

This chart implements functionality for segmenting and comparing the values of a variable by an associated categorical variable.

Reference: BoxPlot on Wikipedia

Parameters:
  • data (Accepted Charts Data Formats) – the data source for the chart
  • values (str, optional) – the values to use for producing the boxplot using table-like input data
  • label (str or list(str), optional) – the categorical variable to use for creating separate boxes
  • color (str or list(str) or bokeh.charts._attributes.ColorAttr, optional) – the categorical variable or color attribute specification to use for coloring the boxes.
  • whisker_color (str or list(str) or bokeh.charts._attributes.ColorAttr, optional) – the color of the “whiskers” that show the spread of values outside the .25 and .75 quartiles.
  • marker (str or list(str) or bokeh.charts._attributes.MarkerAttr, optional) – the marker glyph to use for the outliers
  • outliers (bool, optional) – whether to show outliers. Defaults to True.
  • **kw –

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:includes glyph renderers that generate Boxes and Whiskers
Return type:Chart

Examples:

from bokeh.charts import BoxPlot, output_file, show
from bokeh.layouts import row
from bokeh.sampledata.autompg import autompg as df

box = BoxPlot(df, values='mpg', label='cyl', title="Auto MPG Box Plot", plot_width=400)
box2 = BoxPlot(df, values='mpg', label='cyl', color='cyl',
               title="MPG Box Plot by Cylinder Count", plot_width=400)

output_file('box.html')
show(row(box, box2))

Chord

Chord(data, source=None, target=None, value=None, square_matrix=False, label=None, xgrid=False, ygrid=False, **kw)[source]

Create a chord chart using ChordBuilder to render a chord graph from a variety of value forms. This chart displays the inter-relationships between data in a matrix.

The data can be generated by the chart interface. Given a DataFrame, select two columns to be used as arcs with source and target attributes, passing by the name of those columns. The Chord chart will then deduce the relationship between the arcs.

The value of the connections can be inferred automatically by counting source and target. If you prefer you can assign a fixed value for all the connections with value simply passing by a number. A third option is to pass a reference to a third column in the DataFrame with the values for the connections.

If you want to plot the relationships in a squared matrix, simply pass the matrix and set square_matrix attribute to True.

Reference: Chord diagram on Wikipedia

Parameters:
  • data (Accepted Charts Data Formats) – the data source for the chart.
  • source (list(str) or str, optional) – Data source to use as origin of the connection to a destination.
  • target (list(str) or str, optional) – Data source to use as destination of a connection.
  • value (list(num) or num, optional) – The value the connection should have.
  • square_matrix (bool, optional) – If square matrix, avoid any calculations during the setup.
  • label (list(str), optional) – The labels to be put in the areas.
Returns:

includes glyph renderers that generate the chord
Return type:

Chart

Examples:

import pandas as pd
from bokeh.charts import Chord
from bokeh.io import show, output_file
from bokeh.sampledata.les_mis import data

nodes = data['nodes']
links = data['links']

nodes_df = pd.DataFrame(nodes)
links_df = pd.DataFrame(links)

source_data = links_df.merge(nodes_df, how='left', left_on='source', right_index=True)
source_data = source_data.merge(nodes_df, how='left', left_on='target', right_index=True)
source_data = source_data[source_data["value"] > 5]  # Select those with 5 or more connections

chord_from_df = Chord(source_data, source="name_x", target="name_y", value="value")
output_file('chord_from_df.html')
show(chord_from_df)

Donut

Donut(data, label='index', values=None, color=None, agg=None, hover_tool=True, hover_text=None, plot_height=400, plot_width=400, xgrid=False, ygrid=False, **kw)[source]

Create a Donut chart containing one or more layers from table-like data.

Create a donut chart using DonutBuilder to render the glyphs from input data and specification. The primary use case for the donut chart is to show relative amount each category, within a categorical array or multiple categorical arrays, makes up of the whole for some array of values.

Parameters:
  • data (Accepted Charts Data Formats) – the data source for the chart label (str or list(str), optional): the categorical variable to use for creating separate boxes
  • values (str, optional) – the values to use for producing the boxplot using table-like input data
  • color (str or list(str) or bokeh.charts._attributes.ColorAttr, optional) – the categorical variable or color attribute specification to use for coloring the wedges
  • agg (str, optional) – how the values associated with a wedge should be aggregated hover_tool (bool, optional): whether to show the value of the wedge when hovering
  • hover_text (str, optional) – provide an alternative string to use label the value shown with the hover tool
  • **kw –

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:includes glyph renderers that generate the wedges the make up the donut(s)
Return type:Chart

Examples:

from bokeh.charts import Donut, show, output_file
from bokeh.charts.utils import df_from_json
from bokeh.sampledata.olympics2014 import data

import pandas as pd

# utilize utility to make it easy to get json/dict data converted to a dataframe
df = df_from_json(data)

# filter by countries with at least one medal and sort by total medals
df = df[df['total'] > 8]
df = df.sort_values(by="total", ascending=False)
df = pd.melt(df, id_vars=['abbr'],
             value_vars=['bronze', 'silver', 'gold'],
             value_name='medal_count', var_name='medal')

# original example
d = Donut(df, label=['abbr', 'medal'], values='medal_count',
          text_font_size='8pt', hover_text='medal_count')

output_file("donut.html")

show(d)

HeatMap

HeatMap(data, x=None, y=None, values=None, stat='count', xgrid=False, ygrid=False, hover_tool=True, hover_text=None, **kw)[source]

Represent 3 dimensions in a HeatMap chart using x, y, and values.

Uses the HeatMapBuilder to render the geometry from values.

A HeatMap is a 3 Dimensional chart that crosses two dimensions, then aggregates values that correspond to the intersection of the horizontal and vertical dimensions. The value that falls at the intersection is then mapped to a color in a palette by default. All values that map to the positions on the chart are binned by the number of discrete colors in the palette.

Parameters:
  • data (Accepted Charts Data Formats) – the data source for the chart
  • x (str or list(str), optional) – specifies variable(s) to use for x axis
  • y (str or list(str), optional) – specifies variable(s) to use for y axis
  • values (str, optional) – the values to use for producing the histogram using table-like input data
  • stat (str, optional) – the aggregation to use. Defaults to count. If provided None, then no aggregation will be attempted. This is useful for cases when the values have already been aggregated.
  • hover_tool (bool, optional) – whether to show the hover tool. Defaults to True
  • hover_text (str, optional) – a string to place beside the value in the hover tooltip. Defaults to None. When None, a hover_text will be derived from the aggregation and the values column.

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:a new Chart

Examples:

from bokeh.charts import HeatMap, output_file, show

# (dict, OrderedDict, lists, arrays and DataFrames are valid inputs)
data = {'fruit': ['apples']*3 + ['bananas']*3 + ['pears']*3,
        'fruit_count': [4, 5, 8, 1, 2, 4, 6, 5, 4],
        'sample': [1, 2, 3]*3}

hm = HeatMap(data, x='fruit', y='sample', values='fruit_count',
             title='Fruits', stat=None)

output_file('heatmap.html')
show(hm)

Histogram

Histogram(data, values=None, label=None, color=None, agg='count', bins=None, yscale='linear', xgrid=False, ygrid=True, continuous_range=None, **kw)[source]

Create a histogram chart with one or more histograms.

Create a histogram chart using HistogramBuilder to render the glyphs from input data and specification. This primary use case for the histogram is to depict the distribution of a variable by binning and aggregating the values in each bin.

This chart implements functionality to provide convenience in optimal selection of bin count, but also for segmenting and comparing segments of the variable by a categorical variable.

Parameters:
  • data (Accepted Charts Data Formats) – the data source for the chart. Must consist of at least 2 values. If all values are equal, the result is a single bin with arbitrary width.
  • values (str, optional) – the values to use for producing the histogram using table-like input data
  • label (str or list(str), optional) – the categorical variable to use for creating separate histograms
  • color (str or list(str) or ~bokeh.charts._attributes.ColorAttr, optional) – the categorical variable or color attribute specification to use for coloring the histogram, or explicit color as a string.
  • agg (str, optional) – how to aggregate the bins. Defaults to “count”.
  • bins (int or list(float), optional) – the number of bins to use, or an explicit list of bin edges. Defaults to None to auto select.
  • density (bool, optional) – whether to normalize the histogram. Defaults to False.
  • **kw –

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:includes glyph renderers that generate the histograms
Return type:Chart

Examples:

from bokeh.charts import Histogram, output_file, show
from bokeh.layouts import row
from bokeh.sampledata.autompg import autompg as df

hist = Histogram(df, values='mpg', title="Auto MPG Histogram", plot_width=400)
hist2 = Histogram(df, values='mpg', label='cyl', color='cyl', legend='top_right',
                  title="MPG Histogram by Cylinder Count", plot_width=400)

output_file('hist.html')
show(row(hist, hist2))

Horizon

Horizon(data=None, x=None, y=None, series=None, **kws)[source]

Create a horizon chart using HorizonBuilder to render the geometry from values.

Parameters:

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:includes glyph renderers that generate the scatter points
Return type:Chart

Examples:

import pandas as pd
from bokeh.charts import Horizon, output_file, show

# read in some stock data from the Yahoo Finance API
AAPL = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=AAPL&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])

MSFT = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=MSFT&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])

IBM = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=IBM&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])

data = dict([
    ('AAPL', AAPL['Adj Close']),
    ('Date', AAPL['Date']),
    ('MSFT', MSFT['Adj Close']),
    ('IBM', IBM['Adj Close'])]
)

hp = Horizon(data, x='Date', plot_width=800, plot_height=300,
             title="horizon plot using stock inputs")

output_file("horizon.html")

show(hp)

Line

Line(data=None, x=None, y=None, **kws)[source]

Create a line chart using LineBuilder to render the glyphs.

The line chart is typically is used with column oriented data, where each column contains comparable measurements and the column names are treated as a categorical variable for differentiating the measurement values. One of the columns can be used as an index for either the x or y axis.

Note

Only the x or y axis can display multiple variables, while the other is used as an index.

Parameters:

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Note

This chart type differs on input types as compared to other charts, due to the way that line charts typically are plotting labeled series. For example, a column for AAPL stock prices over time. Another way this could be plotted is to have a DataFrame with a column of stock_label and columns of price, which is the stacked format. Both should be supported, but the former is the expected one. Internally, the latter format is being derived.

Returns:includes glyph renderers that generate the lines
Return type:Chart

Examples:

import numpy as np
from bokeh.charts import Line, output_file, show

# (dict, OrderedDict, lists, arrays and DataFrames are valid inputs)
xyvalues = np.array([[2, 3, 7, 5, 26], [12, 33, 47, 15, 126], [22, 43, 10, 25, 26]])

line = Line(xyvalues, title="line", legend="top_left", ylabel='Languages')

output_file('line.html')
show(line)

Scatter

Scatter(data=None, x=None, y=None, **kws)[source]

Create a scatter chart using ScatterBuilder to render the geometry from values.

Parameters:

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:includes glyph renderers that generate the scatter points
Return type:Chart

Examples:

from bokeh.sampledata.autompg import autompg as df
from bokeh.charts import Scatter, output_file, show

scatter = Scatter(df, x='mpg', y='hp', color='cyl', marker='origin',
                  title="Auto MPG", xlabel="Miles Per Gallon",
                  ylabel="Horsepower")

output_file('scatter.html')
show(scatter)

Step

Step(data=None, x=None, y=None, **kws)[source]

Create a step chart using StepBuilder to render the geometry from the inputs.

Note

Only the x or y axis can display multiple variables, while the other is used as an index.

Parameters:

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Note

This chart type differs on input types as compared to other charts, due to the way that series-type charts typically are plotting labeled series. For example, a column for AAPL stock prices over time. Another way this could be plotted is to have a DataFrame with a column of stock_label and columns of price, which is the stacked format. Both should be supported, but the former is the expected one. Internally, the latter format is being derived.

Returns:includes glyph renderers that generate the stepped lines
Return type:Chart

Examples:

from bokeh.charts import Step, show, output_file

# build a dataset where multiple columns measure the same thing
data = dict(
           stamp=[.33, .33, .34, .37, .37, .37, .37, .39, .41, .42,
                  .44, .44, .44, .45, .46, .49, .49],
           postcard=[.20, .20, .21, .23, .23, .23, .23, .24, .26, .27,
                     .28, .28, .29, .32, .33, .34, .35]
       )

# create a step chart where each column of measures receives a unique color and dash style
step = Step(data, y=['stamp', 'postcard'],
            dash=['stamp', 'postcard'],
            color=['stamp', 'postcard'],
            title="U.S. Postage Rates (1999-2015)",
            ylabel='Rate per ounce', legend=True)

output_file("steps.html")

show(step)

TimeSeries

TimeSeries(data=None, x=None, y=None, builder_type=<class 'bokeh.charts.builders.line_builder.LineBuilder'>, **kws)[source]

Create a timeseries chart using LineBuilder to produce the renderers from the inputs. The timeseries chart acts as a switchboard to produce charts for timeseries data with different glyph representations.

Parameters:
  • data (list(list), numpy.ndarray, pandas.DataFrame, list(pd.Series)) – a 2d data source with columns of data for each stepped line.
  • x (str or list(str), optional) – specifies variable(s) to use for x axis
  • y (str or list(str), optional) – specifies variable(s) to use for y axis
  • builder_type (str or Builder, optional) – the type of builder to use to produce the renderers. Supported options are ‘line’, ‘step’, or ‘point’.

In addition to the parameters specific to this chart, Chart Defaults are also accepted as keyword parameters.

Returns:a new Chart

Examples:

import pandas as pd

from bokeh.charts import TimeSeries, show, output_file
from bokeh.layouts import column

# read in some stock data from the Yahoo Finance API
AAPL = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=AAPL&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])
MSFT = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=MSFT&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])
IBM = pd.read_csv(
    "http://ichart.yahoo.com/table.csv?s=IBM&a=0&b=1&c=2000&d=0&e=1&f=2010",
    parse_dates=['Date'])

data = dict(
    AAPL=AAPL['Adj Close'],
    Date=AAPL['Date'],
    MSFT=MSFT['Adj Close'],
    IBM=IBM['Adj Close'],
)

tsline = TimeSeries(data,
    x='Date', y=['IBM', 'MSFT', 'AAPL'],
    color=['IBM', 'MSFT', 'AAPL'], dash=['IBM', 'MSFT', 'AAPL'],
    title="Timeseries", ylabel='Stock Prices', legend=True)

tspoint = TimeSeries(data,
    x='Date', y=['IBM', 'MSFT', 'AAPL'],
    color=['IBM', 'MSFT', 'AAPL'], dash=['IBM', 'MSFT', 'AAPL'],
    builder_type='point', title="Timeseries Points",
    ylabel='Stock Prices', legend=True)

output_file("timeseries.html")

show(column(tsline, tspoint))

Chart Functions

Data Operations

blend(*cols, **kwargs)[source]

Provides a simple function for specifying a Blend data operation.

Parameters:
  • cols (str) – each column to use for blending by name
  • **kwargs –

    the keyword args supported by Blend

    • name (str): name of the column to contain the blended values
    • labels_name (str): name of the column to contain the name of the columns used for blending

See Blend

bins(data, values=None, column=None, bins=None, labels=None, **kwargs)[source]

Specify binning or bins to be used for column or values.

Attribute Generators

color(columns=None, palette=None, bin=False, **kwargs)[source]

Produces a ColorAttr specification for coloring groups of data based on columns.

Parameters:
  • columns (str or list(str), optional) – a column or list of columns for coloring
  • palette (list(str), optional) – a list of colors to use for assigning to unique values in columns.
  • **kwargs – any keyword, arg supported by AttrSpec
Returns:

a ColorAttr object
marker(columns=None, markers=None, **kwargs)[source]

Specifies detailed configuration for a marker attribute.

Parameters:
  • columns (list or str) –
  • markers (list(str) or str) – a custom list of markers. Must exist within marker_types.
  • **kwargs – any keyword, arg supported by AttrSpec
Returns:

a MarkerAttr object
cat(columns=None, cats=None, sort=True, ascending=True, **kwargs)[source]

Specifies detailed configuration for a chart attribute that uses categoricals.

Parameters:
  • columns (list or str) – the columns used to generate the categorical variable
  • cats (list, optional) – overrides the values derived from columns
  • sort (bool, optional) – whether to sort the categorical values (default=True)
  • ascending (bool, optional) – whether to sort the categorical values (default=True)
  • **kwargs – any keyword, arg supported by AttrSpec
Returns:

a CatAttr object

Builders

class AreaBuilder(*args, **kws)[source]

This is the Area builder and it is in charge of generating glyph renderers that together produce an area chart.

Essentially, we provide a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the ranges. And finally add the needed glyphs (markers) taking the references from the source.

glyph

alias of AreaGlyph

class BarBuilder(*args, **kws)[source]

This is the Bar builder and it is in charge of plotting Bar chart (grouped and stacked) in an easy and intuitive way.

Essentially, it utilizes a standardized way to ingest the data, make the proper calculations and generate renderers. The renderers reference the transformed data, which represent the groups of data that were derived from the inputs. We additionally make calculations for the ranges.

The x_range is categorical, and is made either from the label argument or from the pandas.DataFrame.index. The y_range can be supplied as the parameter continuous_range, or will be calculated as a linear range (Range1d) based on the supplied values.

The bar builder is and can be further used as a base class for other builders that might also be performing some aggregation across derived groups of data.

agg

property type: Enum ( Aggregation )

bar_width

property type: Float

fill_alpha

property type: Float

label_only

property type: Bool

max_height

property type: Float

min_height

property type: Float

values_only

property type: Bool

glyph

alias of BarGlyph

get_extra_args()[source]
set_ranges()[source]

Push the Bar data into the ColumnDataSource and calculate the proper ranges.

setup()[source]
yield_renderers()[source]

Use the rect glyphs to display the bars.

Takes reference points from data loaded at the ColumnDataSource.

default_attributes = {'line_color': ColorAttr(...), 'group': CatAttr(...), 'color': ColorAttr(...), 'label': CatAttr(...), 'stack': CatAttr(...)}
dimensions = ['values']
label_attributes = ['stack', 'group']
values = Dimension(...)
class BoxPlotBuilder(*args, **kws)[source]

Produces Box Glyphs for groups of data.

Handles box plot options to produce one to many boxes, which are used to describe the distribution of a variable.

marker

property type: String

The marker type to use (e.g., circle) if outliers=True.

outliers

property type: Bool

Whether to display markers for any outliers.

glyph

alias of BoxGlyph

setup()[source]
default_attributes = {'line_color': ColorAttr(...), 'whisker_color': ColorAttr(...), 'color': ColorAttr(...), 'outlier_line_color': ColorAttr(...), 'stack': CatAttr(...), 'group': CatAttr(...), 'label': CatAttr(...), 'outlier_fill_color': ColorAttr(...)}
class ChordBuilder(*args, **kws)[source]

This is the Chord builder and it is in charge of plotting Chord graphs in an easy and intuitive way.

Essentially, we provide a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the ranges. And finally add the needed glyphs (markers) taking the references from the source.

arcs_data

property type: Instance ( ColumnDataSource )

connection_data

property type: Instance ( ColumnDataSource )

destination

property type: String

label

property type: Seq ( Any )

matrix

property type: Array ( Array ( Either ( Float , Int ) ) )

origin

property type: String

square_matrix

property type: Bool

text_data

property type: Instance ( ColumnDataSource )

value

property type: Any

process_data()[source]
set_ranges()[source]
setup()[source]
yield_renderers()[source]

Use the marker glyphs to display the arcs and beziers. Takes reference points from data loaded at the ColumnDataSource.

default_attributes = {'marker': MarkerAttr(...), 'color': ColorAttr(...), 'stack': CatAttr(...)}
dimensions = ['values']
values = Dimension(...)
class DonutBuilder(*args, **kws)[source]

Produces layered donut for hierarchical groups of data.

Handles derivation of chart settings from inputs and assignment of attributes to each group of data.

agg

property type: String

chart_data

property type: Instance ( ColumnDataSource )

level_spacing

property type: Either ( Float , List ( Float ) )

level_width

property type: Float

line_color

property type: Color

text_data

property type: Instance ( ColumnDataSource )

text_font_size

property type: String

process_data()[source]
set_ranges()[source]
setup()[source]
yield_renderers()[source]
default_attributes = {'color': ColorAttr(...), 'label': CatAttr(...), 'stack': CatAttr(...)}
dimensions = ['values']
values = Dimension(...)
class HeatMapBuilder(*args, **kws)[source]

Assists in producing glyphs required to represent values by a glyph attribute.

Primary use case is to display the 3rd dimension of a value by binning and aggregating as needed and assigning the results to color. This color is represented on a glyph that is positioned by the x and y dimensions.

bin_height

property type: Float

A derived property that is used to size the glyph in height.

bin_width

property type: Float

A derived property that is used to size the glyph in width.

spacing_ratio

property type: Float

Multiplied by the bin height and width to shrink or grow the relative size of the glyphs. The closer to 0 this becomes, the amount of space between the glyphs will increase. When above 1.0, the glyphs will begin to overlap.

stat

property type: String

The stat to be applied to the values that fall into each x and y intersection. When stat is set to None, then no aggregation will occur.

process_data()[source]

Perform aggregation and binning as requried.

setup()[source]
yield_renderers()[source]

Generate a set fo bins for each group of data.

default_attributes = {'color': ColorAttr(...)}
dimensions = ['x', 'y', 'values']
req_dimensions = [['x', 'y'], ['x', 'y', 'values']]
values = Dimension(...)
class HistogramBuilder(*args, **kws)[source]

Generates one to many histograms with unique attributes.

The HistogramBuilder is responsible for producing a chart containing one to many histograms from table-like inputs.

bins

property type: Either ( List ( Float ), Int )

If bins is an int, it defines the number of equal-width bins in the given range. If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths.

(default: None, use Freedman-Diaconis rule)

density

property type: Bool

Whether to normalize the histogram.

If True, the result is the value of the probability density function at the bin, normalized such that the integral over the range is 1. If False, the result will contain the number of samples in each bin.

For more info check HistogramGlyph documentation.

(default: False)

glyph

alias of HistogramGlyph

get_extra_args()[source]

Build kwargs that are unique to the histogram builder.

set_ranges()[source]

Push the Bar data into the ColumnDataSource and calculate the proper ranges.

setup()[source]
class HorizonBuilder(*args, **kws)[source]

Produces glyph renderers representing a horizon chart from many input types.

The builder handles ingesting the data, deriving settings when not provided, building the renderers, then setting ranges, and modifying the chart as needed.

bins

property type: List ( Float )

The binedges calculated from the number of folds,
and the maximum value of the entire source data.
flip_neg

property type: Bool

When True, the negative values will be
plotted as their absolute value, then their individual axes is flipped. If False, then the negative values will still be taken as their absolute value, but the base of their shape will start from the same origin as the positive values.
fold_height

property type: Float

The size of the bin.

neg_color

property type: Color

The color of the negative folds. (default: “#6495ed”)

num_folds

property type: Int

The number of folds stacked on top of each other. (default: 3)

pos_color

property type: Color

The color of the positive folds. (default: “#006400”)

series_column

property type: String

The column that contains the series names.

series_count

property type: Int

Count of the unique series names.

glyph

alias of HorizonGlyph

process_data()[source]
set_ranges()[source]
setup()[source]
default_attributes = {'series': IdAttr(...), 'color': ColorAttr(...)}
class LineBuilder(*args, **kws)[source]

This is the Line class and it is in charge of plotting Line charts in an easy and intuitive way. Essentially, we provide a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the ranges. And finally add the needed lines taking the references from the source.

series_names

property type: List ( String )

Names that represent the items being plotted.

stack

property type: Bool

column_selector

alias of NumericalColumnsAssigner

glyph

alias of LineGlyph

attr_measurement(attr_name)[source]

Detect if the attribute has been given measurement columns.

get_builder_attr()[source]
get_id_cols(stack_flags)[source]
set_series(col_name)[source]
setup()[source]

Handle input options that require transforming data and/or user selections.

yield_renderers()[source]
default_attributes = {'marker': MarkerAttr(...), 'color': ColorAttr(...), 'dash': DashAttr(...)}
dimensions = ['y', 'x']
measure_input
measures
stack_flags
class ScatterBuilder(*args, **kws)[source]

This is the Scatter class and it is in charge of plotting Scatter charts in an easy and intuitive way.

Essentially, we provide a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the ranges. And finally add the needed glyphs (markers) taking the references from the source.

yield_renderers()[source]

Use the marker glyphs to display the points.

Takes reference points from data loaded at the ColumnDataSource.

default_attributes = {'marker': MarkerAttr(...), 'color': ColorAttr(...)}
class ScatterBuilder(*args, **kws)[source]

This is the Scatter class and it is in charge of plotting Scatter charts in an easy and intuitive way.

Essentially, we provide a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the ranges. And finally add the needed glyphs (markers) taking the references from the source.

yield_renderers()[source]

Use the marker glyphs to display the points.

Takes reference points from data loaded at the ColumnDataSource.

default_attributes = {'marker': MarkerAttr(...), 'color': ColorAttr(...)}
class StepBuilder(*args, **kws)[source]

This is the Step builder and it is in charge of plotting Step charts in an easy and intuitive way.

Essentially, we provide a way to ingest the data, make the proper calculations and push the references into a source object.

We additionally make calculations for the ranges, and finally add the needed stepped lines taking the references from the source.

yield_renderers()[source]

Helper Classes

Core Classes

class Chart(*args, **kwargs)[source]

The main Chart class, the core of the Bokeh.charts interface.

__init__(*args, **kwargs)[source]
xlabel

property type: String

A label for the x-axis. (default: None)

xscale

property type: Either ( Auto , Enum ( Enumeration(linear, categorical, datetime) ) )

What kind of scale to use for the x-axis.

ylabel

property type: String

A label for the y-axis. (default: None)

yscale

property type: Either ( Auto , Enum ( Enumeration(linear, categorical, datetime) ) )

What kind of scale to use for the y-axis.

add_builder(builder)[source]
add_labels(dim, label)[source]
add_legend(chart_legends)[source]

Add the legend to your plot, and the plot to a new Document.

It also add the Document to a new Session in the case of server output.

Parameters:legends (List(Tuple(String, List(GlyphRenderer)) – A list of tuples that maps text labels to the legend to corresponding renderers that should draw sample representations for those labels.
add_ranges(dim, range)[source]
add_renderers(builder, renderers)[source]
add_scales(dim, scale)[source]
add_tooltips(tooltips)[source]
annular_wedge(x, y, inner_radius, outer_radius, start_angle, end_angle, direction='anticlock', **kwargs)

Configure and add annularwedge glyphs to this Figure.

Parameters:
Keyword Arguments:
 
  • end_angle_units (Enum('deg', 'rad')) – (default ‘rad’)
  • fill_alpha (DataSpecPropertyDescriptor) – The fill alpha values for the annular wedges. (default 1.0)
  • fill_color (DataSpecPropertyDescriptor) – The fill color values for the annular wedges. (default ‘gray’)
  • inner_radius_units (Enum('screen', 'data')) – (default ‘data’)
  • js_callbacks (BasicPropertyDescriptor) – A mapping of attribute names to lists of CustomJS callbacks, to be set up on BokehJS side when the document is created. (default {})
  • line_alpha (DataSpecPropertyDescriptor) – The line alpha values for the annular wedges. (default 1.0)
  • line_cap (BasicPropertyDescriptor) – The line cap values for the annular wedges. (default ‘butt’)
  • line_color (DataSpecPropertyDescriptor) – The line color values for the annular wedges. (default ‘black’)
  • line_dash (BasicPropertyDescriptor) – The line dash values for the annular wedges. (default [])
  • line_dash_offset (BasicPropertyDescriptor) – The line dash offset values for the annular wedges. (default 0)
  • line_join (BasicPropertyDescriptor) – The line join values for the annular wedges. (default ‘miter’)
  • line_width (DataSpecPropertyDescriptor) – The line width values for the annular wedges. (default 1)
  • name (BasicPropertyDescriptor) – An arbitrary, user-supplied name for this model. (default None)
  • outer_radius_units (Enum('screen', 'data')) – (default ‘data’)
  • start_angle_units (Enum('deg', 'rad')) – (default ‘rad’)
  • tags (BasicPropertyDescriptor) – An optional list of arbitrary, user-supplied values to attach to this model. (default [])
  • visible (BasicPropertyDescriptor) – Whether the glyph should render or not. (default True)
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
annulus(x, y, inner_radius, outer_radius, **kwargs)

Configure and add annulus glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
arc(x, y, radius, start_angle, end_angle, direction='anticlock', **kwargs)

Configure and add arc glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
asterisk(x, y, size=4, angle=0.0, **kwargs)

Configure and add asterisk glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
bezier(x0, y0, x1, y1, cx0, cy0, cx1, cy1, **kwargs)

Configure and add bezier glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
circle(x, y, **kwargs)

Configure and add circle glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
circle_cross(x, y, size=4, angle=0.0, **kwargs)

Configure and add circlecross glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
circle_x(x, y, size=4, angle=0.0, **kwargs)

Configure and add circlex glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
create_axes()[source]
create_grids(xgrid=True, ygrid=True)[source]
create_tools(tools, active_drag, active_scroll, active_tap)[source]

Create tools if given tools=True input.

Only adds tools if given boolean and does not already have tools added to self.

cross(x, y, size=4, angle=0.0, **kwargs)

Configure and add cross glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
diamond(x, y, size=4, angle=0.0, **kwargs)

Configure and add diamond glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
diamond_cross(x, y, size=4, angle=0.0, **kwargs)

Configure and add diamondcross glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
ellipse(x, y, width, height, angle=0.0, **kwargs)

Configure and add ellipse glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
get_class(view_model_name)

Given a __view_model__ name, returns the corresponding class object

hbar(y, height, right, left=0, **kwargs)

Configure and add hbar glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
image(image, x, y, dw, dh, dilate=False, **kwargs)

Configure and add image glyphs to this Figure.

Parameters:
Keyword Arguments:
 
  • color_mapper (BasicPropertyDescriptor) – A ColorMapper to use to map the scalar data from image into RGBA values for display. (default None)
  • dh_units (Enum('screen', 'data')) – (default ‘data’)
  • dw_units (Enum('screen', 'data')) – (default ‘data’)
  • js_callbacks (BasicPropertyDescriptor) – A mapping of attribute names to lists of CustomJS callbacks, to be set up on BokehJS side when the document is created. (default {})
  • name (BasicPropertyDescriptor) – An arbitrary, user-supplied name for this model. (default None)
  • palette (str or list[color value]) – a palette to construct a value for the color mapper property from
  • tags (BasicPropertyDescriptor) – An optional list of arbitrary, user-supplied values to attach to this model. (default [])
  • visible (BasicPropertyDescriptor) – Whether the glyph should render or not. (default True)
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
image_rgba(image, x, y, dw, dh, dilate=False, **kwargs)

Configure and add imagergba glyphs to this Figure.

Parameters:
Keyword Arguments:
 
  • cols (DataSpecPropertyDescriptor) – The numbers of columns in the images (default None)
  • dh_units (Enum('screen', 'data')) – (default ‘data’)
  • dw_units (Enum('screen', 'data')) – (default ‘data’)
  • js_callbacks (BasicPropertyDescriptor) – A mapping of attribute names to lists of CustomJS callbacks, to be set up on BokehJS side when the document is created. (default {})
  • name (BasicPropertyDescriptor) – An arbitrary, user-supplied name for this model. (default None)
  • rows (DataSpecPropertyDescriptor) – The numbers of rows in the images (default None)
  • tags (BasicPropertyDescriptor) – An optional list of arbitrary, user-supplied values to attach to this model. (default [])
  • visible (BasicPropertyDescriptor) – Whether the glyph should render or not. (default True)
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
image_url(url, x, y, w, h, angle=0, global_alpha=1.0, dilate=False, **kwargs)

Configure and add imageurl glyphs to this Figure.

Parameters:
Keyword Arguments:
 
  • anchor (BasicPropertyDescriptor) – What position of the image should be anchored at the x, y coordinates. (default ‘top_left’)
  • angle_units (Enum('deg', 'rad')) – (default ‘rad’)
  • h_units (Enum('screen', 'data')) – (default ‘data’)
  • js_callbacks (BasicPropertyDescriptor) – A mapping of attribute names to lists of CustomJS callbacks, to be set up on BokehJS side when the document is created. (default {})
  • name (BasicPropertyDescriptor) – An arbitrary, user-supplied name for this model. (default None)
  • retry_attempts (BasicPropertyDescriptor) – Number of attempts to retry loading the images from the specified URL. Default is zero. (default 0)
  • retry_timeout (BasicPropertyDescriptor) – Timeout (in ms) between retry attempts to load the image from the specified URL. Default is zero ms. (default 0)
  • tags (BasicPropertyDescriptor) – An optional list of arbitrary, user-supplied values to attach to this model. (default [])
  • visible (BasicPropertyDescriptor) – Whether the glyph should render or not. (default True)
  • w_units (Enum('screen', 'data')) – (default ‘data’)
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
inverted_triangle(x, y, size=4, angle=0.0, **kwargs)

Configure and add invertedtriangle glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
line(x, y, **kwargs)

Configure and add line glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
make_axis(dim, location, scale, label)[source]

Create linear, date or categorical axis depending on the location, scale and with the proper labels.

Parameters:
  • location (str) – the space localization of the axis. It can be left, right, above or below.
  • scale (str) – the scale on the axis. It can be linear, datetime or categorical.
  • label (str) – the label on the axis.
Returns:

Axis instance
Return type:

axis
make_grid(dimension, ticker)[source]

Create the grid just passing the axis and dimension.

Parameters:
  • dimension (int) – the dimension of the axis, ie. xaxis=0, yaxis=1.
  • ticker (obj) – the axis.ticker object
Returns:

Grid instance
Return type:

grid
multi_line(xs, ys, **kwargs)

Configure and add multiline glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
oval(x, y, width, height, angle=0.0, **kwargs)

Configure and add oval glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
patch(x, y, **kwargs)

Configure and add patch glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
patches(xs, ys, **kwargs)

Configure and add patches glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
quad(left, right, top, bottom, **kwargs)

Configure and add quad glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
quadratic(x0, y0, x1, y1, cx, cy, **kwargs)

Configure and add quadratic glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
ray(x, y, length, angle, **kwargs)

Configure and add ray glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
rect(x, y, width, height, angle=0.0, dilate=False, **kwargs)

Configure and add rect glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
segment(x0, y0, x1, y1, **kwargs)

Configure and add segment glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
square(x, y, size=4, angle=0.0, **kwargs)

Configure and add square glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
square_cross(x, y, size=4, angle=0.0, **kwargs)

Configure and add squarecross glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
square_x(x, y, size=4, angle=0.0, **kwargs)

Configure and add squarex glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
start_plot()[source]

Add the axis, grids and tools

text(x, y, text='text', angle=0, x_offset=0, y_offset=0, **kwargs)

Configure and add text glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
triangle(x, y, size=4, angle=0.0, **kwargs)

Configure and add triangle glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
vbar(x, width, top, bottom=0, **kwargs)

Configure and add vbar glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
wedge(x, y, radius, start_angle, end_angle, direction='anticlock', **kwargs)

Configure and add wedge glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
x(x, y, size=4, angle=0.0, **kwargs)

Configure and add x glyphs to this Figure.

Parameters:
Keyword Arguments:
 
Other Parameters:
 
  • alpha (float) – an alias to set all alpha keyword args at once
  • color (Color) – an alias to set all color keyword args at once
  • source (ColumnDataSource) – a user supplied data source
  • legend (str) – a legend tag for this glyph
  • x_range_name (str) – name an extra range to use for mapping x-coordinates
  • y_range_name (str) – name an extra range to use for mapping y-coordinates
  • level (Enum) – control the render level order for this glyph

It is also possible to set the color and alpha parameters of a “nonselection” glyph. To do so, prefix any visual parameter with 'nonselection_'. For example, pass nonselection_alpha or nonselection_fill_alpha.

Returns:GlyphRenderer
legend

Splattable list of Legend objects.

xgrid

Splattable list of Grid objects for the x dimension.

ygrid

Splattable list of Grid objects for the y dimension.

class Builder(*args, **kws)[source]

A prototype class to inherit each new chart Builder type.

It provides useful methods to be used by the inherited builder classes, in order to automate most of the charts creation tasks and leave the core customization to specialized builder classes. In that pattern inherited builders just need to provide the following methods:

Required:

  • yield_renderers(): yields the glyphs to be rendered into the plot. Here you should call the add_glyph() method so that the builder can setup the legend for you.
  • set_ranges(): setup the ranges for the glyphs. This is called after glyph creation, so you are able to inspect the comp_glyphs for their minimum and maximum values. See the create() method for more information on when this is called and how the builder provides the ranges to the containing Chart using the Chart.add_ranges() method.

Optional:

  • setup(): provides an area where subclasses of builder can introspect properties, setup attributes, or change property values. This is called before process_data().
  • process_data(): provides an area where subclasses of builder can manipulate the source data before renderers are created.
__init__(*args, **kws)[source]

Common arguments to be used by all the inherited classes.

Parameters:
  • data (Accepted Charts Data Formats) – source data for the chart
  • legend (str, bool) – the legend of your plot. The legend content is inferred from incoming input.It can be top_left, top_right, bottom_left, bottom_right. It is top_right is you set it as True.
source

obj – datasource object for your plot, initialized as a dummy None.

x_range

obj – x-associated datarange object for you plot, initialized as a dummy None.

y_range

obj – y-associated datarange object for you plot, initialized as a dummy None.

groups

list – to be filled with the incoming groups of data. Useful for legend construction.

data

dict – to be filled with the incoming data and be passed to the ChartDataSource for each Builder class.

attr

list(AttrSpec) – to be filled with the new attributes created after loading the data dict.

attribute_columns

property type: List ( ColumnLabel ( String , Int ) )

All columns used for specifying attributes for the Chart. The Builder will set this value on creation so that the subclasses can know the distinct set of columns that are being used to assign attributes.

attributes

property type: Dict ( String , Instance ( AttrSpec ) )

The attribute specs used to group data. This is a mapping between the role of the attribute spec (e.g. ‘color’) and the AttrSpec class (e.g., ColorAttr). The Builder will use this attributes property during runtime, which will consist of any attribute specs that are passed into the chart creation function (e.g., Bar), ones that are created for the user from simple input types (e.g. Bar(..., color=’red’) or Bar(..., color=<column_name>)), or lastly, the attribute spec found in the default_attributes configured for the subclass of Builder.

comp_glyph_types

property type: List ( Instance ( CompositeGlyph ) )

comp_glyphs

property type: List ( Instance ( CompositeGlyph ) )

A list of composite glyphs, where each represents a unique subset of data. The composite glyph is a helper class that encapsulates all low level Glyph, that represent a higher level group of data. For example, the BoxGlyph is a single class that yields each GlyphRenderer needed to produce a Box on a BoxPlot. The single Box represents a full array of values that are aggregated, and is made up of multiple Rect and Segment glyphs.

labels

property type: List ( String )

Represents the unique labels to be used for legends.

legend_sort_direction

property type: Enum ( SortDirection )

Sort direction to apply to sort_legend. Valid values are: ascending or descending.

legend_sort_field

property type: String

Attribute that should be used to sort the legend, for example: color, dash, maker, etc. Valid values for this property depend on the type of chart.

palette

property type: List ( Color )

Optional input to override the default palette used
by any color attribute.
sort_dim

property type: Dict ( String , Bool )

source

property type: Instance ( ColumnDataSource )

tooltips

property type: Either ( List ( Tuple ( String , String ) ), List ( String ), Bool )

Tells the builder to add tooltips to the chart by either using the columns specified to the chart attributes (True), or by generating tooltips for each column specified (list(str)), or by explicit specification of the tooltips using the valid input for the HoverTool tooltips kwarg.

x_range

property type: Instance ( Range )

xlabel

property type: String

xscale

property type: String

y_range

property type: Instance ( Range )

ylabel

property type: String

yscale

property type: String

column_selector

alias of OrderedAssigner

add_glyph(group, glyph)[source]

Add a composite glyph.

Manages the legend, since the builder might not want all attribute types used for the legend.

Parameters:
  • group (DataGroup) – the data the glyph is associated with
  • glyph (CompositeGlyph) – the glyph associated with the group
Returns:

None
collect_attr_kwargs()[source]
create(chart=None)[source]

Builds the renderers, adding them and other components to the chart.

Parameters:chart (Chart, optional) – the chart that will contain the glyph renderers that the Builder produces.
Returns:Chart
classmethod generate_help()[source]
get_dim_extents()[source]

Helper method to retrieve maximum extents of all the renderers.

Returns:a dict mapping between dimension and value for x_max, y_max, x_min, y_min
get_group_kwargs(group, attrs)[source]
process_data()[source]

Make any global data manipulations before grouping.

It has to be implemented by any of the inherited class representing each different chart type. It is the place where we make specific calculations for each chart.

Returns:None
set_ranges()[source]

Calculate and set the x and y ranges.

It has to be implemented by any of the subclasses of builder representing each different chart type, and is called after yield_renderers().

Returns:None
setup()[source]

Perform any initial pre-processing, attribute config.

Returns:None
yield_renderers()[source]

Generator that yields the glyphs to be draw on the plot

It has to be implemented by any of the inherited class representing each different chart type.

Yields:GlyphRenderer
default_attributes = None
dimensions = None

The dimension labels that must exist to produce the glyphs. This specifies what are the valid configurations for the chart, with the option of specifying the type of the columns. The ChartDataSource will inspect this property of your subclass of Builder and use this to fill in any required dimensions if no keyword arguments are used.

label_attributes = []

Used to assign columns to dimensions when no selections have been provided. The default behavior is provided by the OrderedAssigner, which assigns a single column to each dimension available in the Builder‘s dims property.

req_dimensions = []
sort_legend
class ChartDataSource(df, dims=None, required_dims=None, selections=None, column_assigner=<class 'bokeh.charts.data_source.OrderedAssigner'>, attrs=None, **kwargs)[source]

Validates, normalizes, groups, and assigns Chart attributes to groups.

Supported inputs are:

Converts inputs that could be treated as table-like data to pandas DataFrame, which is used for assigning attributes to data groups.

__init__(df, dims=None, required_dims=None, selections=None, column_assigner=<class 'bokeh.charts.data_source.OrderedAssigner'>, attrs=None, **kwargs)[source]

Create a ChartDataSource.

Parameters:
  • df (pandas.DataFrame) – the original data source for the chart
  • dims (List(Str), optional) – list of valid dimensions for the chart.
  • required_dims (List(List(Str)), optional) – list of list of valid dimensional selections for the chart.
  • selections (Dict(String, List(Column)), optional) – mapping between a dimension and the column name(s) associated with it. This represents what the user selected for the current chart.
  • column_assigner (ColumnAssigner, optional) – a reference to a ColumnAssigner class, which is used to collect dimension column assignment when keyword arguments aren’t provided. The default value is OrderedAssigner, which assumes you want to assign each column or array to each dimension of the chart in order that they are received.
  • attrs (list(str)) – list of attribute names the chart uses
apply_operations()[source]

Applies each data operation.

static collect_metadata(data)[source]

Introspect which columns match to which types of data.

classmethod from_arrays(arrays, column_names=None, **kwargs)[source]

Produce ColumnDataSource from array-like data.

Returns:ColumnDataSource
classmethod from_data(*args, **kwargs)[source]

Automatically handle all valid inputs.

Attempts to use any data that can be represented in a Table-like format, along with any generated requirements, to produce a ChartDataSource. Internally, these data types are generated, so that a pandas.DataFrame can be generated.

Identifies inputs that are array vs table like, handling them accordingly. If possible, existing column names are used, otherwise column names are generated.

Returns:ColumnDataSource
classmethod from_dict(data, **kwargs)[source]

Produce ColumnDataSource from table-like dict.

Returns:ColumnDataSource
get_selections(selections, **kwargs)[source]

Maps chart dimensions to selections and checks input requirements.

Returns:mapping between each dimension and the selected columns. If no selection is made for a dimension, then the dimension will be associated with None.
groupby(**specs)[source]

Iterable of chart attribute specifications, associated with columns.

Iterates over DataGroup, which represent the lowest level of data that is assigned to the attributes for plotting.

Yields:a DataGroup, which contains metadata and attributes assigned to the group of data
static is_array(data)[source]

Verify if data is array-like.

Returns:bool
static is_computed(column)[source]

Verify if the column provided matches to known computed columns.

Returns:bool
static is_datetime(value)[source]

Verifies that value is a valid Datetime type, or can be converted to it.

Returns:bool
static is_list_arrays(data)[source]

Verify if input data is a list of array-like data.

Returns:bool
static is_list_dicts(data)[source]

Verify if data is row-oriented, table-like data.

Returns:bool
static is_number(value)[source]

Verifies that value is a numerical type.

Returns:bool
static is_table(data)[source]

Verify if data is table-like.

Inspects the types and structure of data.

Returns:bool
join_attrs(**attr_specs)[source]

Produce new DataFrame from source data and AttrSpec provided.

Parameters:**attr_specs (str, AttrSpec, optional) – pairs of names and attribute spec objects. This is optional and not required only if the ChartDataSource already contains references to the attribute specs.
Returns:
a new dataframe that includes a column for each of the
attribute specs joined in, plus one special column called chart_index, which contains the unique items between the different attribute specs.
Return type:pd.DataFrame
setup_derived_columns()[source]

Attempt to add special case columns to the DataFrame for the builder.

stack_measures(measures, ids=None, var_name='variable', value_name='value')[source]

De-pivots _data from a ‘wide’ to ‘tall’ layout.

A wide table is one where the column names represent a categorical variable and each contains only the values associated with each unique value of the categorical variable.

This method uses the pandas.melt() function with additional logic to make sure that the same data source can have multiple operations applied, and so all other columns are maintained through the stacking process.

Example

Note

This example is fairly low level and is not something the typical user should worry about. The interface for data transformations from the user perspective are the Chart Functions.

>>> data = {'a': [1, 2, 3, 4],
...         'b': [2, 3, 4, 5],
...         'month': ['jan', 'jan', 'feb', 'feb']
...         }

>>> ds = ChartDataSource.from_data(data)
>>> ds['x'] =['a', 'b'] # say we selected a and b for dimension x

We may want to combine ‘a’ and ‘b’ together. The final data would look like the following:

>>> ds.stack_measures(['c', 'd'], var_name='c_d_variable',
...                   value_name='c_d_value')
>>> ds.df
Out[35]:
      month a_b_variable  a_b_value
    0   jan            a          1
    1   jan            a          2
    2   feb            a          3
    3   feb            a          4
    4   jan            b          2
    5   jan            b          3
    6   feb            b          4
    7   feb            b          5

The transformed data will use the var_name and value_name inputs to name the columns. These derived columns can then be used as a single column to reference the values and the labels of the data. In the example, I could plot a_b_value vs month, and color by a_b_variable.

What this does for you over the pandas.melt() method is that it will apply the DataOperator for a dimension if it exists (e.g. Blend, generated by blend()), and it will try to handle the id columns for you so you don’t lose other columns with the melt transformation.

Returns:None
attr_specs
columns

All column names associated with the data.

Returns:List(Str)
df
index

The index for the pandas.DataFrame data source.

source
values
class DataGroup(label, data, attr_specs)[source]

Contains subset of data and metadata about it.

The DataGroup contains a map from the labels of each attribute associated with an AttrSpec to the value of the attribute assigned to the DataGroup.

__init__(label, data, attr_specs)[source]

Create a DataGroup for the data, with a label and associated attributes.

Parameters:
  • label (str) – the label for the group based on unique values of each column
  • data (pandas.DataFrame) – the subset of data associated with the group
  • dict (attr_specs) – mapping between attribute name and the associated AttrSpec.
get_values(selection)[source]

Get the data associated with the selection of columns.

Parameters:selection (List(Str) or Str) – the column or columns selected
Returns:pandas.DataFrame
to_dict()[source]
attributes
source

The ColumnDataSource representation of the DataFrame.

class CompositeGlyph(**properties)[source]

Represents a subset of data.

A collection of hetero or homogeneous glyph renderers which represent a subset of data. The purpose of the composite glyph is to abstract away the details of constructing glyphs, based on the details of a subset of data, from the grouping operations that a generalized builders must implement.

In general, the Builder operates at the full column oriented data source level, segmenting and assigning attributes from a large selection, while the composite glyphs will typically be passed an array-like structures with one or more singular attributes to apply.

Another way to explain the concept is that the Builder operates as the groupby, as in pandas, while the CompositeGlyph operates as the function used in the apply.

What is the responsibility of the Composite Glyph?

  • Produce GlyphRenderers

  • Apply any aggregations

  • Tag the GlyphRenderers with the group label

  • Apply transforms due to chart operations
    • Note: Operations require implementation of special methods
__init__(**properties)[source]
bottom_buffer

property type: Float

color

property type: Color

A high level color. Some glyphs will
implement more specific color attributes for parts or specific glyphs.
fill_alpha

property type: Float

fill_color

property type: Color

glyphs

property type: Dict ( String , Any )

label

property type: Either ( String , Dict ( String , Any ) )

Identifies the subset of data.

left_buffer

property type: Float

line_alpha

property type: Float

line_color

property type: Color

A default outline color for contained
glyphs.
operations

property type: List ( Any )

A list of chart operations that can be applied to
manipulate their visual depiction.
renderers

property type: List ( Instance ( GlyphRenderer ) )

right_buffer

property type: Float

source

property type: Instance ( ColumnDataSource )

The data source used for the contained
glyph renderers. Simple glyphs part of the composite glyph might not use the column data source.
top_buffer

property type: Float

values

property type: Either ( Column ( Float ), Column ( String ) )

Array-like values, which are used as the input to the composite glyph.

Most composite glyphs add their own representation of one or more values-like columns/arrays that they receive as inputs. These are compiled together for generating source, data, and df by the individual composite glyphs.

add_chart_index(data)[source]

Add identifier of the data group as a column for each row.

Parameters:data (dict or ColumnDataSource) – can be the type of data used internally to ColumnDataSource, or a ColumnDataSource.
Returns:returns the same type of data provided
Return type:dict or ColumnDataSource
apply_operations()[source]
build_renderers()[source]
build_source()[source]
classmethod glyph_properties()[source]
refresh()[source]

Update the GlyphRenderers.

setup()[source]

Build renderers and data source and set sources on renderers.

data
df

Attribute Specs

class AttrSpec(columns=None, df=None, iterable=None, default=None, items=None, **properties)[source]

A container for assigning attributes to values and retrieving them as needed.

A special function this provides is automatically handling cases where the provided iterator is too short compared to the distinct values provided.

Once created as attr_spec, you can do attr_spec[data_label], where data_label must be a one dimensional tuple of values, representing the unique group in the data.

See the AttrSpec.setup() method for the primary way to provide an existing AttrSpec with data and column values and update all derived property values.

__init__(columns=None, df=None, iterable=None, default=None, items=None, **properties)[source]

Create a lazy evaluated attribute specification.

Parameters:
  • columns – a list of column labels
  • df (DataFrame) – the data source for the attribute spec.
  • iterable – an iterable of distinct attribute values
  • default – a value to use as the default attribute when no columns are passed
  • items – the distinct values in columns. If items is provided as input, then the values provided are used instead of being calculated. This can be used to force a specific order for assignment.
  • **properties – other properties to pass to parent HasProps
ascending

property type: Bool

A boolean flag to tell the attribute specification how to sort items if the sort property is set to True. The default setting for ascending is True.

attr_map

property type: Dict ( Any , Any )

Created by the attribute specification when iterable and data are available. The attr_map will include a mapping between the distinct value(s) found in columns and the attribute value that has been assigned.

attrname

property type: String

Name of the attribute the spec provides.

bins

property type: Instance ( Bins )

If an attribute spec is binning data, so that we can map one value in the iterable to one value in items, then this attribute will contain an instance of the Bins stat. This is used to create unique labels for each bin, which is then used for items instead of the actual unique values in columns.

columns

property type: Either ( ColumnLabel ( String , Int ), List ( ColumnLabel ( String , Int ) ) )

The label or list of column labels that correspond to the columns that will be used to find all distinct values (single column) or combination of values ( multiple columns) to then assign a unique attribute to. If not enough unique attribute values are found, then the attribute values will be cycled.

data

property type: Instance ( ColumnDataSource )

default

property type: Any

The default value for the attribute, which is used if no column is assigned to the attribute for plotting. If the default value is not provided, the first value in the iterable property is used.

items

property type: Any

The attribute specification calculates this list of distinct values that are found in columns of data.

iterable

property type: List ( Any )

sort

property type: Bool

A boolean flag to tell the attribute specification to sort items, when it is calculated. This affects which value of iterable is assigned to each distinct value in items.

set_columns(columns)[source]

Set columns property and update derived properties as needed.

setup(data=None, columns=None)[source]

Set the data and update derived properties as needed.

update_data(data)[source]
series
class ColorAttr(**kwargs)[source]

An attribute specification for mapping unique data values to colors.

Note

Should be expanded to support more complex coloring options.

bin

property type: Bool

add_bin_labels(data)[source]
class MarkerAttr(**kwargs)[source]

An attribute specification for mapping unique data values to markers.

class DashAttr(**kwargs)[source]

An attribute specification for mapping unique data values to line dashes.

class CatAttr(**kwargs)[source]

An attribute specification for mapping unique data values to labels.

Note

this is a special attribute specification, which is used for defining which labels are used for one aspect of a chart (grouping) vs another (stacking or legend)

get_levels(columns)[source]

Provides a list of levels the attribute represents.

Utilities

df_from_json(data, rename=True, **kwargs)[source]

Attempt to produce pandas.DataFrame from hierarchical json-like data.

This utility wraps the pandas.io.json.json_normalize() function and by default will try to rename the columns produced by it.

Parameters:
  • data (str or list(dict) or dict(list(dict))) – a path to json data or loaded json data. This function will look into the data and try to parse it correctly based on common structures of json data.
  • (bool, optional (rename) – try to rename column hierarchy to the base name. So medals.bronze would end up being bronze. This will only rename to the base column name if the name is unique, and only if the pandas json parser produced columns that have a ‘.’ in the column name.
  • **kwargs – any kwarg supported by pandas.io.json.json_normalize()
Returns:

a parsed pandas dataframe from the json data, unless the path does not exist,

the input data is nether a list or dict. In that case, it will return None.