def Chord(data,
source=None,
target=None,
value=None,
square_matrix=False,
label=None,
**kws):
""" Create a chord chart using :class:`ChordBuilder <bokeh.charts.builders.chord_builder.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 source
Reference: `Chord diagram on Wikipedia <https://en.wikipedia.org/wiki/Chord_diagram>`_
Args:
data (:ref:`userguide_charts_data_types`): 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 or num) optional): The labels to be put in the areas.
Returns:
:class:`Chart`: includes glyph renderers that generate the chord
Examples:
.. bokeh-plot::
:source-position: above
import pandas as pd
from bokeh.charts import output_file, Chord
from bokeh.io import show
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]
chord_from_df = Chord(source_data, source="name_x", target="name_y", value="value")
output_file('chord_from_df.html', mode="inline")
show(chord_from_df)
"""
kws["origin"] = source
kws["destination"] = target
kws["value"] = value
kws["square_matrix"] = square_matrix
kws["label"] = label
return create_and_build(ChordBuilder, data, **kws)
def Scatter(data=None, x=None, y=None, **kws):
""" Create a scatter chart using :class:`ScatterBuilder <bokeh.charts.builders.scatter_builder.ScatterBuilder>`
to render the geometry from values.
Args:
data (:ref:`userguide_charts_data_types`): table-like data
x (str or list(str), optional): the column label to use for the x dimension
y (str or list(str), optional): the column label to use for the y dimension
In addition to the parameters specific to this chart,
:ref:`userguide_charts_defaults` are also accepted as keyword parameters.
Returns:
:class:`Chart`: includes glyph renderers that generate the scatter points
Examples:
.. bokeh-plot::
:source-position: above
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)
"""
kws['x'] = x
kws['y'] = y
return create_and_build(ScatterBuilder, data, **kws)
def Horizon(data=None, x=None, y=None, series=None, **kws):
""" Create a scatter chart using :class:`ScatterBuilder <bokeh.charts.builders.scatter_builder.ScatterBuilder>`
to render the geometry from values.
Args:
data (:ref:`userguide_charts_data_types`): table-like data
x (str or list(str), optional): the column label to use for the x dimension
y (str or list(str), optional): the column label to use for the y dimension
In addition the the parameters specific to this chart,
:ref:`userguide_charts_defaults` are also accepted as keyword parameters.
Returns:
:class:`Chart`: includes glyph renderers that generate the scatter points
Examples:
.. bokeh-plot::
:source-position: above
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)
"""
kws['x'] = x
kws['y'] = y
kws['series'] = series
tools = kws.get('tools', True)
if tools == True:
tools = "save,resize,reset"
elif isinstance(tools, string_types):
tools = tools.replace('pan', '')
tools = tools.replace('wheel_zoom', '')
tools = tools.replace('box_zoom', '')
tools = tools.replace(',,', ',')
kws['tools'] = tools
chart = create_and_build(HorizonBuilder, data, **kws)
# Hide numerical axis
chart.left[0].visible = False
# Add the series names to the y axis
chart.extra_y_ranges = {
"series": FactorRange(factors=chart._builders[0].series_names)
}
chart.add_layout(CategoricalAxis(y_range_name="series"), 'left')
return chart
def Chord(data, source=None, target=None, value=None, square_matrix=False, label=None, **kws):
""" Create a chord chart using :class:`ChordBuilder <bokeh.charts.builders.chord_builder.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 source
Reference: `Chord diagram on Wikipedia <https://en.wikipedia.org/wiki/Chord_diagram>`_
Args:
data (:ref:`userguide_charts_data_types`): 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 or num) optional): The labels to be put in the areas.
Returns:
:class:`Chart`: includes glyph renderers that generate the chord
Examples:
.. bokeh-plot::
:source-position: above
import pandas as pd
from bokeh.charts import output_file, Chord
from bokeh.io import show
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]
chord_from_df = Chord(source_data, source="name_x", target="name_y", value="value")
output_file('chord_from_df.html', mode="inline")
show(chord_from_df)
"""
kws["origin"] = source
kws["destination"] = target
kws["value"] = value
kws["square_matrix"] = square_matrix
kws["label"] = label
return create_and_build(ChordBuilder, data, **kws)
def Horizon(data=None, x=None, y=None, series=None, **kws):
""" Create a scatter chart using :class:`ScatterBuilder <bokeh.charts.builders.scatter_builder.ScatterBuilder>`
to render the geometry from values.
Args:
data (:ref:`userguide_charts_data_types`): table-like data
x (str or list(str), optional): the column label to use for the x dimension
y (str or list(str), optional): the column label to use for the y dimension
In addition the the parameters specific to this chart,
:ref:`userguide_charts_defaults` are also accepted as keyword parameters.
Returns:
:class:`Chart`: includes glyph renderers that generate the scatter points
Examples:
.. bokeh-plot::
:source-position: above
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)
"""
kws['x'] = x
kws['y'] = y
kws['series'] = series
tools = kws.get('tools', True)
if tools == True:
tools = "save,resize,reset"
elif isinstance(tools, string_types):
tools = tools.replace('pan', '')
tools = tools.replace('wheel_zoom', '')
tools = tools.replace('box_zoom', '')
tools = tools.replace(',,', ',')
kws['tools'] = tools
chart = create_and_build(HorizonBuilder, data, **kws)
# Hide numerical axis
chart.left[0].visible = False
# Add the series names to the y axis
chart.extra_y_ranges = {"series": FactorRange(factors=chart._builders[0].series_names)}
chart.add_layout(CategoricalAxis(y_range_name="series"), 'left')
return chart
def Area(data=None, x=None, y=None, **kws):
""" Create an area chart using :class:`AreaBuilder
<bokeh.charts.builders.area_builder.AreaBuilder>` to render the
geometry from values.
Args:
data (:ref:`userguide_charts_data_types`): table-like data
x (str or list(str), optional): the column label to use for the x dimension
y (str or list(str), optional): the column label to use for the y dimension
In addition the the parameters specific to this chart,
:ref:`userguide_charts_defaults` are also accepted as keyword parameters.
Returns:
:class:`Chart`: includes glyph renderers that generate the area glyphs
Examples:
.. bokeh-plot::
:source-position: above
from bokeh.charts import Area, show, vplot, 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)
"""
kws['x'] = x
kws['y'] = y
return create_and_build(AreaBuilder, data, **kws)
def Horizon(data=None, x=None, y=None, series=None, **kws):
""" Create a horizon chart using :class:`HorizonBuilder
<bokeh.charts.builders.scatter_builder.HorizonBuilder>`
to render the geometry from values.
Args:
data (:ref:`userguide_charts_data_types`): table-like data
x (str or list(str), optional): the column label to use for the x dimension
y (str or list(str), optional): the column label to use for the y dimension
In addition to the parameters specific to this chart,
:ref:`userguide_charts_defaults` are also accepted as keyword parameters.
Returns:
:class:`Chart`: includes glyph renderers that generate the scatter points
Examples:
.. bokeh-plot::
:source-position: above
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)
"""
kws['x'] = x
kws['y'] = y
kws['series'] = series
tools = kws.get('tools', True)
if tools == True:
tools = "save,resize,reset"
elif isinstance(tools, string_types):
tools = tools.replace('pan', '')
tools = tools.replace('wheel_zoom', '')
tools = tools.replace('box_zoom', '')
tools = tools.replace(',,', ',')
kws['tools'] = tools
chart = create_and_build(HorizonBuilder, data, **kws)
# Hide numerical axis
chart.left[0].visible = False
# Add the series names to the y axis
chart.extra_y_ranges = {"series": FactorRange(factors=chart._builders[0].series_names)}
chart.add_layout(CategoricalAxis(y_range_name="series"), 'left')
return chart
def Chord(data, source=None, target=None, value=None, square_matrix=False, label=None, xgrid=False, ygrid=False, **kw):
"""
Create a chord chart using :class:`ChordBuilder <bokeh.charts.builders.chord_builder.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 :class:`DataFrame <pandas.DataFrame>`,
select two columns to be used as arcs with `source` and `target` attributes, passing by the name of those columns.
The :class:`Chord <bokeh.charts.builders.chord_builder.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 :class:`DataFrame <pandas.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 <https://en.wikipedia.org/wiki/Chord_diagram>`_
Args:
data (:ref:`userguide_charts_data_types`): 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:
:class:`Chart`: includes glyph renderers that generate the chord
Examples:
.. bokeh-plot::
:source-position: above
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)
"""
kw["origin"] = source
kw["destination"] = target
kw["value"] = value
kw["square_matrix"] = square_matrix
kw["label"] = label
kw['xgrid'] = xgrid
kw['ygrid'] = ygrid
chart = create_and_build(ChordBuilder, data, **kw)
chart.left[0].visible = False
chart.below[0].visible = False
chart.outline_line_color = None
return chart
def Horizon(data=None, x=None, y=None, series=None, **kws):
""" Create a horizon chart using :class:`HorizonBuilder
<bokeh.charts.builders.scatter_builder.HorizonBuilder>`
to render the geometry from values.
Args:
data (:ref:`userguide_charts_data_types`): table-like data
x (str or list(str), optional): the column label to use for the x dimension
y (str or list(str), optional): the column label to use for the y dimension
In addition to the parameters specific to this chart,
:ref:`userguide_charts_defaults` are also accepted as keyword parameters.
Returns:
:class:`Chart`: includes glyph renderers that generate the scatter points
Examples:
.. bokeh-plot::
:source-position: above
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', width=800, height=300,
title="horizon plot using stock inputs")
output_file("horizon.html")
show(hp)
"""
kws['x'] = x
kws['y'] = y
kws['series'] = series
tools = kws.get('tools', True)
if tools == True:
tools = "save,resize,reset"
elif isinstance(tools, string_types):
tools = tools.replace('pan', '')
tools = tools.replace('wheel_zoom', '')
tools = tools.replace('box_zoom', '')
tools = tools.replace(',,', ',')
kws['tools'] = tools
chart = create_and_build(HorizonBuilder, data, **kws)
# Hide numerical axis
chart.left[0].visible = False
# Add the series names to the y axis
chart.extra_y_ranges = {"series": FactorRange(factors=chart._builders[0].series_names)}
chart.add_layout(CategoricalAxis(y_range_name="series"), 'left')
return chart
