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sources — Bokeh 3.7.3 Documentation

class AjaxDataSource(*args: Any, id: ID | None = None, **kwargs: Any)[source]#

Bases: WebDataSource

A data source that can populate columns by making Ajax calls to REST endpoints.

The AjaxDataSource can be especially useful if you want to make a standalone document (i.e. not backed by the Bokeh server) that can still dynamically update using an existing REST API.

The response from the REST API should match the .data property of a standard ColumnDataSource, i.e. a JSON dict that maps names to arrays of values:

{
    'x' : [1, 2, 3, ...],
    'y' : [9, 3, 2, ...]
}

Alternatively, if the REST API returns a different format, a CustomJS callback can be provided to convert the REST response into Bokeh format, via the adapter property of this data source.

Initial data can be set by specifying the data property directly. This is necessary when used in conjunction with a FactorRange, even if the columns in data` are empty.

A full example can be seen at examples/basic/data/ajax_source.py

{
  "adapter": null, 
  "content_type": "application/json", 
  "data": {
    "type": "map"
  }, 
  "data_url": {
    "name": "unset", 
    "type": "symbol"
  }, 
  "default_values": {
    "type": "map"
  }, 
  "http_headers": {
    "type": "map"
  }, 
  "id": "p64802", 
  "if_modified": false, 
  "js_event_callbacks": {
    "type": "map"
  }, 
  "js_property_callbacks": {
    "type": "map"
  }, 
  "max_size": null, 
  "method": "POST", 
  "mode": "replace", 
  "name": null, 
  "polling_interval": null, 
  "selected": {
    "attributes": {
      "indices": [], 
      "line_indices": []
    }, 
    "id": "p64803", 
    "name": "Selection", 
    "type": "object"
  }, 
  "selection_policy": {
    "id": "p64804", 
    "name": "UnionRenderers", 
    "type": "object"
  }, 
  "subscribed_events": {
    "type": "set"
  }, 
  "syncable": true, 
  "tags": []
}

adapter = None#

Type:

Nullable(Instance(CustomJS))

A JavaScript callback to adapt raw JSON responses to Bokeh ColumnDataSource format.

If provided, this callback is executes immediately after the JSON data is received, but before appending or replacing data in the data source. The CustomJS callback will receive the AjaxDataSource as cb_obj and will receive the raw JSON response as cb_data.response. The callback code should return a data object suitable for a Bokeh ColumnDataSource (i.e. a mapping of string column names to arrays of data).

content_type = 'application/json'#

Type:

String

Set the “contentType” parameter for the Ajax request.

data = {}#

Type:

ColumnData

Mapping of column names to sequences of data. The columns can be, e.g, Python lists or tuples, NumPy arrays, etc.

The .data attribute can also be set from Pandas DataFrames or GroupBy objects. In these cases, the behaviour is identical to passing the objects to the ColumnDataSource initializer.

data_url = Undefined#

Type:

Required(String)

A URL to to fetch data from.

default_values = {}#

Type:

Dict(String, AnyRef)

Defines the default value for each column.

This is used when inserting rows into a data source, e.g. by edit tools, when a value for a given column is not explicitly provided. If a default value is missing, a tool will defer to its own configuration or will try to let the data source to infer a sensible default value.

Type:

Dict(String, String)

Specify HTTP headers to set for the Ajax request.

Example:

ajax_source.headers = { 'x-my-custom-header': 'some value' }

if_modified = False#

Type:

Bool

Whether to include an If-Modified-Since header in Ajax requests to the server. If this header is supported by the server, then only new data since the last request will be returned.

max_size = None#

Type:

Nullable(Int)

Maximum size of the data columns. If a new fetch would result in columns larger than max_size, then earlier data is dropped to make room.

method = 'POST'#

Type:

Enum(Enumeration(POST, GET))

Specify the HTTP method to use for the Ajax request (GET or POST)

mode = 'replace'#

Type:

Enum(Enumeration(replace, append))

Whether to append new data to existing data (up to max_size), or to replace existing data entirely.

name = None#

Type:

Nullable(String)

An arbitrary, user-supplied name for this model.

This name can be useful when querying the document to retrieve specific Bokeh models.

>>> plot.scatter([1,2,3], [4,5,6], name="temp")
>>> plot.select(name="temp")
[GlyphRenderer(id='399d53f5-73e9-44d9-9527-544b761c7705', ...)]

Note

No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

polling_interval = None#

Type:

Nullable(Int)

A polling interval (in milliseconds) for updating data source.

selected = Selection(id='p64844', ...)#

Type:

Readonly

An instance of a Selection that indicates selected indices on this DataSource. This is a read-only property. You may only change the attributes of this object to change the selection (e.g., selected.indices).

selection_policy = UnionRenderers(id='p64848', ...)#

Type:

Instance(SelectionPolicy)

An instance of a SelectionPolicy that determines how selections are set.

syncable = True#

Type:

Bool

Indicates whether this model should be synchronized back to a Bokeh server when updated in a web browser. Setting to False may be useful to reduce network traffic when dealing with frequently updated objects whose updated values we don’t need.

Note

Setting this property to False will prevent any on_change() callbacks on this object from triggering. However, any JS-side callbacks will still work.

tags = []#

Type:

List

An optional list of arbitrary, user-supplied values to attach to this model.

This data can be useful when querying the document to retrieve specific Bokeh models:

>>> r = plot.scatter([1,2,3], [4,5,6])
>>> r.tags = ["foo", 10]
>>> plot.select(tags=['foo', 10])
[GlyphRenderer(id='1de4c3df-a83d-480a-899b-fb263d3d5dd9', ...)]

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.

Note

No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.

add(data: Sequence[Any], name: str | None = None) → str#

Appends a new column of data to the data source.

Parameters:

• data (seq) – new data to add

• name (str, optional) – column name to use. If not supplied, generate a name of the form “Series ####”

Returns:

the column name used

Return type:

str

apply_theme(property_values: dict[str, Any]) → None#

Apply a set of theme values which will be used rather than defaults, but will not override application-set values.

The passed-in dictionary may be kept around as-is and shared with other instances to save memory (so neither the caller nor the HasProps instance should modify it).

Parameters:

property_values (dict) – theme values to use in place of defaults

Returns:

None

classmethod clear_extensions() → None#

Clear any currently defined custom extensions.

Serialization calls will result in any currently defined custom extensions being included with the generated Document, whether or not there are utilized. This method can be used to clear out all existing custom extension definitions.

clone(**overrides: Any) → Self#

Duplicate a HasProps object.

This creates a shallow clone of the original model, i.e. any mutable containers or child models will not be duplicated. Allows to override particular properties while cloning.

classmethod dataspecs() → dict[str, DataSpec]#

Collect the names of all DataSpec properties on this class.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of DataSpec properties

Return type:

set[str]

classmethod descriptors() → list[PropertyDescriptor[Any]]#

List of property descriptors in the order of definition.

destroy() → None#

Clean up references to the document and property

equals(other: HasProps) → bool#

Structural equality of models.

Parameters:

other (HasProps) – the other instance to compare to

Returns:

True, if properties are structurally equal, otherwise False

classmethod from_df(data: pd.DataFrame) → DataDict#

Create a dict of columns from a Pandas DataFrame, suitable for creating a ColumnDataSource.

Parameters:

data (DataFrame) – data to convert

Returns:

dict[str, np.array]

classmethod from_groupby(data: pd.core.groupby.GroupBy) → DataDict#

Create a dict of columns from a Pandas GroupBy, suitable for creating a ColumnDataSource.

The data generated is the result of running describe on the group.

Parameters:

data (Groupby) – data to convert

Returns:

dict[str, np.array]

js_link(attr: str, other: Model, other_attr: str, attr_selector: int | str | None = None) → None#

Link two Bokeh model properties using JavaScript.

This is a convenience method that simplifies adding a CustomJS callback to update one Bokeh model property whenever another changes value.

Parameters:

• attr (str) – The name of a Bokeh property on this model

• other (Model) – A Bokeh model to link to self.attr

• other_attr (str) – The property on other to link together

• attr_selector (int | str) – The index to link an item in a subscriptable attr

Added in version 1.1

Raises:

ValueError –

Examples

This code with js_link:

select.js_link('value', plot, 'sizing_mode')

is equivalent to the following:

from bokeh.models import CustomJS
select.js_on_change('value',
    CustomJS(args=dict(other=plot),
             code="other.sizing_mode = this.value"
    )
)

Additionally, to use attr_selector to attach the left side of a range slider to a plot’s x_range:

range_slider.js_link('value', plot.x_range, 'start', attr_selector=0)

which is equivalent to:

from bokeh.models import CustomJS
range_slider.js_on_change('value',
    CustomJS(args=dict(other=plot.x_range),
             code="other.start = this.value[0]"
    )
)

js_on_change(event: str, *callbacks: JSChangeCallback) → None#

Attach a CustomJS callback to an arbitrary BokehJS model event.

On the BokehJS side, change events for model properties have the form "change:property_name". As a convenience, if the event name passed to this method is also the name of a property on the model, then it will be prefixed with "change:" automatically:

# these two are equivalent
source.js_on_change('data', callback)
source.js_on_change('change:data', callback)

However, there are other kinds of events that can be useful to respond to, in addition to property change events. For example to run a callback whenever data is streamed to a ColumnDataSource, use the "stream" event on the source:

source.js_on_change('streaming', callback)

classmethod lookup(name: str, *, raises: bool = True) → PropertyDescriptor[Any] | None#

Find the PropertyDescriptor for a Bokeh property on a class, given the property name.

Parameters:

• name (str) – name of the property to search for

• raises (bool) – whether to raise or return None if missing

Returns:

descriptor for property named name

Return type:

PropertyDescriptor

on_change(attr: str, *callbacks: PropertyCallback) → None#

Add a callback on this object to trigger when attr changes.

Parameters:

• attr (str) – an attribute name on this object

• *callbacks (callable) – callback functions to register

Returns:

None

Examples

widget.on_change('value', callback1, callback2, ..., callback_n)

on_event(event: str | type[Event], *callbacks: Callable[[Event], None] | Callable[[], None]) → None#

Run callbacks when the specified event occurs on this Model

Not all Events are supported for all Models. See specific Events in bokeh.events for more information on which Models are able to trigger them.

classmethod parameters() → list[Parameter]#

Generate Python Parameter values suitable for functions that are derived from the glyph.

Returns:

list(Parameter)

patch(patches: Patches, setter: Setter | None = None) → None#

Efficiently update data source columns at specific locations

If it is only necessary to update a small subset of data in a ColumnDataSource, this method can be used to efficiently update only the subset, instead of requiring the entire data set to be sent.

This method should be passed a dictionary that maps column names to lists of tuples that describe a patch change to apply. To replace individual items in columns entirely, the tuples should be of the form:

(index, new_value)  # replace a single column value

# or

(slice, new_values) # replace several column values

Values at an index or slice will be replaced with the corresponding new values.

In the case of columns whose values are other arrays or lists, (e.g. image or patches glyphs), it is also possible to patch “subregions”. In this case the first item of the tuple should be a whose first element is the index of the array item in the CDS patch, and whose subsequent elements are integer indices or slices into the array item:

# replace the entire 10th column of the 2nd array:

  +----------------- index of item in column data source
  |
  |       +--------- row subindex into array item
  |       |
  |       |       +- column subindex into array item
  V       V       V
([2, slice(None), 10], new_values)

Imagining a list of 2d NumPy arrays, the patch above is roughly equivalent to:

data = [arr1, arr2, ...]  # list of 2d arrays

data[2][:, 10] = new_data

There are some limitations to the kinds of slices and data that can be accepted.

• Negative start, stop, or step values for slices will result in a ValueError.

• In a slice, start > stop will result in a ValueError

• When patching 1d or 2d subitems, the subitems must be NumPy arrays.

• New values must be supplied as a flattened one-dimensional array of the appropriate size.

Parameters:

patches (dict[str, list[tuple]]) – lists of patches for each column

Returns:

None

Raises:

ValueError –

Example:

The following example shows how to patch entire column elements. In this case,

source = ColumnDataSource(data=dict(foo=[10, 20, 30], bar=[100, 200, 300]))

patches = {
    'foo' : [ (slice(2), [11, 12]) ],
    'bar' : [ (0, 101), (2, 301) ],
}

source.patch(patches)

After this operation, the value of the source.data will be:

dict(foo=[11, 12, 30], bar=[101, 200, 301])

For a more comprehensive example, see examples/server/app/patch_app.py.

classmethod properties(*, _with_props: bool = False) → set[str] | dict[str, Property[Any]]#

Collect the names of properties on this class.

Warning

In a future version of Bokeh, this method will return a dictionary mapping property names to property objects. To future-proof this current usage of this method, wrap the return value in list.

Returns:

property names

classmethod properties_with_refs() → dict[str, Property[Any]]#

Collect the names of all properties on this class that also have references.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of properties that have references

Return type:

set[str]

properties_with_values(*, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Collect a dict mapping property names to their values.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Non-serializable properties are skipped and property values are in “serialized” format which may be slightly different from the values you would normally read from the properties; the intent of this method is to return the information needed to losslessly reconstitute the object instance.

Parameters:

include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)

Returns:

mapping from property names to their values

Return type:

dict

query_properties_with_values(query: Callable[[PropertyDescriptor[Any]], bool], *, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Query the properties values of HasProps instances with a predicate.

Parameters:

• query (callable) – A callable that accepts property descriptors and returns True or False

• include_defaults (bool, optional) – Whether to include properties that have not been explicitly set by a user (default: True)

Returns:

mapping of property names and values for matching properties

Return type:

dict

references() → set[Model]#

Returns all Models that this object has references to.

remove(name: str) → None#

Remove a column of data.

Parameters:

name (str) – name of the column to remove

Returns:

None

Note

If the column name does not exist, a warning is issued.

remove_on_change(attr: str, *callbacks: Callable[[str, Any, Any], None]) → None#

Remove a callback from this object

select(selector: SelectorType) → Iterable[Model]#

Query this object and all of its references for objects that match the given selector.

Parameters:

selector (JSON-like)

Returns:

seq[Model]

select_one(selector: SelectorType) → Model | None#

Query this object and all of its references for objects that match the given selector. Raises an error if more than one object is found. Returns single matching object, or None if nothing is found :param selector: :type selector: JSON-like

Returns:

Model

set_from_json(name: str, value: Any, *, setter: Setter | None = None) → None#

Set a property value on this object from JSON.

Parameters:

• name (str) – name of the attribute to set

• value (JSON-value) – value to set to the attribute to

• setter (ClientSession or ServerSession or None, optional) –

  This is used to prevent “boomerang” updates to Bokeh apps.

  In the context of a Bokeh server application, incoming updates to properties will be annotated with the session that is doing the updating. This value is propagated through any subsequent change notifications that the update triggers. The session can compare the event setter to itself, and suppress any updates that originate from itself.

Returns:

None

set_select(selector: type[Model] | SelectorType, updates: dict[str, Any]) → None#

Update objects that match a given selector with the specified attribute/value updates.

Parameters:

• selector (JSON-like)

• updates (dict)

Returns:

None

stream(new_data: DataDict, rollover: int | None = None) → None#

Efficiently update data source columns with new append-only data.

In cases where it is necessary to update data columns in, this method can efficiently send only the new data, instead of requiring the entire data set to be re-sent.

Parameters:

• new_data (dict[str, seq]) –

  a mapping of column names to sequences of new data to append to each column.

  All columns of the data source must be present in new_data, with identical-length append data.

• rollover (int, optional) – A maximum column size, above which data from the start of the column begins to be discarded. If None, then columns will continue to grow unbounded (default: None)

Returns:

None

Raises:

ValueError –

Example:

source = ColumnDataSource(data=dict(foo=[], bar=[]))

# has new, identical-length updates for all columns in source
new_data = {
    'foo' : [10, 20],
    'bar' : [100, 200],
}

source.stream(new_data)

themed_values() → dict[str, Any] | None#

Get any theme-provided overrides.

Results are returned as a dict from property name to value, or None if no theme overrides any values for this instance.

Returns:

dict or None

to_df() → pd.DataFrame#

Convert this data source to pandas DataFrame.

Returns:

DataFrame

to_serializable(serializer: Serializer) → ObjectRefRep#

Converts this object to a serializable representation.

trigger(attr: str, old: Any, new: Any, hint: DocumentPatchedEvent | None = None, setter: Setter | None = None) → None#

unapply_theme() → None#

Remove any themed values and restore defaults.

Returns:

None

update(**kwargs: Any) → None#

Updates the object’s properties from the given keyword arguments.

Returns:

None

Examples

The following are equivalent:

from bokeh.models import Range1d

r = Range1d

# set properties individually:
r.start = 10
r.end = 20

# update properties together:
r.update(start=10, end=20)

property column_names: list[str]#

A list of the column names in this data source.

property document: Document | None#

The Document this model is attached to (can be None)

class CDSView(*args: Any, id: ID | None = None, **kwargs: Any)[source]#

Bases: Model

A view into a ColumnDataSource that represents a row-wise subset.

{
  "filter": {
    "id": "p64856", 
    "name": "AllIndices", 
    "type": "object"
  }, 
  "id": "p64855", 
  "js_event_callbacks": {
    "type": "map"
  }, 
  "js_property_callbacks": {
    "type": "map"
  }, 
  "name": null, 
  "subscribed_events": {
    "type": "set"
  }, 
  "syncable": true, 
  "tags": []
}

filter = AllIndices(id='p64859', ...)#

Type:

Instance(Filter)

Defines the subset of indices to use from the data source this view applies to.

By default all indices are used (AllIndices filter). This can be changed by using specialized filters like IndexFilter, BooleanFilter, etc. Filters can be composed using set operations to create non-trivial data masks. This can be accomplished by directly using models like InversionFilter, UnionFilter, etc., or by using set operators on filters, e.g.:

# filters everything but indexes 10 and 11
cds_view.filter &= ~IndexFilter(indices=[10, 11])

name = None#

Type:

Nullable(String)

An arbitrary, user-supplied name for this model.

This name can be useful when querying the document to retrieve specific Bokeh models.

>>> plot.scatter([1,2,3], [4,5,6], name="temp")
>>> plot.select(name="temp")
[GlyphRenderer(id='399d53f5-73e9-44d9-9527-544b761c7705', ...)]

Note

No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

syncable = True#

Type:

Bool

Indicates whether this model should be synchronized back to a Bokeh server when updated in a web browser. Setting to False may be useful to reduce network traffic when dealing with frequently updated objects whose updated values we don’t need.

Note

Setting this property to False will prevent any on_change() callbacks on this object from triggering. However, any JS-side callbacks will still work.

tags = []#

Type:

List

An optional list of arbitrary, user-supplied values to attach to this model.

This data can be useful when querying the document to retrieve specific Bokeh models:

>>> r = plot.scatter([1,2,3], [4,5,6])
>>> r.tags = ["foo", 10]
>>> plot.select(tags=['foo', 10])
[GlyphRenderer(id='1de4c3df-a83d-480a-899b-fb263d3d5dd9', ...)]

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.

Note

No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.

apply_theme(property_values: dict[str, Any]) → None#

Apply a set of theme values which will be used rather than defaults, but will not override application-set values.

The passed-in dictionary may be kept around as-is and shared with other instances to save memory (so neither the caller nor the HasProps instance should modify it).

Parameters:

property_values (dict) – theme values to use in place of defaults

Returns:

None

classmethod clear_extensions() → None#

Clear any currently defined custom extensions.

Serialization calls will result in any currently defined custom extensions being included with the generated Document, whether or not there are utilized. This method can be used to clear out all existing custom extension definitions.

clone(**overrides: Any) → Self#

Duplicate a HasProps object.

This creates a shallow clone of the original model, i.e. any mutable containers or child models will not be duplicated. Allows to override particular properties while cloning.

classmethod dataspecs() → dict[str, DataSpec]#

Collect the names of all DataSpec properties on this class.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of DataSpec properties

Return type:

set[str]

classmethod descriptors() → list[PropertyDescriptor[Any]]#

List of property descriptors in the order of definition.

destroy() → None#

Clean up references to the document and property

equals(other: HasProps) → bool#

Structural equality of models.

Parameters:

other (HasProps) – the other instance to compare to

Returns:

True, if properties are structurally equal, otherwise False

js_link(attr: str, other: Model, other_attr: str, attr_selector: int | str | None = None) → None#

Link two Bokeh model properties using JavaScript.

This is a convenience method that simplifies adding a CustomJS callback to update one Bokeh model property whenever another changes value.

Parameters:

• attr (str) – The name of a Bokeh property on this model

• other (Model) – A Bokeh model to link to self.attr

• other_attr (str) – The property on other to link together

• attr_selector (int | str) – The index to link an item in a subscriptable attr

Added in version 1.1

Raises:

ValueError –

Examples

This code with js_link:

select.js_link('value', plot, 'sizing_mode')

is equivalent to the following:

from bokeh.models import CustomJS
select.js_on_change('value',
    CustomJS(args=dict(other=plot),
             code="other.sizing_mode = this.value"
    )
)

Additionally, to use attr_selector to attach the left side of a range slider to a plot’s x_range:

range_slider.js_link('value', plot.x_range, 'start', attr_selector=0)

which is equivalent to:

from bokeh.models import CustomJS
range_slider.js_on_change('value',
    CustomJS(args=dict(other=plot.x_range),
             code="other.start = this.value[0]"
    )
)

js_on_change(event: str, *callbacks: JSChangeCallback) → None#

Attach a CustomJS callback to an arbitrary BokehJS model event.

On the BokehJS side, change events for model properties have the form "change:property_name". As a convenience, if the event name passed to this method is also the name of a property on the model, then it will be prefixed with "change:" automatically:

# these two are equivalent
source.js_on_change('data', callback)
source.js_on_change('change:data', callback)

However, there are other kinds of events that can be useful to respond to, in addition to property change events. For example to run a callback whenever data is streamed to a ColumnDataSource, use the "stream" event on the source:

source.js_on_change('streaming', callback)

classmethod lookup(name: str, *, raises: bool = True) → PropertyDescriptor[Any] | None#

Find the PropertyDescriptor for a Bokeh property on a class, given the property name.

Parameters:

• name (str) – name of the property to search for

• raises (bool) – whether to raise or return None if missing

Returns:

descriptor for property named name

Return type:

PropertyDescriptor

on_change(attr: str, *callbacks: PropertyCallback) → None#

Add a callback on this object to trigger when attr changes.

Parameters:

• attr (str) – an attribute name on this object

• *callbacks (callable) – callback functions to register

Returns:

None

Examples

widget.on_change('value', callback1, callback2, ..., callback_n)

on_event(event: str | type[Event], *callbacks: Callable[[Event], None] | Callable[[], None]) → None#

Run callbacks when the specified event occurs on this Model

Not all Events are supported for all Models. See specific Events in bokeh.events for more information on which Models are able to trigger them.

classmethod parameters() → list[Parameter]#

Generate Python Parameter values suitable for functions that are derived from the glyph.

Returns:

list(Parameter)

classmethod properties(*, _with_props: bool = False) → set[str] | dict[str, Property[Any]]#

Collect the names of properties on this class.

Warning

In a future version of Bokeh, this method will return a dictionary mapping property names to property objects. To future-proof this current usage of this method, wrap the return value in list.

Returns:

property names

classmethod properties_with_refs() → dict[str, Property[Any]]#

Collect the names of all properties on this class that also have references.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of properties that have references

Return type:

set[str]

properties_with_values(*, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Collect a dict mapping property names to their values.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Non-serializable properties are skipped and property values are in “serialized” format which may be slightly different from the values you would normally read from the properties; the intent of this method is to return the information needed to losslessly reconstitute the object instance.

Parameters:

include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)

Returns:

mapping from property names to their values

Return type:

dict

query_properties_with_values(query: Callable[[PropertyDescriptor[Any]], bool], *, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Query the properties values of HasProps instances with a predicate.

Parameters:

• query (callable) – A callable that accepts property descriptors and returns True or False

• include_defaults (bool, optional) – Whether to include properties that have not been explicitly set by a user (default: True)

Returns:

mapping of property names and values for matching properties

Return type:

dict

references() → set[Model]#

Returns all Models that this object has references to.

remove_on_change(attr: str, *callbacks: Callable[[str, Any, Any], None]) → None#

Remove a callback from this object

select(selector: SelectorType) → Iterable[Model]#

Query this object and all of its references for objects that match the given selector.

Parameters:

selector (JSON-like)

Returns:

seq[Model]

select_one(selector: SelectorType) → Model | None#

Query this object and all of its references for objects that match the given selector. Raises an error if more than one object is found. Returns single matching object, or None if nothing is found :param selector: :type selector: JSON-like

Returns:

Model

set_from_json(name: str, value: Any, *, setter: Setter | None = None) → None#

Set a property value on this object from JSON.

Parameters:

• name (str) – name of the attribute to set

• value (JSON-value) – value to set to the attribute to

• setter (ClientSession or ServerSession or None, optional) –

  This is used to prevent “boomerang” updates to Bokeh apps.

  In the context of a Bokeh server application, incoming updates to properties will be annotated with the session that is doing the updating. This value is propagated through any subsequent change notifications that the update triggers. The session can compare the event setter to itself, and suppress any updates that originate from itself.

Returns:

None

set_select(selector: type[Model] | SelectorType, updates: dict[str, Any]) → None#

Update objects that match a given selector with the specified attribute/value updates.

Parameters:

• selector (JSON-like)

• updates (dict)

Returns:

None

themed_values() → dict[str, Any] | None#

Get any theme-provided overrides.

Results are returned as a dict from property name to value, or None if no theme overrides any values for this instance.

Returns:

dict or None

to_serializable(serializer: Serializer) → ObjectRefRep#

Converts this object to a serializable representation.

trigger(attr: str, old: Any, new: Any, hint: DocumentPatchedEvent | None = None, setter: Setter | None = None) → None#

unapply_theme() → None#

Remove any themed values and restore defaults.

Returns:

None

update(**kwargs: Any) → None#

Updates the object’s properties from the given keyword arguments.

Returns:

None

Examples

The following are equivalent:

from bokeh.models import Range1d

r = Range1d

# set properties individually:
r.start = 10
r.end = 20

# update properties together:
r.update(start=10, end=20)

property document: Document | None#

The Document this model is attached to (can be None)

class ColumnDataSource(*args: Any, id: ID | None = None, **kwargs: Any)[source]#

Bases: ColumnarDataSource

Maps names of columns to sequences or arrays.

The ColumnDataSource is a fundamental data structure of Bokeh. Most plots, data tables, etc. will be driven by a ColumnDataSource.

If the ColumnDataSource initializer is called with a single argument that can be any of the following:

• A Python dict that maps string names to sequences of values, e.g. lists, arrays, etc.

  data = {'x': [1,2,3,4], 'y': np.array([10.0, 20.0, 30.0, 40.0])}
  
  source = ColumnDataSource(data)
  

Note

ColumnDataSource only creates a shallow copy of data. Use e.g. ColumnDataSource(copy.deepcopy(data)) if initializing from another ColumnDataSource.data object that you want to keep independent.

• A Pandas DataFrame object

  source = ColumnDataSource(df)
  

  In this case the CDS will have columns corresponding to the columns of the DataFrame. If the DataFrame columns have multiple levels, they will be flattened using an underscore (e.g. level_0_col_level_1_col). The index of the DataFrame will be flattened to an Index of tuples if it’s a MultiIndex, and then reset using reset_index. The result will be a column with the same name if the index was named, or level_0_name_level_1_name if it was a named MultiIndex. If the Index did not have a name or the MultiIndex name could not be flattened/determined, the reset_index function will name the index column index, or level_0 if the name index is not available.

• A Pandas GroupBy object

  group = df.groupby(('colA', 'ColB'))
  

  In this case the CDS will have columns corresponding to the result of calling group.describe(). The describe method generates columns for statistical measures such as mean and count for all the non-grouped original columns. The CDS columns are formed by joining original column names with the computed measure. For example, if a DataFrame has columns 'year' and 'mpg'. Then passing df.groupby('year') to a CDS will result in columns such as 'mpg_mean'

  If the GroupBy.describe result has a named index column, then CDS will also have a column with this name. However, if the index name (or any subname of a MultiIndex) is None, then the CDS will have a column generically named index for the index.

  Note this capability to adapt GroupBy objects may only work with Pandas >=0.20.0.

Note

There is an implicit assumption that all the columns in a given ColumnDataSource all have the same length at all times. For this reason, it is usually preferable to update the .data property of a data source “all at once”.

{
  "data": {
    "type": "map"
  }, 
  "default_values": {
    "type": "map"
  }, 
  "id": "p64866", 
  "js_event_callbacks": {
    "type": "map"
  }, 
  "js_property_callbacks": {
    "type": "map"
  }, 
  "name": null, 
  "selected": {
    "attributes": {
      "indices": [], 
      "line_indices": []
    }, 
    "id": "p64867", 
    "name": "Selection", 
    "type": "object"
  }, 
  "selection_policy": {
    "id": "p64868", 
    "name": "UnionRenderers", 
    "type": "object"
  }, 
  "subscribed_events": {
    "type": "set"
  }, 
  "syncable": true, 
  "tags": []
}

data = {}#

Type:

ColumnData

Mapping of column names to sequences of data. The columns can be, e.g, Python lists or tuples, NumPy arrays, etc.

The .data attribute can also be set from Pandas DataFrames or GroupBy objects. In these cases, the behaviour is identical to passing the objects to the ColumnDataSource initializer.

default_values = {}#

Type:

Dict(String, AnyRef)

Defines the default value for each column.

This is used when inserting rows into a data source, e.g. by edit tools, when a value for a given column is not explicitly provided. If a default value is missing, a tool will defer to its own configuration or will try to let the data source to infer a sensible default value.

name = None#

Type:

Nullable(String)

An arbitrary, user-supplied name for this model.

This name can be useful when querying the document to retrieve specific Bokeh models.

>>> plot.scatter([1,2,3], [4,5,6], name="temp")
>>> plot.select(name="temp")
[GlyphRenderer(id='399d53f5-73e9-44d9-9527-544b761c7705', ...)]

Note

No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

selected = Selection(id='p64881', ...)#

Type:

Readonly

An instance of a Selection that indicates selected indices on this DataSource. This is a read-only property. You may only change the attributes of this object to change the selection (e.g., selected.indices).

selection_policy = UnionRenderers(id='p64885', ...)#

Type:

Instance(SelectionPolicy)

An instance of a SelectionPolicy that determines how selections are set.

syncable = True#

Type:

Bool

Indicates whether this model should be synchronized back to a Bokeh server when updated in a web browser. Setting to False may be useful to reduce network traffic when dealing with frequently updated objects whose updated values we don’t need.

Note

Setting this property to False will prevent any on_change() callbacks on this object from triggering. However, any JS-side callbacks will still work.

tags = []#

Type:

List

An optional list of arbitrary, user-supplied values to attach to this model.

This data can be useful when querying the document to retrieve specific Bokeh models:

>>> r = plot.scatter([1,2,3], [4,5,6])
>>> r.tags = ["foo", 10]
>>> plot.select(tags=['foo', 10])
[GlyphRenderer(id='1de4c3df-a83d-480a-899b-fb263d3d5dd9', ...)]

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.

Note

No uniqueness guarantees or other conditions are enforced on any tags that are provided, nor are the tags used directly by Bokeh for any reason.

__init__(data: DataDict | pd.DataFrame | pd.core.groupby.GroupBy, **kwargs: Any) → None[source]#

__init__(**kwargs: Any) → None

If called with a single argument that is a dict or pandas.DataFrame, treat that implicitly as the “data” attribute.

add(data: Sequence[Any], name: str | None = None) → str[source]#

Appends a new column of data to the data source.

Parameters:

• data (seq) – new data to add

• name (str, optional) – column name to use. If not supplied, generate a name of the form “Series ####”

Returns:

the column name used

Return type:

str

apply_theme(property_values: dict[str, Any]) → None#

Apply a set of theme values which will be used rather than defaults, but will not override application-set values.

The passed-in dictionary may be kept around as-is and shared with other instances to save memory (so neither the caller nor the HasProps instance should modify it).

Parameters:

property_values (dict) – theme values to use in place of defaults

Returns:

None

classmethod clear_extensions() → None#

Clear any currently defined custom extensions.

Serialization calls will result in any currently defined custom extensions being included with the generated Document, whether or not there are utilized. This method can be used to clear out all existing custom extension definitions.

clone(**overrides: Any) → Self#

Duplicate a HasProps object.

This creates a shallow clone of the original model, i.e. any mutable containers or child models will not be duplicated. Allows to override particular properties while cloning.

classmethod dataspecs() → dict[str, DataSpec]#

Collect the names of all DataSpec properties on this class.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of DataSpec properties

Return type:

set[str]

classmethod descriptors() → list[PropertyDescriptor[Any]]#

List of property descriptors in the order of definition.

destroy() → None#

Clean up references to the document and property

equals(other: HasProps) → bool#

Structural equality of models.

Parameters:

other (HasProps) – the other instance to compare to

Returns:

True, if properties are structurally equal, otherwise False

classmethod from_df(data: pd.DataFrame) → DataDict[source]#

Create a dict of columns from a Pandas DataFrame, suitable for creating a ColumnDataSource.

Parameters:

data (DataFrame) – data to convert

Returns:

dict[str, np.array]

classmethod from_groupby(data: pd.core.groupby.GroupBy) → DataDict[source]#

Create a dict of columns from a Pandas GroupBy, suitable for creating a ColumnDataSource.

The data generated is the result of running describe on the group.

Parameters:

data (Groupby) – data to convert

Returns:

dict[str, np.array]

js_link(attr: str, other: Model, other_attr: str, attr_selector: int | str | None = None) → None#

Link two Bokeh model properties using JavaScript.

This is a convenience method that simplifies adding a CustomJS callback to update one Bokeh model property whenever another changes value.

Parameters:

• attr (str) – The name of a Bokeh property on this model

• other (Model) – A Bokeh model to link to self.attr

• other_attr (str) – The property on other to link together

• attr_selector (int | str) – The index to link an item in a subscriptable attr

Added in version 1.1

Raises:

ValueError –

Examples

This code with js_link:

select.js_link('value', plot, 'sizing_mode')

is equivalent to the following:

from bokeh.models import CustomJS
select.js_on_change('value',
    CustomJS(args=dict(other=plot),
             code="other.sizing_mode = this.value"
    )
)

Additionally, to use attr_selector to attach the left side of a range slider to a plot’s x_range:

range_slider.js_link('value', plot.x_range, 'start', attr_selector=0)

which is equivalent to:

from bokeh.models import CustomJS
range_slider.js_on_change('value',
    CustomJS(args=dict(other=plot.x_range),
             code="other.start = this.value[0]"
    )
)

js_on_change(event: str, *callbacks: JSChangeCallback) → None#

Attach a CustomJS callback to an arbitrary BokehJS model event.

On the BokehJS side, change events for model properties have the form "change:property_name". As a convenience, if the event name passed to this method is also the name of a property on the model, then it will be prefixed with "change:" automatically:

# these two are equivalent
source.js_on_change('data', callback)
source.js_on_change('change:data', callback)

However, there are other kinds of events that can be useful to respond to, in addition to property change events. For example to run a callback whenever data is streamed to a ColumnDataSource, use the "stream" event on the source:

source.js_on_change('streaming', callback)

classmethod lookup(name: str, *, raises: bool = True) → PropertyDescriptor[Any] | None#

Find the PropertyDescriptor for a Bokeh property on a class, given the property name.

Parameters:

• name (str) – name of the property to search for

• raises (bool) – whether to raise or return None if missing

Returns:

descriptor for property named name

Return type:

PropertyDescriptor

on_change(attr: str, *callbacks: PropertyCallback) → None#

Add a callback on this object to trigger when attr changes.

Parameters:

• attr (str) – an attribute name on this object

• *callbacks (callable) – callback functions to register

Returns:

None

Examples

widget.on_change('value', callback1, callback2, ..., callback_n)

on_event(event: str | type[Event], *callbacks: Callable[[Event], None] | Callable[[], None]) → None#

Run callbacks when the specified event occurs on this Model

Not all Events are supported for all Models. See specific Events in bokeh.events for more information on which Models are able to trigger them.

classmethod parameters() → list[Parameter]#

Generate Python Parameter values suitable for functions that are derived from the glyph.

Returns:

list(Parameter)

patch(patches: Patches, setter: Setter | None = None) → None[source]#

Efficiently update data source columns at specific locations

If it is only necessary to update a small subset of data in a ColumnDataSource, this method can be used to efficiently update only the subset, instead of requiring the entire data set to be sent.

This method should be passed a dictionary that maps column names to lists of tuples that describe a patch change to apply. To replace individual items in columns entirely, the tuples should be of the form:

(index, new_value)  # replace a single column value

# or

(slice, new_values) # replace several column values

Values at an index or slice will be replaced with the corresponding new values.

In the case of columns whose values are other arrays or lists, (e.g. image or patches glyphs), it is also possible to patch “subregions”. In this case the first item of the tuple should be a whose first element is the index of the array item in the CDS patch, and whose subsequent elements are integer indices or slices into the array item:

# replace the entire 10th column of the 2nd array:

  +----------------- index of item in column data source
  |
  |       +--------- row subindex into array item
  |       |
  |       |       +- column subindex into array item
  V       V       V
([2, slice(None), 10], new_values)

Imagining a list of 2d NumPy arrays, the patch above is roughly equivalent to:

data = [arr1, arr2, ...]  # list of 2d arrays

data[2][:, 10] = new_data

There are some limitations to the kinds of slices and data that can be accepted.

• Negative start, stop, or step values for slices will result in a ValueError.

• In a slice, start > stop will result in a ValueError

• When patching 1d or 2d subitems, the subitems must be NumPy arrays.

• New values must be supplied as a flattened one-dimensional array of the appropriate size.

Parameters:

patches (dict[str, list[tuple]]) – lists of patches for each column

Returns:

None

Raises:

ValueError –

Example:

The following example shows how to patch entire column elements. In this case,

source = ColumnDataSource(data=dict(foo=[10, 20, 30], bar=[100, 200, 300]))

patches = {
    'foo' : [ (slice(2), [11, 12]) ],
    'bar' : [ (0, 101), (2, 301) ],
}

source.patch(patches)

After this operation, the value of the source.data will be:

dict(foo=[11, 12, 30], bar=[101, 200, 301])

For a more comprehensive example, see examples/server/app/patch_app.py.

classmethod properties(*, _with_props: bool = False) → set[str] | dict[str, Property[Any]]#

Collect the names of properties on this class.

Warning

In a future version of Bokeh, this method will return a dictionary mapping property names to property objects. To future-proof this current usage of this method, wrap the return value in list.

Returns:

property names

classmethod properties_with_refs() → dict[str, Property[Any]]#

Collect the names of all properties on this class that also have references.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Returns:

names of properties that have references

Return type:

set[str]

properties_with_values(*, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Collect a dict mapping property names to their values.

This method always traverses the class hierarchy and includes properties defined on any parent classes.

Non-serializable properties are skipped and property values are in “serialized” format which may be slightly different from the values you would normally read from the properties; the intent of this method is to return the information needed to losslessly reconstitute the object instance.

Parameters:

include_defaults (bool, optional) – Whether to include properties that haven’t been explicitly set since the object was created. (default: True)

Returns:

mapping from property names to their values

Return type:

dict

query_properties_with_values(query: Callable[[PropertyDescriptor[Any]], bool], *, include_defaults: bool = True, include_undefined: bool = False) → dict[str, Any]#

Query the properties values of HasProps instances with a predicate.

Parameters:

• query (callable) – A callable that accepts property descriptors and returns True or False

• include_defaults (bool, optional) – Whether to include properties that have not been explicitly set by a user (default: True)

Returns:

mapping of property names and values for matching properties

Return type:

dict

references() → set[Model]#

Returns all Models that this object has references to.

remove(name: str) → None[source]#

Remove a column of data.

Parameters:

name (str) – name of the column to remove

Returns:

None

Note

If the column name does not exist, a warning is issued.

remove_on_change(attr: str, *callbacks: Callable[[str, Any, Any], None]) → None#

Remove a callback from this object

select(selector: SelectorType) → Iterable[Model]#

Query this object and all of its references for objects that match the given selector.

Parameters:

selector (JSON-like)

Returns:

seq[Model]

select_one(selector: SelectorType) → Model | None#

Query this object and all of its references for objects that match the given selector. Raises an error if more than one object is found. Returns single matching object, or None if nothing is found :param selector: :type selector: JSON-like

Returns:

Model

set_from_json(name: str, value: Any, *, setter: Setter | None = None) → None#

Set a property value on this object from JSON.

Parameters:

• name (str) – name of the attribute to set

• value (JSON-value) – value to set to the attribute to

• setter (ClientSession or ServerSession or None, optional) –

  This is used to prevent “boomerang” updates to Bokeh apps.

  In the context of a Bokeh server application, incoming updates to properties will be annotated with the session that is doing the updating. This value is propagated through any subsequent change notifications that the update triggers. The session can compare the event setter to itself, and suppress any updates that originate from itself.

Returns:

None

set_select(selector: type[Model] | SelectorType, updates: dict[str, Any]) → None#

Update objects that match a given selector with the specified attribute/value updates.

Parameters:

• selector (JSON-like)

• updates (dict)

Returns:

None

stream(new_data: DataDict, rollover: int | None = None) → None[source]#

Efficiently update data source columns with new append-only data.

In cases where it is necessary to update data columns in, this method can efficiently send only the new data, instead of requiring the entire data set to be re-sent.

Parameters:

• new_data (dict[str, seq]) –

  a mapping of column names to sequences of new data to append to each column.

  All columns of the data source must be present in new_data, with identical-length append data.

• rollover (int, optional) – A maximum column size, above which data from the start of the column begins to be discarded. If None, then columns will continue to grow unbounded (default: None)

Returns:

None

Raises:

ValueError –

Example:

source = ColumnDataSource(data=dict(foo=[], bar=[]))

# has new, identical-length updates for all columns in source
new_data = {
    'foo' : [10, 20],
    'bar' : [100, 200],
}

source.stream(new_data)

themed_values() → dict[str, Any] | None#

Get any theme-provided overrides.

Results are returned as a dict from property name to value, or None if no theme overrides any values for this instance.

Returns:

dict or None

to_df() → pd.DataFrame[source]#

Convert this data source to pandas DataFrame.

Returns:

DataFrame

to_serializable(serializer: Serializer) → ObjectRefRep#

Converts this object to a serializable representation.

trigger(attr: str, old: Any, new: Any, hint: DocumentPatchedEvent | None = None, setter: Setter | None = None) → None#

unapply_theme() → None#

Remove any themed values and restore defaults.

Returns:

None

update(**kwargs: Any) → None#

Updates the object’s properties from the given keyword arguments.

Returns:

None

Examples

The following are equivalent:

from bokeh.models import Range1d

r = Range1d

# set properties individually:
r.start = 10
r.end = 20

# update properties together:
r.update(start=10, end=20)

property column_names: list[str]#

A list of the column names in this data source.

property document: Document | None#

The Document this model is attached to (can be None)