bokeh.models.sources¶

class AjaxDataSource(*args, **kw)[source]¶

Bases: bokeh.models.sources.RemoteSource

content_type¶

property type: String

Set the “contentType” parameter for the Ajax request.

http_headers¶

property type: Dict ( String , String )

HTTP headers to set for the Ajax request.

if_modified¶

property 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¶

property type: Int

Maximum size of the data array being kept after each pull requests. Larger than that size, the data will be right shifted.

method¶

property type: Enum ( Enumeration(POST, GET) )

http method - GET or POST

mode¶

property type: Enum ( Enumeration(replace, append) )

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

{
  "callback": null,
  "column_names": [],
  "content_type": "application/json",
  "data": {},
  "data_url": null,
  "http_headers": {},
  "id": "b16d37a0-3398-451b-819c-3e2963dd20d0",
  "if_modified": false,
  "js_callbacks": {},
  "max_size": null,
  "method": "POST",
  "mode": "replace",
  "name": null,
  "polling_interval": null,
  "selected": {
    "0d": {
      "glyph": null,
      "indices": []
    },
    "1d": {
      "indices": []
    },
    "2d": {}
  },
  "tags": []
}

class ColumnDataSource(*args, **kw)[source]¶

Bases: bokeh.models.sources.ColumnarDataSource

Maps names of columns to sequences or arrays.

If the ColumnDataSource initializer is called with a single argument that is a dict or pandas.DataFrame, that argument is used as the value for the “data” attribute. For example:

ColumnDataSource(mydict) # same as ColumnDataSource(data=mydict)
ColumnDataSource(df) # same as ColumnDataSource(data=df)

Note

There is an implicit assumption that all the columns in a a given ColumnDataSource have the same length.

data¶

property type: ColumnData ( String , Seq ( Any ) )

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

add(data, name=None)[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

classmethod from_df(data)[source]¶

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

Parameters:	data (DataFrame) – data to convert
Returns:	dict(str, list)

patch(patches, setter=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, each of the form (index, new_value). The value at the given index for that column will be updated with the new value.

Parameters:	patches (dict[str, list[tuple]]) – lists of patches for each column.
Returns:	None
Raises:	ValueError

Example:

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

patches = {
    'foo' : [ (0, 1) ],
    'bar' : [ (0, 101), (1, 201) ],
}

source.patch(patches)

push_notebook()[source]¶

Update a data source for a plot in a Jupyter notebook.

This function can be be used to update data in plot data sources in the Jupyter notebook, without having to use the Bokeh server.

Warning

This function has been deprecated. Please use bokeh.io.push_notebook() which will push all changes (not just data sources) to the last shown plot in a Jupyter notebook.

Returns:	None

remove(name)[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.

stream(new_data, rollover=None, setter=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)

to_df()[source]¶

Convert this data source to pandas dataframe.

If column_names is set, use those. Otherwise let Pandas infer the column names. The column_names property can be used both to order and filter the columns.

Returns:	DataFrame

{
  "callback": null,
  "column_names": [],
  "data": {},
  "id": "c40499d6-ac2f-43d4-a2e9-3b728eb48a15",
  "js_callbacks": {},
  "name": null,
  "selected": {
    "0d": {
      "glyph": null,
      "indices": []
    },
    "1d": {
      "indices": []
    },
    "2d": {}
  },
  "tags": []
}

class ColumnarDataSource(**kwargs)[source]¶

Bases: bokeh.models.sources.DataSource

A baseclass for data source types, which can be mapped onto a columnar format. Not useful to instantiate on its own.

column_names¶

property type: List ( String )

An list of names for all the columns in this DataSource.

{
  "callback": null,
  "column_names": [],
  "id": "e4e1a426-33af-4b1c-a396-5ac0db1fad28",
  "js_callbacks": {},
  "name": null,
  "selected": {
    "0d": {
      "glyph": null,
      "indices": []
    },
    "1d": {
      "indices": []
    },
    "2d": {}
  },
  "tags": []
}

class DataSource(**kwargs)[source]¶

Bases: bokeh.model.Model

A base class for data source types. DataSource is not generally useful to instantiate on its own.

callback¶

property type: Instance ( Callback )

A callback to run in the browser whenever the selection is changed.

selected¶

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

A dict to indicate selected indices on different dimensions on this DataSource. Keys are:

• 0d: indicates whether a Line or Patch glyphs have been hit. Value is a

  dict with the following keys:

  • flag (boolean): true if glyph was with false otherwise
  • indices (list): indices hit (if applicable)

• 1d: indicates whether any of all other glyph (except [multi]line or

  patches) was hit:

  • indices (list): indices that were hit/selected

• 2d: indicates whether a [multi]line or patches) were hit:

  • indices (list(list)): indices of the lines/patches that were

    hit/selected

{
  "callback": null,
  "id": "86a90b88-25f2-4684-820d-fdeb32d67c64",
  "js_callbacks": {},
  "name": null,
  "selected": {
    "0d": {
      "glyph": null,
      "indices": []
    },
    "1d": {
      "indices": []
    },
    "2d": {}
  },
  "tags": []
}

class GeoJSONDataSource(**kwargs)[source]¶

Bases: bokeh.models.sources.ColumnarDataSource

geojson¶

property type: JSON

GeoJSON that contains features for plotting. Currently GeoJSONDataSource can only process a FeatureCollection or GeometryCollection.

{
  "callback": null,
  "column_names": [],
  "geojson": null,
  "id": "aab9f4a9-720d-4eeb-a9c8-ffbe9c864170",
  "js_callbacks": {},
  "name": null,
  "selected": {
    "0d": {
      "glyph": null,
      "indices": []
    },
    "1d": {
      "indices": []
    },
    "2d": {}
  },
  "tags": []
}

class RemoteSource(*args, **kw)[source]¶

Bases: bokeh.models.sources.ColumnDataSource

data_url¶

property type: String

The URL to the endpoint for the data.

polling_interval¶

property type: Int

polling interval for updating data source in milliseconds

{
  "callback": null,
  "column_names": [],
  "data": {},
  "data_url": null,
  "id": "2c554964-4a44-41d5-ab19-86c4c272341a",
  "js_callbacks": {},
  "name": null,
  "polling_interval": null,
  "selected": {
    "0d": {
      "glyph": null,
      "indices": []
    },
    "1d": {
      "indices": []
    },
    "2d": {}
  },
  "tags": []
}