AjaxDataSource
Bases: bokeh.models.sources.RemoteSource
bokeh.models.sources.RemoteSource
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:
.data
ColumnDataSource
{ '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.
CustomJS
adapter
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.
data
FactorRange
A full example can be seen at examples/howto/ajax_source.py
content_type
property type: String
String
Set the “contentType” parameter for the Ajax request.
http_headers
property type: Dict ( String , String )
Dict
Specify HTTP headers to set for the Ajax request.
Example:
ajax_source.headers = { 'x-my-custom-header': 'some value' }
if_modified
property type: Bool
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.
If-Modified-Since
method
property type: Enum ( Enumeration(POST, GET) )
Enum
Specify the HTTP method to use for the Ajax request (GET or POST)
{ "adapter": null, "callback": null, "content_type": "application/json", "data": {}, "data_url": null, "http_headers": {}, "id": "14360", "if_modified": false, "js_event_callbacks": {}, "js_property_callbacks": {}, "max_size": null, "method": "POST", "mode": "replace", "name": null, "polling_interval": null, "selected": { "id": "14361", "type": "Selection" }, "selection_policy": { "id": "14362", "type": "UnionRenderers" }, "subscribed_events": [], "tags": [] }
CDSView
Bases: bokeh.model.Model
bokeh.model.Model
A view into a ColumnDataSource that represents a row-wise subset.
filters
property type: List ( Instance ( Filter ) )
List
Instance
Filter
List of filters that the view comprises.
source
property type: Instance ( ColumnarDataSource )
ColumnarDataSource
The ColumnDataSource associated with this view. Used to determine the length of the columns.
{ "filters": [], "id": "14367", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "source": null, "subscribed_events": [], "tags": [] }
Bases: bokeh.models.sources.DataSource
bokeh.models.sources.DataSource
A base class for data source types, which can be mapped onto a columnar format.
Note
This is an abstract base class used to help organize the hierarchy of Bokeh model types. It is not useful to instantiate on its own.
selection_policy
property type: Instance ( SelectionPolicy )
SelectionPolicy
An instance of a SelectionPolicy that determines how selections are set.
{ "callback": null, "id": "14370", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "selected": { "id": "14372", "type": "Selection" }, "selection_policy": { "id": "14371", "type": "UnionRenderers" }, "subscribed_events": [], "tags": [] }
Bases: bokeh.models.sources.ColumnarDataSource
bokeh.models.sources.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.
dict
data = {'x': [1,2,3,4], 'y': np.ndarray([10.0, 20.0, 30.0, 40.0])} source = ColumnDataSource(data)
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.
ColumnDataSource(copy.deepcopy(data))
ColumnDataSource.data
A Pandas DataFrame object
DataFrame
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.
Index
MultiIndex
reset_index
index
level_0
A Pandas GroupBy object
GroupBy
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'
group.describe()
describe
mean
count
'year'
'mpg'
df.groupby('year')
'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.
GroupBy.describe
None
Note this capability to adapt GroupBy objects may only work with Pandas >=0.20.0.
>=0.20.0
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”.
property type: ColumnData ( String , Seq ( Any ) )
ColumnData
Seq
Any
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.
__init__
If called with a single argument that is a dict or pandas.DataFrame, treat that implicitly as the “data” attribute.
pandas.DataFrame
add
Appends a new column of data to the data source.
data (seq) – new data to add
name (str, optional) – column name to use. If not supplied, generate a name of the form “Series ####”
the column name used
str
from_df
Create a dict of columns from a Pandas DataFrame, suitable for creating a ColumnDataSource.
data (DataFrame) – data to convert
dict[str, np.array]
from_groupby
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.
data (Groupby) – data to convert
patch
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.
start
stop
step
ValueError
In a slice, start > stop will result in a ValueError
start > stop
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.
patches (dict[str, list[tuple]]) – lists of patches for each column
ValueError –
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:
source.data
dict(foo=[11, 12, 30], bar=[101, 200, 301])
For a more comprehensive complete example, see examples/howto/patch_app.py.
remove
Remove a column of data.
name (str) – name of the column to remove
If the column name does not exist, a warning is issued.
stream
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.
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.
new_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)
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
Convert this data source to pandas DataFrame.
column_names
A list of the column names in this data source.
{ "callback": null, "data": {}, "id": "14374", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "selected": { "id": "14376", "type": "Selection" }, "selection_policy": { "id": "14375", "type": "UnionRenderers" }, "subscribed_events": [], "tags": [] }
DataSource
A base class for data source types.
callback
property type: Instance ( Callback )
Callback
A callback to run in the browser whenever the selection is changed.
selected
property type: Instance ( Selection )
Selection
A Selection that indicates selected indices on this DataSource.
{ "callback": null, "id": "14378", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "selected": { "id": "14379", "type": "Selection" }, "subscribed_events": [], "tags": [] }
GeoJSONDataSource
geojson
property type: JSON
JSON
GeoJSON that contains features for plotting. Currently GeoJSONDataSource can only process a FeatureCollection or GeometryCollection.
FeatureCollection
GeometryCollection
{ "callback": null, "geojson": null, "id": "14382", "js_event_callbacks": {}, "js_property_callbacks": {}, "name": null, "selected": { "id": "14384", "type": "Selection" }, "selection_policy": { "id": "14383", "type": "UnionRenderers" }, "subscribed_events": [], "tags": [] }
RemoteSource
Bases: bokeh.models.sources.WebSource
bokeh.models.sources.WebSource
Base class for remote column data sources that can update from data URLs at prescribed time intervals.
This base class is typically not useful to instantiate on its own.
polling_interval
property type: Int
Int
A polling interval (in milliseconds) for updating data source.
{ "adapter": null, "callback": null, "data": {}, "data_url": null, "id": "14386", "js_event_callbacks": {}, "js_property_callbacks": {}, "max_size": null, "mode": "replace", "name": null, "polling_interval": null, "selected": { "id": "14387", "type": "Selection" }, "selection_policy": { "id": "14388", "type": "UnionRenderers" }, "subscribed_events": [], "tags": [] }
ServerSentDataSource
A data source that can populate columns by receiving server sent events endpoints.
{ "adapter": null, "callback": null, "data": {}, "data_url": null, "id": "14390", "js_event_callbacks": {}, "js_property_callbacks": {}, "max_size": null, "mode": "replace", "name": null, "selected": { "id": "14391", "type": "Selection" }, "selection_policy": { "id": "14392", "type": "UnionRenderers" }, "subscribed_events": [], "tags": [] }
WebSource
Bases: bokeh.models.sources.ColumnDataSource
bokeh.models.sources.ColumnDataSource
Base class for web column data sources that can update from data URLs.
property type: 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).
cb_obj
cb_data.response
data_url
A URL to to fetch data from.
max_size
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.
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.
{ "adapter": null, "callback": null, "data": {}, "data_url": null, "id": "14393", "js_event_callbacks": {}, "js_property_callbacks": {}, "max_size": null, "mode": "replace", "name": null, "selected": { "id": "14394", "type": "Selection" }, "selection_policy": { "id": "14395", "type": "UnionRenderers" }, "subscribed_events": [], "tags": [] }