API Reference¶

DataFrame¶

class cuxfilter.dataframe.DataFrame(data)¶

A cuxfilter GPU DataFrame object

Attributes

data

Methods

`dashboard`(self, charts[, layout, theme, …])	Creates a cuxfilter.DashBoard object
`from_arrow`(dataframe_location)	read an arrow file from disk as cuxfilter.DataFrame
`from_dataframe`(dataframe)	create a cuxfilter.DataFrame from cudf.DataFrame (zero-copy reference)

dashboard(self, charts: list, layout=<class 'cuxfilter.layouts.layouts.Layout0'>, theme=<class 'cuxfilter.themes.light.Theme'>, title='Dashboard', data_size_widget=True, warnings=False)¶

Creates a cuxfilter.DashBoard object

Parameters

charts: list: list of cuxfilter.charts
layout: cuxfilter.layouts
title: str: title of the dashboard, default “Dashboard”
data_size_widget: boolean: flag to determine whether to diplay the current datapoints selected in the dashboard, default True
warnings: boolean: flag to disable or enable runtime warnings related to layouts, default False

Returns

cuxfilter.DashBoard object

Examples

>>> import cudf
>>> import cuxfilter
>>> from cuxfilter.charts import bokeh
>>> df = cudf.DataFrame(
>>>     {
>>>         'key': [0, 1, 2, 3, 4],
>>>         'val':[float(i + 10) for i in range(5)]
>>>     }
>>> )
>>> cux_df = cuxfilter.DataFrame.from_dataframe(df)
>>> line_chart_1 = bokeh.line(
>>>     'key', 'val', data_points=5, add_interaction=False
>>> )

>>> # create a dashboard object
>>> d = cux_df.dashboard([line_chart_1])

classmethod from_arrow(dataframe_location)¶

read an arrow file from disk as cuxfilter.DataFrame

Parameters

dataframe_location: str or arrow in-memory table

Returns

cuxfilter.DataFrame object

Examples

Read dataframe as an arrow file from disk

>>> import cuxfilter
>>> cux_df = cuxfilter.DataFrame.from_arrow(
    './location/of/dataframe.arrow'
    )

classmethod from_dataframe(dataframe)¶

create a cuxfilter.DataFrame from cudf.DataFrame (zero-copy reference)

Parameters

dataframe_location: cudf.DataFrame

Returns

cuxfilter.DataFrame object

Examples

Read dataframe from a cudf.DataFrame

>>> import cuxfilter
>>> import cudf
>>> cudf_df = cudf.DataFrame(
>>>     {
>>>         'key': [0, 1, 2, 3, 4],
>>>         'val':[float(i + 10) for i in range(5)]
>>>     }
>>> )
>>> cux_df = cuxfilter.DataFrame.from_dataframe(cudf_df)

DashBoard¶

class cuxfilter.dashboard.DashBoard(charts=[], data=None, layout=<class 'cuxfilter.layouts.layouts.Layout0'>, theme=<class 'cuxfilter.themes.light.Theme'>, title='Dashboard', data_size_widget=True, warnings=False)¶

A cuxfilter GPU DashBoard object. Examples ——–

Create a dashboard

>>> import cudf
>>> import cuxfilter
>>> from cuxfilter.charts import bokehfrom cuxfilter.charts import bokeh
>>> df = cudf.DataFrame(
>>>     {'key': [0, 1, 2, 3, 4], 'val':[float(i + 10) for i in range(5)]}
>>> )
>>> cux_df = cuxfilter.DataFrame.from_dataframe(df)
>>> line_chart_1 = bokeh.line(
>>>     'key', 'val', data_points=5, add_interaction=False
>>> )
>>> line_chart_2 = bokeh.bar(
>>>     'val', 'key', data_points=5, add_interaction=False
>>> )
>>> d = cux_df.dashboard([line_chart_1, line_chart_2])
>>> d
`cuxfilter DashBoard
[title] Markdown(str)
[chart1] Column(sizing_mode='scale_both', width=1600)
    [0] Bokeh(Figure)
[chart2] Column(sizing_mode='scale_both', width=1600)
    [0] Bokeh(Figure)`

Attributes

charts: Charts in the dashboard as a dictionary.
data_size_widget: Data_size_widget flag.
title: Title of the dashboard.
warnings: Layout warnings flag.

Methods

`add_charts`(self[, charts])	Adding more charts to the dashboard, after it has been initialized.
`app`(self[, notebook_url])	Run the dashboard with a bokeh backend server within the notebook.
`export`(self)	Export the cudf.DataFrame based on the current filtered state of the dashboard.
`preview`(self)	Preview(Async) all the charts in a jupyter cell, non interactive(no backend server).
`show`(self[, notebook_url, port, threaded])	Run the dashboard with a bokeh backend server within the notebook.
`stop`(self)	stop the bokeh server

add_charts(self, charts=[])¶

Adding more charts to the dashboard, after it has been initialized. Parameters ———- charts: list

list of cuxfilter.charts objects

Notes

After adding the charts, re-run the dashboard.app() or dashboard.show() cell to see the updated charts.

Examples

>>> import cudf
>>> import cuxfilter
>>> from cuxfilter.charts import bokeh
>>> df = cudf.DataFrame(
>>>     {
>>>         'key': [0, 1, 2, 3, 4],
>>>         'val':[float(i + 10) for i in range(5)]
>>>     }
>>> )
>>> cux_df = cuxfilter.DataFrame.from_dataframe(df)
>>> line_chart_1 = bokeh.line(
>>>     'key', 'val', data_points=5, add_interaction=False
>>> )
>>> d = cux_df.dashboard([line_chart_1])
>>> line_chart_2 = bokeh.bar(
>>>     'val', 'key', data_points=5, add_interaction=False
>>> )
>>> d.add_charts([line_chart_2])

app(self, notebook_url='', port: int = 0)¶

Run the dashboard with a bokeh backend server within the notebook. Parameters ———- url: str, optional

url of the notebook(including the port). Can use localhost instead of ip if running locally

port: int, optional: Port number bokeh uses for it’s two communication protocol. Default is random open port. Recommended to set this value if running jupyter remotely and only few ports are exposed.

Examples

>>> import cudf
>>> import cuxfilter
>>> from cuxfilter.charts import bokeh
>>> df = cudf.DataFrame(
>>>     {
>>>         'key': [0, 1, 2, 3, 4],
>>>         'val':[float(i + 10) for i in range(5)]
>>>     }
>>> )
>>> cux_df = cuxfilter.DataFrame.from_dataframe(df)
>>> line_chart_1 = bokeh.line(
>>>     'key', 'val', data_points=5, add_interaction=False
>>> )
>>> d = cux_df.dashboard([line_chart_1])
>>> d.app(notebook_url='localhost:8888')

property charts¶: Charts in the dashboard as a dictionary.

property data_size_widget¶: Data_size_widget flag.

export(self)¶

Export the cudf.DataFrame based on the current filtered state of the dashboard.

Also prints the query string of the current state of the dashboard. Returns ——- cudf.DataFrame

Examples

>>> import cudf
>>> import cuxfilter
>>> from cuxfilter.charts import bokeh
>>> df = cudf.DataFrame(
>>>     {
>>>         'key': [0, 1, 2, 3, 4],
>>>         'val':[float(i + 10) for i in range(5)]
>>>     }
>>> )
>>> cux_df = cuxfilter.DataFrame.from_dataframe(df)
>>> line_chart_1 = bokeh.line(
>>>     'key', 'val', data_points=5, add_interaction=False
>>> )
>>> line_chart_2 = bokeh.bar(
>>>     'val', 'key', data_points=5, add_interaction=False
>>> )
>>> d = cux_df.dashboard([line_chart_1, line_chart_2])
>>> d.app() #or d.show()
displays dashboard
do some visual querying/ crossfiltering

>>> queried_df = d.export()
final query 2<=key<=4

async preview(self)¶

Preview(Async) all the charts in a jupyter cell, non interactive(no backend server). Mostly intended to save notebook state for blogs, documentation while still rendering the dashboard.

Notes

Png format

Examples

>>> import cudf
>>> import cuxfilter
>>> from cuxfilter.charts import bokeh
>>> df = cudf.DataFrame(
>>>     {
>>>         'key': [0, 1, 2, 3, 4],
>>>         'val':[float(i + 10) for i in range(5)]
>>>     }
>>> )
>>> cux_df = cuxfilter.DataFrame.from_dataframe(df)
>>> line_chart_1 = bokeh.line(
>>>     'key', 'val', data_points=5, add_interaction=False
>>> )
>>> line_chart_2 = bokeh.bar(
>>>     'val', 'key', data_points=5, add_interaction=False
>>> )
>>> d = cux_df.dashboard([line_chart_1, line_chart_2])
>>> await d.preview()
displays charts in the dashboard

show(self, notebook_url='', port=0, threaded=False, **kwargs)¶

Run the dashboard with a bokeh backend server within the notebook. Parameters ———- url: str, optional

URL where you want to run the dashboard as a web-app,

including the port number. - Can use localhost instead of ip if running locally. - Has to be an open port.

Examples

>>> import cudf
>>> import cuxfilter
>>> from cuxfilter.charts import bokeh
>>> df = cudf.DataFrame(
>>>     {
>>>         'key': [0, 1, 2, 3, 4],
>>>         'val':[float(i + 10) for i in range(5)]
>>>     }
>>> )
>>> cux_df = cuxfilter.DataFrame.from_dataframe(df)
>>> line_chart_1 = bokeh.line(
>>>     'key', 'val', data_points=5, add_interaction=False
>>> )
>>> d = cux_df.dashboard([line_chart_1])
>>> d.show(url='localhost:8889')

stop(self)¶: stop the bokeh server

property title¶: Title of the dashboard.

property warnings¶: Layout warnings flag.