Bokeh Charts

Bar Chart

bokeh.bar(x, y=None, data_points=100, add_interaction=True, aggregate_fn='count', width=400, height=400, step_size=None, step_size_type=<class 'int'>, **library_specific_params)

Parameters

x: str

x-axis column name from the gpu dataframe

y: str, default None

y-axis column name from the gpu dataframe

data_points: int, default 100

add_interaction: {True, False}, default True

aggregate_fn: {‘count’, ‘mean’}, default ‘count’

width: int, default 400

height: int, default 400

step_size: int, default 1

step_size_type: {int, float}, default int

x_label_map: dict, default None

label maps for x axis {value: mapped_str}

y_label_map: dict, default None

label maps for y axis {value: mapped_str}

title: str,

chart title

**library_specific_params:

additional library specific keyword arguments to be passed to the function

Returns

A bokeh chart object of type vbar

Example

import cudf
from cuxfilter import DataFrame
from cuxfilter.charts import bokeh

cux_df = DataFrame.from_dataframe(cudf.DataFrame({'key': [0, 1, 2, 3, 4], 'val':[float(i + 10) for i in range(5)]}))
bar_chart_1 = bokeh.bar('key', 'val', data_points=5, add_interaction=False)

d = cux_df.dashboard([bar_chart_1])
#view the individual bar chart part of the dashboard d
bar_chart_1.view()

Line Chart

bokeh.line(x, y=None, data_points=100, add_interaction=True, aggregate_fn='count', width=400, height=400, step_size=None, step_size_type=<class 'int'>, **library_specific_params)

Parameters

x: str

x-axis column name from the gpu dataframe

y: str, default None

y-axis column name from the gpu dataframe

data_points: int, default 100

add_interaction: {True, False}, default True

aggregate_fn: {‘count’, ‘mean’}, default ‘count’

width: int, default 400

height: int, default 400

step_size: int, default 1

step_size_type: {int, float}, default int

x_label_map: dict, default None

label maps for x axis {value: mapped_str}

y_label_map: dict, default None

label maps for y axis {value: mapped_str}

title: str,

chart title

**library_specific_params:

additional library specific keyword arguments to be passed to the function

Returns

A bokeh chart object of type line

Example

import cudf
from cuxfilter import DataFrame
from cuxfilter.charts import bokeh

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

d = cux_df.dashboard([line_chart_1])
#view the individual bar chart part of the dashboard d
line_chart_1.view()

Choropleth Chart

bokeh.choropleth(x, y=None, data_points=100, add_interaction=True, aggregate_fn='count', width=800, height=400, step_size=None, step_size_type=<class 'int'>, geoJSONSource=None, geoJSONProperty=None, geo_color_palette=None, tile_provider=None, **library_specific_params)

Parameters

x: str

x-axis column name from the gpu dataframe

y: str, default None

y-axis column name from the gpu dataframe

data_points: int, default 100

add_interaction: {True, False}, default True

aggregate_fn: {‘count’, ‘mean’}, default ‘count’

defaults to ‘count’

width: int, default 800

height: int, default 400

step_size: int, default 1

step_size_type: {int, float}, default int

x_label_map: dict, default None

label maps for x axis {value: mapped_str}

y_label_map: dict, default None

label maps for y axis {value: mapped_str}

geoJSONSource: str

url to the geoJSON file

geoJSONProperty: str, optional

Property to use while doing aggregation operations using the geoJSON file. Defaults to the first value in properties in geoJSON file.

geo_color_palette: bokeh.palette, default bokeh.palettes.Inferno256

nan_color: str, default white

color of the patches of value NaN in the map.

tile_provider: bokeh.tile_provider object or

cuxfilter.assets.custom_tiles.get_provider object, default None

title: str,

chart title

**library_specific_params:

additional library specific keyword arguments to be passed to the function

Returns

A bokeh chart object of type choropleth

Example

import cudf
from cuxfilter import DataFrame
from cuxfilter.charts import bokeh

cux_df = DataFrame.from_dataframe(cudf.DataFrame({'states': [i for i in range(57)], 'val':[float(i + 10) for i in range(57)]}))
choropleth_chart_1 = bokeh.choropleth(x = 'states', y = 'val', aggregate_fn='mean', data_points=57, add_interaction=False,
                                    geoJSONSource= 'https://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_500k.json', geoJSONProperty='STATE',
                                    )

d = cux_df.dashboard([choropleth_chart_1])
#view the individual bar chart part of the dashboard d
choropleth_chart_1.view()