How to Build a Fully Interactive, Real-Time Visualization Dashboard Using Bokeh and Custom JavaScript?
In this tutorial, we create a absolutely interactive, visually compelling information visualization dashboard utilizing Bokeh. We begin by turning uncooked information into insightful plots, then improve them with options resembling linked brushing, colour gradients, and real-time filters powered by dropdowns and sliders. As we progress, we convey our dashboard to life with Custom JavaScript (CustomJS) interactivity, enabling immediate browser-side responses with out a single Python callback. By mixing the most effective of Python’s analytical power with JavaScript’s responsiveness, we construct a seamless, dynamic dashboard expertise that redefines how we visualize and work together with information. Check out the FULL CODES here.
!pip set up bokeh pandas numpy scipy -q
import numpy as np
import pandas as pd
from bokeh.io import output_notebook, present, export_png, output_file
from bokeh.plotting import determine
from bokeh.layouts import row, column, gridplot
from bokeh.fashions import (
ColumnDataSupply, HoverTool, LassoSelectSoftware, FieldSelectSoftware, TapTool,
ColorBar, LinearColorMapper, BasicTicker, PrintfTickFormatter, Slider,
Select, CheckboxGroup, CustomJS, CDSView, BooleanFilter, Div, Button
)
from bokeh.palettes import Viridis256
from bokeh.fashions.widgets import DataTable, TableColumn
output_notebook()
np.random.seed(42)
N = 300
information = pd.DataBody({
"temp_c": 20 + 5 * np.random.randn(N),
"pressure_kpa": 101 + 3 * np.random.randn(N),
"humidity_pct": 40 + 15 * np.random.randn(N),
"sensor_id": np.random.selection(["A1","A2","B7","C3"], dimension=N),
"timestep": np.arange(N)
})
source_main = ColumnDataSupply(information)
p_scatter = determine(title="Temperature vs Pressure", width=400, top=300,
x_axis_label="Temperature (°C)", y_axis_label="Pressure (kPa)",
instruments="pan,wheel_zoom,reset")
scat = p_scatter.circle(x="temp_c", y="pressure_kpa", dimension=8, fill_alpha=0.6,
fill_color="orange", line_color="black", supply=source_main,
legend_label="Sensor Readings")
hover = HoverTool(tooltips=[
("Temp (°C)", "@temp_c{0.0}"), ("Pressure", "@pressure_kpa{0.0} kPa"),
("Humidity", "@humidity_pct{0.0}%"), ("Sensor", "@sensor_id"),
("Timestep", "@timestep")], renderers=[scat])
p_scatter.add_tools(hover)
p_scatter.legend.location = "top_left"
present(p_scatter)We start by establishing our surroundings and importing all the mandatory libraries. We then create a artificial dataset and visualize temperature in opposition to strain utilizing a easy scatter plot with hover performance. This helps us set up a basis for our interactive dashboard. Check out the FULL CODES here.
p_humidity = determine(title="Humidity vs Temperature (Linked Selection)", width=400, top=300,
x_axis_label="Temperature (°C)", y_axis_label="Humidity (%)",
instruments="pan,wheel_zoom,reset,box_select,lasso_select,faucet")
r2 = p_humidity.sq.(x="temp_c", y="humidity_pct", dimension=8, fill_alpha=0.6,
fill_color="navy", line_color="white", supply=source_main)
p_humidity.add_tools(HoverTool(tooltips=[
("Temp (°C)", "@temp_c{0.0}"), ("Humidity", "@humidity_pct{0.0}%"),
("Sensor", "@sensor_id")], renderers=[r2]))
layout_linked = row(p_scatter, p_humidity)
present(layout_linked)We prolong our visualization by including one other plot that hyperlinks humidity and temperature via shared information. We use linked brushing in order that alternatives in a single plot routinely mirror within the different, serving to us analyze relationships throughout a number of variables concurrently. Check out the FULL CODES here.
color_mapper = LinearColorMapper(palette=Viridis256, low=information["humidity_pct"].min(),
excessive=information["humidity_pct"].max())
p_color = determine(title="Pressure vs Humidity (Colored by Humidity)", width=500, top=350,
x_axis_label="Pressure (kPa)", y_axis_label="Humidity (%)",
instruments="pan,wheel_zoom,reset,box_select,lasso_select")
r3 = p_color.circle(x="pressure_kpa", y="humidity_pct", dimension=8, fill_alpha=0.8,
line_color=None, colour={"subject": "humidity_pct", "rework": color_mapper},
supply=source_main)
color_bar = ColorBar(color_mapper=color_mapper, ticker=BasicTicker(desired_num_ticks=5),
formatter=PrintfTickFormatter(format="%4.1f%%"), label_standoff=8,
border_line_color=None, location=(0,0), title="Humidity %")
p_color.add_layout(color_bar, "proper")
present(p_color)We improve our visualization by introducing a steady colour mapping function to characterize humidity ranges. By including a colour bar and gradient, we make our chart extra informative and intuitive, permitting us to interpret variations visually. Check out the FULL CODES here.
sensor_options = sorted(information["sensor_id"].distinctive().tolist())
sensor_select = Select(title="Sensor ID Filter", worth=sensor_options[0], choices=sensor_options)
temp_slider = Slider(title="Max Temperature (°C)",
begin=float(information["temp_c"].min()),
finish=float(information["temp_c"].max()), step=0.5,
worth=float(information["temp_c"].max()))
columns_available = ["temp_c", "pressure_kpa", "humidity_pct", "sensor_id", "timestep"]
checkbox_group = CheckboxGroup(labels=columns_available,
energetic=record(vary(len(columns_available))))
def filter_mask(sensor_val, max_temp):
return [(s == sensor_val) and (t <= max_temp)
for s, t in zip(data["sensor_id"], information["temp_c"])]
bool_filter = BooleanFilter(filter_mask(sensor_select.worth, temp_slider.worth))
view = CDSView(filter=bool_filter)
p_filtered = determine(title="Filtered: Temp vs Pressure", width=400, top=300,
x_axis_label="Temp (°C)", y_axis_label="Pressure (kPa)",
instruments="pan,wheel_zoom,reset,box_select,lasso_select")
r_filtered = p_filtered.circle(x="temp_c", y="pressure_kpa", dimension=8, fill_alpha=0.7,
fill_color="firebrick", line_color="white",
supply=source_main, view=view)
p_filtered.add_tools(HoverTool(tooltips=[
("Temp", "@temp_c{0.0}"), ("Pressure", "@pressure_kpa{0.0}"),
("Humidity", "@humidity_pct{0.0}%"), ("Sensor", "@sensor_id")],
renderers=[r_filtered]))
def make_table_src(cols):
return ColumnDataSupply(information[cols])
table_src = make_table_src(columns_available)
table_columns = [TableColumn(field=c, title=c) for c in columns_available]
table_widget = DataTable(supply=table_src, columns=table_columns, width=500, top=200)
def update_filters(attr, previous, new):
bool_filter.booleans = filter_mask(sensor_select.worth, temp_slider.worth)
def update_table(attr, previous, new):
active_cols = [columns_available[i] for i in checkbox_group.energetic]
new_src = make_table_src(active_cols)
table_widget.supply.information = new_src.information
table_widget.columns = [TableColumn(field=c, title=c) for c in active_cols]
sensor_select.on_change("worth", update_filters)
temp_slider.on_change("worth", update_filters)
checkbox_group.on_change("energetic", update_table)
dashboard_controls = column(Div(textual content="<b>Interactive Filters</b>"), sensor_select,
temp_slider, Div(textual content="<b>Columns in Table</b>"), checkbox_group)
dashboard_layout = row(column(p_filtered, table_widget), dashboard_controls)
present(dashboard_layout)We introduce interactivity via widgets resembling dropdowns, sliders, and checkboxes. We dynamically filter information and replace tables in actual time, enabling us to simply discover totally different subsets and attributes of the dataset. Check out the FULL CODES here.
mini_source = ColumnDataSupply({
"x": np.linspace(0, 2*np.pi, 80),
"y": np.sin(np.linspace(0, 2*np.pi, 80))
})
p_wave = determine(title="Sine Wave (CustomJS: Enlarge factors)", width=400, top=250,
instruments="pan,wheel_zoom,reset")
wave_render = p_wave.circle(x="x", y="y", dimension=6, fill_alpha=0.8,
fill_color="inexperienced", line_color="black", supply=mini_source)
js_callback = CustomJS(args=dict(r=wave_render),
code="const new_size = r.glyph.dimension.worth + 2; r.glyph.dimension = new_size;")
grow_button = Button(label="Enlarge factors (CustomJS)", button_type="success")
grow_button.js_on_click(js_callback)
present(column(p_wave, grow_button))We implement a JavaScript-based interplay utilizing Bokeh’s CustomJS. We create a sine wave visualization and enable customers to enlarge the plot markers with a button click on, demonstrating client-side management with none Python callbacks. Check out the FULL CODES here.
stream_source = ColumnDataSupply({"t": [], "val": []})
p_stream = determine(title="Streaming Sensor Value", width=500, top=250,
x_axis_label="timestep", y_axis_label="worth",
instruments="pan,wheel_zoom,reset")
p_stream.line(x="t", y="val", supply=stream_source, line_width=3, line_alpha=0.8)
p_stream.circle(x="t", y="val", supply=stream_source, dimension=6, fill_color="pink")
present(p_stream)
for t in vary(10):
new_point = {"t": [t], "val": [np.sin(t/2) + 0.2*np.random.randn()]}
stream_source.stream(new_point, rollover=200)
present(p_stream)We simulate a dwell information stream by repeatedly including new information factors to our plot. We watch the visualization replace dynamically, showcasing how Bokeh can deal with real-time information and present immediate visible suggestions.
In conclusion, we create a absolutely practical, real-time, and browser-interactive dashboard that showcases the total potential of Bokeh. We find out how to visualize a number of dimensions of knowledge, dynamically filter and replace visuals, and even harness JavaScript integration to make immediate, client-side updates instantly inside the browser. This hands-on expertise exhibits us how Bokeh effortlessly merges Python and JavaScript, empowering us to design dashboards that aren’t simply interactive however clever, responsive, and production-ready.
Check out the FULL CODES here. Feel free to take a look at our GitHub Page for Tutorials, Codes and Notebooks. Also, be happy to observe us on Twitter and don’t neglect to be part of our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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