How to Design an Advanced Multi-Page Interactive Analytics Dashboard with Dynamic Filtering, Live KPIs, and Rich Visual Exploration Using Panel

import sys, subprocess


def install_deps():
   pkgs = ["panel", "hvplot", "pandas", "numpy", "bokeh"]
   subprocess.check_call([sys.executable, "-m", "pip", "install", "-q"] + pkgs)


try:
   import panel as pn
   import hvplot.pandas
   import pandas as pd
   import numpy as np
except ImportError:
   install_deps()
   import panel as pn
   import hvplot.pandas
   import pandas as pd
   import numpy as np


pn.extension()


rng = np.random.default_rng(42)
dates = pd.date_range("2024-01-01", periods=365, freq="D")
segments = ["A", "B", "C"]
regions = ["North", "South", "East", "West"]


base = pd.DataFrame(
   {
       "date": np.tile(dates, len(segments) * len(regions)),
       "segment": np.repeat(segments, len(dates) * len(regions)),
       "region": np.repeat(np.tile(regions, len(segments)), len(dates)),
   }
)
base["traffic"] = (
   100
   + 40 * np.sin(2 * np.pi * base["date"].dt.dayofyear / 365)
   + rng.normal(0, 15, len(base))
)
trend = {"A": 1.0, "B": 1.5, "C": 2.0}
base["traffic"] *= base["segment"].map(trend)
base["conversions"] = (base["traffic"] * rng.uniform(0.01, 0.05, len(base))).astype(int)
base["revenue"] = base["conversions"] * rng.uniform(20, 60, len(base))
df = base.reset_index(drop=True)

segment_sel = pn.widgets.CheckBoxGroup(name="Segment", value=segments[:2], options=segments, inline=True)
region_sel = pn.widgets.MultiChoice(name="Region", value=["North"], options=regions)
metric_sel = pn.widgets.Select(name="Metric", value="traffic", options=["traffic", "conversions", "revenue"])
date_range = pn.widgets.DateRangeSlider(
   name="Date Range",
   start=df["date"].min(),
   end=df["date"].max(),
   value=(df["date"].min(), df["date"].max()),
)
smooth_slider = pn.widgets.IntSlider(name="Rolling Window (days)", start=1, end=30, value=7)


def filtered_df(segment, region, drange):
   d1, d2 = drange
   mask = (
       df["segment"].isin(segment)
       & df["region"].isin(region or regions)
       & (df["date"] >= d1)
       & (df["date"] <= d2)
   )
   sub = df[mask].copy()
   if sub.empty:
       return df.iloc[:0]
   return sub


@pn.depends(segment_sel, region_sel, metric_sel, smooth_slider, date_range)
def timeseries_plot(segment, region, metric, window, drange):
   data = filtered_df(segment, region, drange)
   if data.empty:
       return pn.pane.Markdown("### No data for current filters")
   grouped = data.sort_values("date").groupby("date")[metric].sum()
   line = grouped.hvplot.line(title=f"{metric.title()} over time", ylabel=metric.title())
   if window > 1:
       smooth = grouped.rolling(window).mean().hvplot.line(line_width=3, alpha=0.6)
       return (line * smooth).opts(legend_position="top_left")
   return line

@pn.depends(segment_sel, region_sel, metric_sel, date_range)
def segment_bar(segment, region, metric, drange):
   data = filtered_df(segment, region, drange)
   if data.empty:
       return pn.pane.Markdown("### No data to aggregate")
   agg = data.groupby("segment")[metric].sum().sort_values(ascending=False)
   return agg.hvplot.bar(title=f"{metric.title()} by Segment", yaxis=None)


@pn.depends(segment_sel, region_sel, metric_sel, date_range)
def region_heatmap(segment, region, metric, drange):
   data = filtered_df(segment, region, drange)
   if data.empty:
       return pn.pane.Markdown("### No data to aggregate")
   pivot = data.pivot_table(index="segment", columns="region", values=metric, aggfunc="sum")
   return pivot.hvplot.heatmap(title=f"{metric.title()} Heatmap", clabel=metric.title())

kpi_source = df.copy()
kpi_idx = [0]


def compute_kpi(slice_df):
   if slice_df.empty:
       return 0, 0, 0
   total_rev = slice_df["revenue"].sum()
   avg_conv = slice_df["conversions"].mean()
   cr = (slice_df["conversions"].sum() / slice_df["traffic"].sum()) * 100
   return total_rev, avg_conv, cr


kpi_value = pn.indicators.Number(name="Total Revenue (window)", value=0, format="$0,0")
conv_value = pn.indicators.Number(name="Avg Conversions", value=0, format="0.0")
cr_value = pn.indicators.Number(name="Conversion Rate", value=0, format="0.00%")


def update_kpis():
   step = 200
   start = kpi_idx[0]
   end = start + step
   if start >= len(kpi_source):
       kpi_idx[0] = 0
       start, end = 0, step
   window_df = kpi_source.iloc[start:end]
   kpi_idx[0] = end
   total_rev, avg_conv, cr = compute_kpi(window_df)
   kpi_value.value = total_rev
   conv_value.value = avg_conv
   cr_value.value = cr / 100


pn.state.add_periodic_callback(update_kpis, period=1000, start=True)

controls = pn.WidgetBox(
   "### Global Controls",
   segment_sel,
   region_sel,
   metric_sel,
   date_range,
   smooth_slider,
   sizing_mode="stretch_width",
)


page_overview = pn.Column(
   pn.pane.Markdown("## Overview: Filtered Time Series"),
   controls,
   timeseries_plot,
)


page_insights = pn.Column(
   pn.pane.Markdown("## Segment & Region Insights"),
   pn.Row(segment_bar, region_heatmap),
)


page_live = pn.Column(
   pn.pane.Markdown("## Live KPI Window (simulated streaming)"),
   pn.Row(kpi_value, conv_value, cr_value),
)


dashboard = pn.Tabs(
   ("Overview", page_overview),
   ("Insights", page_insights),
   ("Live KPIs", page_live),
)


dashboard