Visual Storytelling: Building a One-Page KPI Dashboard in Python

brief = {
    "audience": "Operations head",
    "question": "Are we growing healthily, and where do we need to act?",
    "kpis": ["Delivered revenue", "Orders", "Avg delivery time", "On-time rate (<=30 min)", "Cancellation rate"],
    "charts": ["Revenue trend", "City ranking", "Delivery time vs target", "Cancellations by city"],
    "action": "Decide which city gets extra riders next month",
}
for k, v in brief.items():
    print(f"{k:<10}{v}")
print(len(brief["kpis"]), len(brief["charts"]))

import pandas as pd
import numpy as np

orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
items = pd.read_csv("order_items.csv")
stores = pd.read_csv("stores.csv")

value = items.assign(v=items["quantity"] * items["unit_price"]).groupby("order_id")["v"].sum().rename("value")
o = orders.merge(value, on="order_id", how="left").merge(stores[["store_id", "city"]], on="store_id")
o["month"] = o["order_date"].dt.to_period("M")

def kpis(d):
    delivered = d[d["order_status"] == "Delivered"]
    return {
        "revenue": delivered["value"].sum(),
        "orders": len(d),
        "avg_minutes": delivered["delivery_minutes"].mean(),
        "on_time": (delivered["delivery_minutes"] <= 30).mean(),
        "cancel_rate": (d["order_status"] == "Cancelled").mean(),
    }

latest, prev = o["month"].max(), o["month"].max() - 1
cur, old = kpis(o[o["month"] == latest]), kpis(o[o["month"] == prev])

for k in cur:
    delta = (cur[k] - old[k]) / old[k] * 100
    print(f"{k:<12}{cur[k]:>12,.2f}{old[k]:>12,.2f}{delta:>8.1f}%")

def inr_short(x):
    if abs(x) >= 1e7:
        return f"₹{x / 1e7:.2f} Cr"
    if abs(x) >= 1e5:
        return f"₹{x / 1e5:.2f} L"
    return f"₹{x:,.0f}"

def pct(x, d=1):
    return f"{x * 100:.{d}f}%"

def delta_text(new, old, good_when_up=True):
    change = (new - old) / old
    arrow = "▲" if change >= 0 else "▼"
    good = (change >= 0) == good_when_up
    return f"{arrow} {abs(change) * 100:.1f}%", ("#2A9D8F" if good else "#E76F51")

print(inr_short(cur["revenue"]), pct(cur["on_time"]), pct(cur["cancel_rate"], 2))
print(delta_text(cur["revenue"], old["revenue"]))
print(delta_text(cur["cancel_rate"], old["cancel_rate"], good_when_up=False))

import matplotlib.pyplot as plt
from matplotlib.patches import FancyBboxPatch
import skillancy_style

tiles = [
    ("Delivered revenue", inr_short(cur["revenue"]), delta_text(cur["revenue"], old["revenue"])),
    ("Orders", f'{cur["orders"]:,}', delta_text(cur["orders"], old["orders"])),
    ("Avg delivery", f'{cur["avg_minutes"]:.1f} min', delta_text(cur["avg_minutes"], old["avg_minutes"], good_when_up=False)),
    ("On-time (<=30 min)", pct(cur["on_time"]), delta_text(cur["on_time"], old["on_time"])),
    ("Cancellations", pct(cur["cancel_rate"]), delta_text(cur["cancel_rate"], old["cancel_rate"], good_when_up=False)),
]

fig, ax = plt.subplots(figsize=(12, 1.9))
ax.set_xlim(0, len(tiles)); ax.set_ylim(0, 1); ax.axis("off")
for i, (label, big, (change, color)) in enumerate(tiles):
    ax.add_patch(FancyBboxPatch((i + 0.04, 0.06), 0.92, 0.88, boxstyle="round,pad=0.0,rounding_size=0.06", fc="#F4F7F8", ec="#D0D5DD"))
    ax.text(i + 0.5, 0.80, label, ha="center", va="center", fontsize=10, color="#5B6770")
    ax.text(i + 0.5, 0.50, big, ha="center", va="center", fontsize=22, fontweight="bold", color="#264653")
    ax.text(i + 0.5, 0.20, f"{change} vs last month", ha="center", va="center", fontsize=9.5, color=color)

from matplotlib.gridspec import GridSpec
import matplotlib.dates as mdates
from matplotlib.ticker import FuncFormatter

delivered = o[o["order_status"] == "Delivered"].copy()
monthly = delivered.groupby(delivered["order_date"].dt.to_period("M").dt.to_timestamp())["value"].sum()
by_city = delivered.groupby("city").agg(revenue=("value", "sum"), avg=("delivery_minutes", "mean"), on_time=("delivery_minutes", lambda s: (s <= 30).mean())).sort_values("revenue")
canc = (o.assign(c=o["order_status"] == "Cancelled").groupby("city")["c"].mean().sort_values())

fig = plt.figure(figsize=(13, 8.2))
gs = GridSpec(3, 6, figure=fig, height_ratios=[0.8, 2.2, 2.2], hspace=0.55, wspace=0.9)

# tiles across the top
tile_gs = gs[0, :].subgridspec(1, 5, wspace=0.15)
tile_axes = [fig.add_subplot(tile_gs[0, i]) for i in range(5)]
for ax_t, (label, big, (change, color)) in zip(tile_axes, tiles):
    ax_t.axis("off")
    ax_t.text(0.5, 0.78, label, ha="center", fontsize=9, color="#5B6770", transform=ax_t.transAxes)
    ax_t.text(0.5, 0.40, big, ha="center", fontsize=16, fontweight="bold", color="#264653", transform=ax_t.transAxes)
    ax_t.text(0.5, 0.05, change, ha="center", fontsize=9, color=color, transform=ax_t.transAxes)

a1 = fig.add_subplot(gs[1, :4])
a1.plot(monthly.index, monthly.values, marker="o", linewidth=2)
a1.yaxis.set_major_formatter(FuncFormatter(lambda x, p: inr_short(x)))
a1.xaxis.set_major_formatter(mdates.DateFormatter("%b '%y"))
a1.set_title("Revenue is growing every quarter", loc="left")

a2 = fig.add_subplot(gs[1, 4:])
bars = a2.barh(by_city.index, by_city["revenue"] / 1e5, color="#028090")
a2.set_title("Delhi and Bengaluru lead", loc="left")
a2.set_xlabel("Revenue (₹ lakh)")
a2.grid(axis="y", visible=False)

a3 = fig.add_subplot(gs[2, :3])
a3.bar(by_city.index, by_city["avg"], color=["#E76F51" if v == by_city["avg"].max() else "#8D99AE" for v in by_city["avg"]])
a3.axhline(30, color="#264653", linestyle="--")
a3.text(-0.4, 30.4, "service target 30 min", fontsize=8, color="#264653")
a3.set_ylim(0, 34)
a3.set_title("Every city meets the 30-minute target", loc="left")
a3.tick_params(axis="x", rotation=45, labelsize=8)
a3.set_ylabel("Avg minutes")

a4 = fig.add_subplot(gs[2, 3:])
a4.barh(canc.index, canc.values * 100, color="#E76F51")
a4.set_title("Cancellations are 5-7% everywhere", loc="left")
a4.set_xlabel("Cancellation rate (%)")
a4.grid(axis="y", visible=False)

fig.suptitle("FreshBasket - Operations dashboard, latest month and trend", x=0.01, ha="left", fontsize=15, fontweight="bold")

city_stats = by_city.copy()
city_stats["late_pct"] = 1 - city_stats["on_time"]
city_stats = city_stats.sort_values("late_pct")
worst = city_stats.index[-1]

fig, ax = plt.subplots(figsize=(8, 4.2))
colors = ["#E76F51" if c == worst else "#C5CCD3" for c in city_stats.index]
bars = ax.barh(city_stats.index, city_stats["late_pct"] * 100, color=colors)
ax.bar_label(bars, labels=[f"{v * 100:.1f}%" for v in city_stats["late_pct"]], padding=3, fontsize=9)
ax.set_xlabel("Deliveries slower than 30 minutes (%)")
ax.set_title(f"{worst} is slowest to deliver on time, but only narrowly", loc="left")
ax.grid(axis="y", visible=False)
ax.set_xlim(0, city_stats["late_pct"].max() * 100 * 1.2)

fig, ax = plt.subplots(figsize=(9, 4.2))
ax.plot(monthly.index, monthly.values, linewidth=2.5, color="#028090")
ax.fill_between(monthly.index, monthly.values, alpha=0.12, color="#028090")
ax.yaxis.set_major_formatter(FuncFormatter(lambda x, p: inr_short(x)))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b '%y"))

first, last = monthly.iloc[0], monthly.iloc[-1]
ax.annotate(inr_short(first), (monthly.index[0], first), xytext=(10, 30), textcoords="offset points", arrowprops=dict(arrowstyle="-", color="#8D99AE"))
ax.annotate(inr_short(last), (monthly.index[-1], last), xytext=(-60, 5), textcoords="offset points", fontweight="bold", color="#028090")
ax.set_title(f"Monthly delivered revenue grew {last / first:.0f}x in {len(monthly) - 1} months", loc="left")
ax.set_ylim(bottom=0)
fig.autofmt_xdate()

from matplotlib.backends.backend_pdf import PdfPages
from pathlib import Path

Path("out").mkdir(exist_ok=True)

fig.savefig("out/trend.png", dpi=200, bbox_inches="tight", facecolor="white")

with PdfPages("out/report.pdf") as pdf:
    for title, vals in [("Revenue by month", monthly), ("Late share by city", city_stats["late_pct"])]:
        f, a = plt.subplots(figsize=(8.27, 5))
        a.plot(range(len(vals)), vals.values, marker="o")
        a.set_title(title)
        pdf.savefig(f, bbox_inches="tight")
        plt.close(f)

for p in sorted(Path("out").glob("*")):
    if p.name in ("trend.png", "report.pdf"):
        print(p.name, p.stat().st_size > 2000)

def month_summary(df, month):
    month = pd.Period(month)
    cur = kpis(df[df["month"] == month])
    old = kpis(df[df["month"] == month - 1])
    lines = []
    for key, label in [("revenue", "Delivered revenue"), ("orders", "Orders"), ("on_time", "On-time rate")]:
        text, _ = delta_text(cur[key], old[key])
        value_txt = inr_short(cur[key]) if key == "revenue" else (pct(cur[key]) if key == "on_time" else f"{cur[key]:,}")
        lines.append(f"{label}: {value_txt} ({text} vs {month - 1})")
    return lines

for m in ["2026-04", "2026-05", "2026-06"]:
    print(m, month_summary(o, m)[0])

print(month_summary(o, "2026-06")[2])

checklist = [
    ("Each chart title states a finding", True),
    ("Axes labelled with units", True),
    ("Bars start at zero", True),
    ("One accent colour, grey for context", True),
    ("Numbers formatted for the audience (lakh/crore)", True),
    ("Every title claim checked against data", True),
    ("Source and date shown", False),
]
done = sum(ok for _, ok in checklist)
for item, ok in checklist:
    print("[x]" if ok else "[ ]", item)
print(done, len(checklist))

fig, ax = plt.subplots()
ax.set_title(f"Revenue grew {int(last) // 0}x")