jonas
71803418e5
Shelly device management app with mDNS/subnet discovery, inventory, configuration, and mass operations for Gen1/Gen2+ devices. Includes .gitignore excluding runtime data (device DB, user config), AI conversation history, build artifacts, and common Python/OS patterns.
366 lines
12 KiB
Python
366 lines
12 KiB
Python
"""
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Company Analytics Dashboard Template
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A company leaderboard dashboard demonstrating:
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- Interactive dataframe with sparkline columns
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- Segmented control for ranking (top spenders, gainers, shrinkers)
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- Multi-select pills for account type filtering
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- Time window filtering
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- Growth score calculation
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- Dialog popup for company details
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This template uses synthetic data. Replace generate_company_data()
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with your actual data source (e.g., Snowflake queries, CRM APIs, etc.)
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"""
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from datetime import date, timedelta
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import numpy as np
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import pandas as pd
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import streamlit as st
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import altair as alt
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st.set_page_config(
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page_title="Company Analytics",
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page_icon=":material/business:",
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layout="wide",
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)
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# =============================================================================
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# Synthetic Data Generation (Replace with your data source)
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# =============================================================================
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COMPANY_NAMES = [
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"Acme Corp", "TechFlow Inc", "DataDriven Co", "CloudFirst Ltd",
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"InnovateTech", "ScaleUp Systems", "PrimeData Inc", "FutureStack",
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"ByteWise Corp", "StreamLine Co", "Quantum Labs", "NexGen Solutions",
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"AlphaMetrics", "BetaAnalytics", "GammaInsights", "DeltaData",
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"OmegaTech", "SigmaSoft", "ThetaCloud", "ZetaDigital",
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]
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ACCOUNT_TYPES = ["Enterprise", "Growth", "Startup", "Trial", "Internal"]
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REGIONS = ["North America", "EMEA", "APAC", "LATAM"]
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SEGMENTS = ["Technology", "Finance", "Healthcare", "Retail", "Manufacturing"]
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@st.cache_data(ttl=3600)
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def generate_company_data(days: int = 90) -> pd.DataFrame:
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"""Generate synthetic company usage data.
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Replace this function with your actual data source.
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"""
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np.random.seed(42)
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end_date = date.today() - timedelta(days=1)
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start_date = end_date - timedelta(days=days)
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dates = pd.date_range(start=start_date, end=end_date, freq="D")
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records = []
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for company in COMPANY_NAMES:
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# Assign static attributes
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account_type = np.random.choice(ACCOUNT_TYPES, p=[0.3, 0.25, 0.2, 0.15, 0.1])
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region = np.random.choice(REGIONS)
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segment = np.random.choice(SEGMENTS)
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# Generate usage pattern
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base_usage = np.random.randint(100, 10000)
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growth = np.random.uniform(-0.005, 0.01) # Some companies shrink
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for i, dt in enumerate(dates):
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# Base trend
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trend = base_usage * (1 + growth) ** i
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# Weekly seasonality
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if dt.dayofweek >= 5:
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trend *= 0.3
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# Random noise
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daily_credits = max(0, trend * np.random.uniform(0.7, 1.3))
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records.append({
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"company_name": company,
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"date": dt,
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"daily_credits": daily_credits,
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"account_type": account_type,
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"region": region,
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"segment": segment,
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})
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return pd.DataFrame(records)
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@st.cache_data(ttl=3600)
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def load_company_data() -> pd.DataFrame:
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"""Load all company data."""
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return generate_company_data(days=90)
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def aggregate_companies(
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df: pd.DataFrame,
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days: int | None = None,
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account_types: list[str] | None = None,
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sort_by: str = "total_credits",
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) -> pd.DataFrame:
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"""Filter and aggregate company data."""
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result = df.copy()
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# Filter by time window
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if days:
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cutoff = pd.Timestamp.now() - pd.Timedelta(days=days)
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result = result[result["date"] >= cutoff]
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# Filter by account type
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if account_types:
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result = result[result["account_type"].isin(account_types)]
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if result.empty:
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return pd.DataFrame()
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# Aggregate to company level
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agg = result.groupby("company_name").agg(
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total_credits=("daily_credits", "sum"),
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active_days=("date", "nunique"),
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account_type=("account_type", "first"),
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region=("region", "first"),
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segment=("segment", "first"),
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).reset_index()
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# Calculate daily average
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agg["daily_avg"] = agg["total_credits"] / agg["active_days"]
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# Build sparkline data (list of daily values)
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sparklines = (
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result.groupby("company_name")
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.apply(lambda x: x.sort_values("date")["daily_credits"].tolist())
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.reset_index()
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)
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sparklines.columns = ["company_name", "usage_trend"]
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agg = agg.merge(sparklines, on="company_name")
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# Calculate growth score (second half vs first half)
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def calc_growth(trend):
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if not trend or len(trend) < 2:
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return 0
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mid = len(trend) // 2
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first_half = sum(trend[:mid]) if mid > 0 else 0
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second_half = sum(trend[mid:])
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return second_half - first_half
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agg["growth_score"] = agg["usage_trend"].apply(calc_growth)
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# Sort
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if sort_by == "growth_asc":
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agg = agg.sort_values("growth_score", ascending=True)
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elif sort_by == "growth_desc":
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agg = agg.sort_values("growth_score", ascending=False)
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else:
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agg = agg.sort_values("total_credits", ascending=False)
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return agg
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def render_company_dialog(company_name: str, company_row: pd.Series, df: pd.DataFrame):
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"""Render company details inside a dialog."""
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company_data = df[df["company_name"] == company_name].sort_values("date")
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if company_data.empty:
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st.warning("No data available for this company.")
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return
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# Company info badges - extract from list format back to single value
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account_type = company_row["account_type"][0] if company_row["account_type"] else "Unknown"
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region = company_row["region"][0] if company_row["region"] else "Unknown"
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segment = company_row["segment"][0] if company_row["segment"] else "Unknown"
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total_credits = company_row["total_credits"]
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st.markdown(
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f":blue-badge[{account_type}] "
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f":violet-badge[{region}] "
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f":orange-badge[{segment}] "
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f":green-badge[{total_credits:,.0f} credits]"
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)
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# Summary metrics
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("Total Credits", f"{total_credits:,.0f}")
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with col2:
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st.metric("Active Days", f"{company_row['active_days']:,}")
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with col3:
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growth = company_row["growth_score"]
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st.metric("Growth Score", f"{growth:+,.0f}")
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# Charts
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col1, col2 = st.columns(2)
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with col1:
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with st.container(border=True):
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st.markdown("**Daily usage**")
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st.line_chart(company_data, x="date", y="daily_credits", height=250)
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with col2:
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with st.container(border=True):
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st.markdown("**Cumulative usage**")
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chart_data = company_data.copy()
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chart_data["cumulative"] = chart_data["daily_credits"].cumsum()
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st.area_chart(chart_data, x="date", y="cumulative", height=250)
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# =============================================================================
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# Page Layout
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# =============================================================================
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# Load data
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all_data = load_company_data()
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st.markdown("# :material/business: Company Analytics")
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st.caption("Track company adoption - usage, growth trends, and account details.")
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# Filters
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with st.container(border=True):
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st.markdown("**Filters**")
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# Company selection mode
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sort_mode = st.segmented_control(
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"Sort by",
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options=[
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"All companies",
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":material/military_tech: Top spenders",
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":material/trending_down: Top shrinkers",
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":material/trending_up: Top gainers",
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],
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default="All companies",
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)
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# Time window
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timeframe_options = {
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"All time": None,
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"Last 28 days": 28,
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"Last 7 days": 7,
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}
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timeframe = st.segmented_control(
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"Time window",
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options=list(timeframe_options.keys()),
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default="Last 28 days",
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)
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days_filter = timeframe_options.get(timeframe)
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# Account types
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account_types = st.pills(
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"Account types",
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options=ACCOUNT_TYPES,
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default=["Enterprise", "Growth", "Startup"],
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selection_mode="multi",
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)
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# Determine sort order
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if "Top shrinkers" in (sort_mode or ""):
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sort_by = "growth_asc"
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elif "Top gainers" in (sort_mode or ""):
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sort_by = "growth_desc"
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else:
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sort_by = "total_credits"
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# Get filtered data
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leaderboard = aggregate_companies(
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all_data,
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days=days_filter,
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account_types=account_types,
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sort_by=sort_by,
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)
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if leaderboard.empty:
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st.warning("No company data found for the selected filters.")
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st.stop()
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def _to_list(val):
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"""Convert a single value to a list for MultiselectColumn display."""
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return [val] if pd.notna(val) else []
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# Convert columns to lists for MultiselectColumn display (shows nice colored chips)
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for col in ["account_type", "region", "segment"]:
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leaderboard[col] = leaderboard[col].apply(_to_list)
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# Companies dataframe
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with st.container(border=True):
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timeframe_text = timeframe.lower() if timeframe != "All time" else "all time"
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st.markdown(f"**Companies — {timeframe_text}**")
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# Selection dataframe with cell-click support
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selection = st.dataframe(
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leaderboard,
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column_config={
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"company_name": st.column_config.TextColumn(
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"Company (👋 click to view details)",
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width="medium",
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),
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"account_type": st.column_config.MultiselectColumn(
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"Type",
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options=ACCOUNT_TYPES,
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color="auto",
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width="small",
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),
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"total_credits": st.column_config.NumberColumn(
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"Credits",
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format="%.0f",
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),
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"growth_score": st.column_config.NumberColumn(
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"Growth",
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format="%+.0f",
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help="Credit change: second half vs first half of period",
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),
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"usage_trend": st.column_config.LineChartColumn(
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"Trend",
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width="medium",
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),
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"daily_avg": st.column_config.NumberColumn(
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"Daily Avg",
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format="%.1f",
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),
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"active_days": st.column_config.NumberColumn(
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"Active Days",
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format="%d",
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),
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"region": st.column_config.MultiselectColumn(
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"Region",
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options=REGIONS,
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color="auto",
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),
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"segment": st.column_config.MultiselectColumn(
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"Segment",
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options=SEGMENTS,
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color="auto",
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),
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},
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column_order=[
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"company_name", "account_type", "total_credits", "growth_score",
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"usage_trend", "daily_avg", "region", "segment",
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],
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hide_index=True,
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on_select="rerun",
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selection_mode="single-cell",
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key="company_leaderboard",
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)
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# Company drill-down via dialog when Company column cell is clicked
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if selection.selection.cells:
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cell = selection.selection.cells[0] # tuple: (row_index, column_name)
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row_idx, col_name = cell
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# Check if the clicked cell is in the company_name column
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if col_name == "company_name":
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selected_company = leaderboard.iloc[row_idx]["company_name"]
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company_row = leaderboard.iloc[row_idx]
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@st.dialog(f"{selected_company}", width="large")
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def show_company_dialog():
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render_company_dialog(
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selected_company,
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company_row=company_row,
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df=all_data,
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)
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show_company_dialog()
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