Data & Content Blocks¶

from holysheet import DataTable

DataTable(
    title="Top Customers",
    data=[
        {"company": "Meridian Corp", "plan": "Enterprise", "mrr": "$12,400"},
        {"company": "Atlas Dynamics", "plan": "Enterprise", "mrr": "$9,800"},
        {"company": "Helix Systems", "plan": "Mid-Market", "mrr": "$5,600"},
    ],
)

DataTable(
    title="Project Portfolio",
    data=projects_data,
    columns=["project", "owner", "risk", "status", "completion", "budget_used"],
)

import pandas as pd

df = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol"],
    "Score": [95, 87, 92],
    "Grade": ["A", "B+", "A-"],
})

DataTable(title="Student Grades", data=df)

DataTable(
    title="Static Data",
    data=my_data,
    searchable=False,  # No search bar
    paginated=False,   # Show all rows
)

from holysheet import Markdown

Markdown(content="""## Executive Summary

Portfolio health remains **strong** with 42 active projects delivering on schedule.
Risk-adjusted returns are trending positively, with a **12% improvement** in delivery
confidence over the past quarter.

### Key Highlights

- Revenue exceeded targets for 10 consecutive months
- Overall portfolio risk score decreased from 72 to 38
- Frontend team delivered 110% of planned capacity

---

> _This report was generated with HolySheet v0.2.0_
""")

from holysheet import CodeBlock

CodeBlock(
    code=(
        "from holysheet import Report, KPI, LineChart\n\n"
        "report = Report(title='Q4 Metrics', theme='dark')\n"
        "report.add(KPI(label='Revenue', value='$258K', delta='+12%', status='positive'))\n"
        "report.add(LineChart(title='Trend', data=monthly_data, x='month', y='revenue'))\n\n"
        "report.export_html('q4_report.html')\n"
    ),
    language="python",
    title="Quick Start — Generate a Report",
)

from holysheet import Image

Image(
    src="https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=800",
    alt="Data Visualization Dashboard",
    caption="NovaPulse Platform — Live Dashboard View",
    width="100%",
)

Image(
    src="https://example.com/chart.png",
    alt="Q4 Revenue Chart",
    width=600,
    height=400,
)

from holysheet import ProgressBar, Columns

Columns(children=[
    ProgressBar(
        label="CPU Utilisation",
        value=73,
        description="18 / 24 vCPUs allocated",
    ),
    ProgressBar(
        label="Memory Usage",
        value=61,
        description="49 GB / 80 GB",
    ),
    ProgressBar(
        label="Disk I/O",
        value=42,
        description="Healthy throughput",
    ),
    ProgressBar(
        label="Network Bandwidth",
        value=88,
        description="Approaching limit — consider upgrade",
        color="#EF4444",  # Red to indicate warning
    ),
])

ProgressBar(
    label="Sprint Progress",
    value=34,
    max=50,       # 34 out of 50 story points
    description="34 / 50 story points completed",
)

from holysheet import Embed

Embed(
    url="https://www.google.com/maps/embed?pb=...",
    title="Office Locations",
    height=450,
)

from holysheet import InfoList

InfoList(
    title="System Configuration",
    items=[
        {"key": "Environment", "value": "Production", "icon": "🌐"},
        {"key": "Region", "value": "us-east-1", "icon": "📍"},
        {"key": "Python", "value": "3.12.4", "icon": "🐍"},
        {"key": "Database", "value": "PostgreSQL 16.2", "icon": "🗄️"},
    ],
)

from holysheet import TagList

TagList(
    title="Tech Stack",
    tags=[
        {"label": "Python", "color": "#3776AB"},
        {"label": "React", "color": "#61DAFB"},
        {"label": "TypeScript", "color": "#3178C6"},
        {"label": "PostgreSQL", "color": "#4169E1"},
    ],
)

from holysheet import Sparkline, Columns

Columns(children=[
    Sparkline(data=[10, 25, 18, 35, 28, 42, 55, 48, 62, 75], color="#6C63FF"),
    Sparkline(data=[50, 45, 52, 48, 55, 42, 58, 62, 68, 80], color="#34d399"),
])

import pandas as pd
from holysheet import DataProfile

df = pd.read_csv("customers.csv")

# Build column profiles from pandas describe
profiles = []
for col in df.columns:
    profile = {
        "name": col,
        "dtype": str(df[col].dtype),
        "count": int(df[col].count()),
        "null_count": int(df[col].isna().sum()),
        "null_pct": round(df[col].isna().mean() * 100, 2),
        "unique": int(df[col].nunique()),
    }
    if df[col].dtype in ("float64", "int64"):
        profile["mean"] = round(df[col].mean(), 2)
        profile["std"] = round(df[col].std(), 2)
        profile["min"] = round(df[col].min(), 2)
        profile["max"] = round(df[col].max(), 2)
    else:
        profile["top"] = str(df[col].mode().iloc[0]) if len(df[col].mode()) > 0 else None
        profile["freq"] = int(df[col].value_counts().iloc[0]) if len(df[col]) > 0 else None
    profiles.append(profile)

DataProfile(title=f"Profile: {df.shape[0]} rows × {df.shape[1]} cols", columns=profiles)

from holysheet import Video

Video(
    src="https://example.com/demo.mp4",
    title="Product Demo",
    poster="https://example.com/poster.jpg",
    controls=True,
)

from holysheet import SqlBlock

SqlBlock(
    title="Revenue by Region",
    query="SELECT region, SUM(revenue) as total FROM data GROUP BY region ORDER BY total DESC",
    data=[
        {"region": "US", "revenue": 120000},
        {"region": "EU", "revenue": 95000},
        {"region": "APAC", "revenue": 78000},
    ],
)

from holysheet import NarrationBlock

NarrationBlock(
    text="Revenue grew 22% quarter-over-quarter, driven by enterprise accounts.",
    autoplay=False,
)

from holysheet import AIInsight

AIInsight(
    title="Revenue Trends",
    data=revenue_df,
    provider="openai",
)

from holysheet import GoogleSheet

GoogleSheet(
    spreadsheet_id="1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgVE2upms",
    sheet_name="Q4 Data",
    title="Sales by Region",
)