Examples

Examples#

Learn by example with these practical demonstrations of chartbook features.

Quick Examples#

Basic Pipeline#

Create a simple analytics pipeline:

import pandas as pd
from pathlib import Path

# Generate sample data
import numpy as np
df = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=365, freq='D'),
    'sales': np.random.randint(1000, 5000, 365),
    'costs': np.random.randint(500, 2000, 365)
})
df['profit'] = df['sales'] - df['costs']

# Save data
df.to_parquet('_data/financial_data.parquet')

Loading Data#

from chartbook import data

# Load from a catalog pipeline
df = data.load(pipeline="EX", dataframe="repo_public")

# With explicit catalog path
df = data.load(pipeline="EX", dataframe="repo_public",
               catalog_path="/path/to/catalog")

Generating Documentation#

# Generate documentation website
chartbook build ./docs --force-write

# View locally
python -m http.server -d ./docs

Complete Examples#

  • Pipeline Example: A complete analytics pipeline with charts and dataframes

  • Catalog Example: Multi-pipeline catalog project

  • Data Pipeline: End-to-end data processing workflow

Contents