Data Analysis 1 Lazy Evaluation First

1. Lazy Evaluation First (+3)

1. Lazy Evaluation First

# Prefer lazy operations, collect only when needed
result = (
    pl.scan_parquet("data/*.parquet")
    .filter(...)
    .group_by(...)
    .agg(...)
    .collect()  # Execute at the end
)

2. Progressive Disclosure in Dashboards

# Start with summary, allow drill-down
st.header("Overview")
show_metrics()

with st.expander("Detailed Analysis"):
    show_detailed_charts()

with st.expander("Raw Data"):
    st.dataframe(df)

3. Reproducible Reports

# Include metadata in reports
report_metadata = {
    "generated_at": datetime.now().isoformat(),
    "data_source": "sales_database",
    "date_range": f"{start_date} to {end_date}",
    "filters_applied": filters
}

4. Performance Monitoring

import time

def timed_operation(name):
    def decorator(func):
        def wrapper(*args, **kwargs):
            start = time.time()
            result = func(*args, **kwargs)
            duration = time.time() - start
            logger.info(f"{name} completed in {duration:.2f}s")
            return result
        return wrapper
    return decorator

@timed_operation("Data aggregation")
def aggregate_sales():
    ...