[tmgsr02]

Summary

• add a macrobase_py package with a high-level analysis API and result container classes
• provide Matplotlib and Plotly visualization helpers for summary tables, bar charts, and pivot heatmaps
• document the workflow with example notebooks and a Streamlit demo for interactive exploration

Testing

• python - <<'PY'
  from macrobase_py.api import analyze

result = analyze('core/demo/sample.csv')
print(result.row_count, result.column_count)
print(len(result.column_profiles))
print(result.column_profiles[0].top_values[:2])
print(result.summary_statistics.head(2))
PY

• python - <<'PY'
  from macrobase_py.api import analyze

cfg = {
'target_column': 'usage',
'pivot': [
{'index': 'location', 'columns': 'version', 'values': ['latency'], 'aggfunc': 'mean'}
]
}
res = analyze('core/demo/sample.csv', cfg)
print(len(res.pivot_views))
print(res.pivot_views[0].metrics['latency'].head())
PY

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https://chatgpt.com/codex/tasks/task_e_68fade19bb348320aee0ab9263847ff9