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Notebooks

Jupyter notebooks that produce publication-ready charts from the VynCo API. Every notebook renders its figures into figures/ at 150 DPI for use in reports, READMEs, or blog posts.

Setup

pip install vynco matplotlib seaborn networkx jupyter
export VYNCO_API_KEY=vc_live_your_api_key
jupyter lab

Or execute headlessly:

jupyter nbconvert --to notebook --execute notebooks/<name>.ipynb --output <name>.ipynb

Notebooks

swiss_market_analytics.ipynb — Market landscape

High-level analytics about the Swiss corporate database: canton distribution, auditor market share, status breakdown, cohort analysis, RFM segmentation, and legal-form distribution.

Swiss companies by canton RFM segmentation

Figures: canton_distribution.png, auditor_market_share.png, company_statistics.png, cohort_analysis.png, rfm_segments.png, legal_form_distribution.png

company_deep_dive.ipynb — Single-company investigation

A complete research workflow on one company: profile card, board of directors, risk score with factor breakdown, corporate network graph (up to ~200 nodes), multi-company comparison, and nearby companies on a geographic scatter.

Company profile card

Risk assessment

Corporate network graph

Figures: company_profile_card.png, board_members.png, risk_score.png, corporate_network.png, company_comparison.png, nearby_companies.png

compliance_screening.ipynb — Batch compliance

Screens multiple companies against sanctions lists, runs AI risk scoring on each, and produces a consolidated compliance dashboard with per-company PASS/MONITOR/REVIEW status.

Compliance dashboard

Risk factor heatmap

Figures: screening_results.png, risk_heatmap.png, compliance_dashboard.png, fingerprint_comparison.png

market_flows.ipynb (v3.1+) — Market dynamics

Uses the new analytics.flows() and analytics.migrations() endpoints to visualize registrations vs dissolutions over time, net formation rate, canton-to-canton migration flows, and industry flow rankings.

Figures: market_flows.png, canton_migrations.png, industry_flows.png

similar_companies.ipynb (v3.1+) — Peer discovery and benchmarking

Finds similar companies via companies.similar() (scored 0–100 on industry, canton, capital, legal form, auditor tier), then benchmarks the target against industry peers with analytics.benchmark(). Includes a radar chart and a percentile detail table.

Figures: similar_companies.png, benchmark_radar.png, benchmark_table.png

Figure catalog

All figures are regenerated by re-executing the notebooks. They live in figures/ as 150 DPI PNGs suitable for embedding in documentation or marketing material.

Good candidates for README screenshots or blog hero images:

  • compliance_dashboard.png — readable at small sizes, lots of information
  • corporate_network.png — visually striking (UBS: 1086 nodes, 1246 edges)
  • risk_heatmap.png — shows comparative analysis across multiple companies
  • canton_distribution.png — classic bar chart, good for "about" pages
  • company_profile_card.png — clean summary for a single company
  • benchmark_radar.png — pops on light backgrounds

Tips

  • The notebooks are idempotent — running them repeatedly produces the same figures (same seed for layouts).
  • Target companies are looked up dynamically by search query, so they adapt to whatever data is live.
  • For the v3.1+ notebooks (market_flows, similar_companies), ensure your API key has access to the new endpoints.
  • Generated figures are kept out of git by default — regenerate them on demand.