Shipvane · 4 AE roles
Marco E. · joined Cargolio
Fleetora · first roles in DED3.js VS SciPy_
As of 4 November 2026, SciPy is named by 520 companies and D3.js by 466; D3.js skews to companies under 200 people (55% vs 47%), SciPy to companies over 1,000 (28% vs 23%).
As of 4 November 2026 · refreshed daily · counts move as websites, DNS and career sites are re-read.
The website-and-DNS footprint is not comparable between a marketing suite and a CRM: D3.js's script and mail records show on hundreds of thousands of small sites, SciPy rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- D3.js: 2–200 employees
- 55%
- SciPy: 2–200 employees
- 47%
- D3.js: 1,000+ employees
- 23%
- SciPy: 1,000+ employees
- 28%
- Postings per company, 90 days
- 1.6 · 2.0
- Website or DNS footprint
- 0 · 0
Who chooses which
Share of each tool's companies in every segment. "Leans" marks a gap of more than two points.
| Segment | D3.js | share | SciPy | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 26 | 6% | 20 | 4% | even |
| 2-10 employees | 69 | 15% | 43 | 8% | D3.js |
| 11-50 employees | 89 | 19% | 88 | 17% | D3.js |
| 51-200 employees | 100 | 21% | 111 | 21% | even |
| 201-500 employees | 41 | 9% | 78 | 15% | SciPy |
| 501-1000 employees | 33 | 7% | 34 | 7% | even |
| 1001-5000 employees | 54 | 12% | 60 | 12% | even |
| 5001-10000 employees | 20 | 4% | 22 | 4% | even |
| 10,001+ employees | 34 | 7% | 64 | 12% | SciPy |
| Segment | D3.js | share | SciPy | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 252 | 54% | 182 | 35% | D3.js |
| Business & finance services | 89 | 19% | 93 | 18% | even |
| Manufacturing & industry | 35 | 8% | 112 | 22% | SciPy |
| Public, community & associations | 18 | 4% | 38 | 7% | SciPy |
| Consumer services & leisure | 18 | 4% | 21 | 4% | even |
| Media & content | 17 | 4% | 15 | 3% | even |
| Property & construction | 10 | 2% | 3 | 1% | even |
| Commerce & trade | 7 | 2% | 8 | 2% | even |
| Utilities & infrastructure | 7 | 2% | 23 | 4% | SciPy |
| Transport, logistics & rental | 4 | 1% | 5 | 1% | even |
| Automotive | 3 | 1% | 10 | 2% | even |
| Health care | 3 | 1% | 9 | 2% | even |
| Segment | D3.js | share | SciPy | share | Leans |
|---|---|---|---|---|---|
| United States | 315 | 68% | 310 | 60% | D3.js |
| United Kingdom | 22 | 5% | 37 | 7% | SciPy |
| India | 20 | 4% | 17 | 3% | even |
| Germany | 17 | 4% | 27 | 5% | even |
| Canada | 14 | 3% | 10 | 2% | even |
| France | 8 | 2% | 14 | 3% | even |
| Italy | 4 | 1% | – | – | outside top 12 |
| Singapore | 4 | 1% | – | – | outside top 12 |
| United Arab Emirates | 3 | 1% | – | – | outside top 12 |
| Brazil | 3 | 1% | – | – | outside top 12 |
| Switzerland | 3 | 1% | 6 | 1% | even |
| Spain | 3 | 1% | 9 | 2% | even |
| Poland | – | – | 8 | 2% | outside top 12 |
| Sweden | – | – | 8 | 2% | outside top 12 |
| Netherlands | – | – | 7 | 1% | outside top 12 |
| Australia | – | – | 4 | 1% | outside top 12 |
| Segment | D3.js | share | SciPy | share | Leans |
|---|---|---|---|---|---|
| Software engineering | 617 | 54% | 251 | 19% | D3.js |
| Data & AI | 244 | 21% | 578 | 43% | SciPy |
| IT operations & business systems | 59 | 5% | 19 | 1% | D3.js |
| Design & UX | 27 | 2% | – | – | outside top 12 |
| Engineering (non-software) | 20 | 2% | 140 | 10% | SciPy |
| construction and civil | 12 | 1% | – | – | outside top 12 |
| Product, program & project management | 11 | 1% | 1 | 0% | even |
| logistics warehouse and transport | 7 | 1% | – | – | outside top 12 |
| Sales | 6 | 1% | – | – | outside top 12 |
| Education & training | 4 | 0% | 3 | 0% | even |
| Quality, safety & environment | 4 | 0% | 2 | 0% | even |
| research and science | 4 | 0% | 48 | 4% | SciPy |
| skilled trades and field service | 4 | 0% | – | – | outside top 12 |
| arts media and entertainment | 3 | 0% | 1 | 0% | even |
| Business development & strategy | 3 | 0% | – | – | outside top 12 |
| Finance & accounting | 2 | 0% | 61 | 4% | SciPy |
| Marketing & communications | 2 | 0% | 5 | 0% | even |
| Administration & office | 1 | 0% | – | – | outside top 12 |
| Customer service & success | 1 | 0% | 5 | 0% | even |
| facilities cleaning and security | 1 | 0% | – | – | outside top 12 |
| Human resources | 1 | 0% | – | – | outside top 12 |
| Legal, compliance & risk | 1 | 0% | 11 | 1% | even |
| manufacturing and production | 1 | 0% | – | – | outside top 12 |
| Unclassified | – | – | 5 | 0% | outside top 12 |
| General management & consulting | – | – | 3 | 0% | outside top 12 |
| public safety and protective services | – | – | 2 | 0% | outside top 12 |
The companies behind each column
Companies using D3.js →
Companies using SciPy →
data science libraries ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
SciPy is named by 520 companies in job postings and D3.js by 466; 0 name both. (Breaknorm, 4 November 2026, https://breaknorm.co/compare/d3-js-vs-scipy)
Read from the source, not self-reported
Every number on this page can be traced to a page we crawled or a posting we read. Full methodology →
- Job postingsRead hourly from career sites. A tool counts when the employer's own text names it as the team's system; both tools can count for one company.
- Website and DNSScripts and embeds on the company's pages, plus SPF, DKIM, MX and CNAME records of its domain that point at the vendor.
- SegmentsCountry, size and industry from the company profile built from its website.
- Not a reviewNo ratings, prices or feature claims. For those, read the vendors.
D3.js vs SciPy: questions
Which is more used, D3.js or SciPy?
By companies naming it in their own job postings, SciPy (520) over D3.js (466). By website-and-DNS footprint, SciPy (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses D3.js?
55% of D3.js companies have 2 to 200 employees, against 47% for SciPy. 23% of D3.js companies have over 1,000 employees, against 28% for SciPy.
Do companies run both?
0 companies name both in their postings, usually D3.js in marketing and SciPy in sales, or during a migration.
Where do these numbers come from?
From the employer's own text in job postings read hourly from career sites, and from scripts found on company websites in our crawl. No vendor data, no surveys.
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