NumPy VS Seaborn
As of 30 October 2026, Seaborn is named by 204 companies and NumPy by 1,759; NumPy skews to companies under 200 people (46% vs 44%), Seaborn to companies over 1,000 (33% vs 29%).
As of 30 October 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: NumPy's script and mail records show on hundreds of thousands of small sites, Seaborn rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- NumPy: 2–200 employees
- 46%
- Seaborn: 2–200 employees
- 44%
- NumPy: 1,000+ employees
- 29%
- Seaborn: 1,000+ employees
- 33%
- Postings per company, 90 days
- 2.3 · 2.6
- 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 | NumPy | share | Seaborn | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 39 | 2% | 5 | 2% | even |
| 2-10 employees | 151 | 9% | 15 | 7% | even |
| 11-50 employees | 313 | 18% | 33 | 16% | even |
| 51-200 employees | 347 | 20% | 41 | 20% | even |
| 201-500 employees | 245 | 14% | 26 | 13% | even |
| 501-1000 employees | 148 | 8% | 17 | 8% | even |
| 1001-5000 employees | 213 | 12% | 32 | 16% | Seaborn |
| 5001-10000 employees | 64 | 4% | 9 | 4% | even |
| 10,001+ employees | 239 | 14% | 26 | 13% | even |
| Segment | NumPy | share | Seaborn | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 722 | 41% | 87 | 43% | even |
| Business & finance services | 394 | 22% | 38 | 19% | NumPy |
| Manufacturing & industry | 258 | 15% | 27 | 13% | even |
| Public, community & associations | 70 | 4% | 7 | 3% | even |
| Utilities & infrastructure | 64 | 4% | 10 | 5% | even |
| Consumer services & leisure | 63 | 4% | 12 | 6% | Seaborn |
| Media & content | 58 | 3% | – | – | outside top 12 |
| Commerce & trade | 38 | 2% | 3 | 1% | even |
| Automotive | 30 | 2% | 3 | 1% | even |
| Transport, logistics & rental | 20 | 1% | 5 | 2% | even |
| Health care | 18 | 1% | 3 | 1% | even |
| Property & construction | 15 | 1% | 4 | 2% | even |
| Hospitality & travel | – | – | 3 | 1% | outside top 12 |
| Segment | NumPy | share | Seaborn | share | Leans |
|---|---|---|---|---|---|
| United States | 1,055 | 60% | 117 | 57% | NumPy |
| United Kingdom | 127 | 7% | 12 | 6% | even |
| India | 100 | 6% | 14 | 7% | even |
| Germany | 77 | 4% | 9 | 4% | even |
| France | 57 | 3% | 6 | 3% | even |
| Canada | 36 | 2% | 7 | 3% | even |
| Spain | 24 | 1% | 2 | 1% | even |
| Poland | 21 | 1% | 3 | 1% | even |
| Australia | 18 | 1% | 3 | 1% | even |
| Singapore | 18 | 1% | – | – | outside top 12 |
| Italy | 17 | 1% | – | – | outside top 12 |
| Netherlands | 17 | 1% | – | – | outside top 12 |
| Switzerland | – | – | 6 | 3% | outside top 12 |
| Belgium | – | – | 4 | 2% | outside top 12 |
| Brazil | – | – | 3 | 1% | outside top 12 |
| Segment | NumPy | share | Seaborn | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 3,414 | 60% | 573 | 79% | Seaborn |
| Software engineering | 805 | 14% | 26 | 4% | NumPy |
| Engineering (non-software) | 262 | 5% | 17 | 2% | NumPy |
| Finance & accounting | 158 | 3% | 10 | 1% | even |
| Research and science | 85 | 2% | 12 | 2% | even |
| IT operations & business systems | 58 | 1% | 8 | 1% | even |
| Legal, compliance & risk | 46 | 1% | 5 | 1% | even |
| Education & training | 37 | 1% | 6 | 1% | even |
| Marketing & communications | 21 | 0% | – | – | outside top 12 |
| Product, program & project management | 13 | 0% | – | – | outside top 12 |
| General management & consulting | 10 | 0% | – | – | outside top 12 |
| Logistics warehouse and transport | 10 | 0% | – | – | outside top 12 |
| Human resources | – | – | 4 | 1% | outside top 12 |
| Sales | – | – | 4 | 1% | outside top 12 |
| Unclassified | – | – | 3 | 0% | outside top 12 |
| Customer service & success | – | – | 2 | 0% | outside top 12 |
The companies behind each column
Companies using NumPy →
Companies using Seaborn →
data science libraries ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
Seaborn is named by 204 companies in job postings and NumPy by 1,759; 208 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/numpy-vs-seaborn)
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.
NumPy vs Seaborn: questions
Which is more used, NumPy or Seaborn?
By companies naming it in their own job postings, NumPy (1,759) over Seaborn (204). By website-and-DNS footprint, Seaborn (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses NumPy?
46% of NumPy companies have 2 to 200 employees, against 44% for Seaborn. 29% of NumPy companies have over 1,000 employees, against 33% for Seaborn.
Do companies run both?
208 companies name both in their postings, usually NumPy in marketing and Seaborn 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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