Polars DataFrames users list

Email list of companies using Polars DataFrames

Q: How many companies use Polars DataFrames?

Data InfoMetrix tracks 5,004 companies using Polars DataFrames, with 22,593 verified contacts at those accounts as of October 2026. The largest groups are Quantitative Finance, Research and SaaS. Counts are estimates from our database and market research, so ask us for a live count for your exact criteria.

Q: Who buys and manages Polars DataFrames inside a company?

Polars DataFrames is usually owned by High-Performance DataFrame Engineering. Decision-makers include Chief Data Officer, VP of Data & Analytics and CTO; Data Platform Lead and Analytics Engineering Manager shape the choice, and Data Engineers and Analytics Engineers use it day to day. Our Polars DataFrames list can include any of these roles.

Q: Which Polars DataFrames products or editions can I target?

You can target accounts by the Polars DataFrames products they run, including Polars (Open Source), Polars Lazy API, Polars Streaming Engine, Polars Cloud and Polars GPU Engine. Tell us which edition or module matters and we filter the list to those accounts.

Q: What other software do Polars DataFrames customers use?

Polars DataFrames customers commonly also run Python, Apache Arrow, DuckDB, Jupyter and AWS. Polars DataFrames is often connected to Delta Lake, so users of those tools overlap heavily. We can filter any list by this kind of stack overlap.

Q: What are the main alternatives to Polars DataFrames?

The main alternatives to Polars DataFrames are pandas, Apache Spark and DuckDB. We also keep verified user lists for DuckDB, which is useful for competitive displacement campaigns.

Q: When are Polars DataFrames customers most likely to buy?

Buying triggers we track for Polars DataFrames accounts include pipeline performance project, Polars Cloud trial, data science team growth and spark cost review. A common pain point is pandas performance limits: large data slows notebooks.

Q: Can Data InfoMetrix deliver a free sample Polars contact list before ordering?

Yes. Data InfoMetrix delivers a complimentary 10-record sample tailored to your target Polars criteria—including target job titles, company size, and region—delivered within one business day with no obligation.

Q: How does Data InfoMetrix guarantee email deliverability for Polars contacts?

Data InfoMetrix provides a 99% email deliverability guarantee on all Polars datasets. Every contact record undergoes real-time email verification within a 30 to 45-day refresh cycle, backed by a 1:1 hard bounce replacement policy for 30 days after delivery.

Reach data scientists and engineers using Polars and Polars Cloud for fast DataFrame processing in Python and Rust.

5,004+Companies

22,593+Contacts

Updated Oct 7, 2026GDPR · CCPA99% accuracy

See sample data

Free Polars DataFrames count + 10 sample records

5,004+ companies · 22,593+ contacts available

✓Exact count for your titles, regions and company size
✓10 verified sample records to check quality
✓Price quote within 1 business day

Trusted by B2B and enterprise revenue teams

How many companies use Polars DataFrames?

Data InfoMetrix tracks 5,004+ companies running Polars DataFrames as of October 2026, with 22,593+ verified contacts at those accounts. The largest groups are Quantitative Finance, Research and SaaS. Buying decisions usually sit with the Chief Data Officer, VP of Data & Analytics and CTO. Counts are estimates built from technology footprint scans, job-post signals and partner directories, checked by our research team. How we count

Tracked accounts5,004+

Organizations detected running Polars DataFrames.

Verified contacts22,593+

Direct emails and phone dials for decision-makers.

Primary personasData Scientist, ML Engineer, Data Engineering Lead

Full buying committee mapped.

Deliverability SLA99%

SMTP-verified, with 1:1 replacement of any email that hard bounces within 30 days.

Data specification · Updated Q4 2026

Technology category	Data Analytics & Modern Data Stack
Tracked organizations	5,004+ verified accounts worldwide
Decision-maker contacts	22,593+ records with emails, mobile and direct dials
Key roles	Data Scientist, ML Engineer, Data Engineering Lead, platform administrators, IT directors, procurement
Geography	180+ countries · country-by-country counts on request
Verification	DNS and script telemetry, 5-stage SMTP handshake, manual research
Refresh cadence	Continuous, 30–45 day maximum record age at delivery
Compliance	GDPR, CAN-SPAM, CCPA and CASL
Delivery	24–48 hours as CSV, XLSX or CRM API

Editorial & verification

Researched by the Data InfoMetrix research team. Counts updated October 2026.

How we count and verify data

Size your Polars DataFrames audience

Pick a region, company size and buying role to estimate how many accounts and contacts you can reach.

Region

Company size

Buying role

Estimated segment✓ 99% SLA

5,004+Target accounts

22,593+Direct contacts

Decision-makers35%

Influencers45%

End users20%

Estimates from our current Polars DataFrames dataset. Exact counts come with your quote.

Reach the whole buying committee

A Polars DataFrames account is not one contact. We map the people who evaluate, buy, run and use it across High-Performance DataFrame Engineering.

Decision-makersC-suite & sponsors

Chief Data OfficerVP of Data & AnalyticsCTOHead of Data Engineering

Influencers & adminsEvaluate & configure

Data Platform LeadAnalytics Engineering ManagerData ArchitectData Governance Lead

End usersRun it every day

Data EngineersAnalytics EngineersData AnalystsData Scientists

These are examples. We build records for any department, seniority or custom title.

See exactly what you get

Every record carries 60+ fields, from verified email, direct dial and LinkedIn to full postal address, Polars DataFrames edition, coexisting stack and intent.

Contact	Buying role	Work email	Direct dial	Company phone	Fax	LinkedIn profile	Company	LinkedIn company page	Industry	Address 1	Address 2	City	State	ZIP code	Country	Polars DataFrames footprint	Intent	Status
SPDr. Sanjay PatelChief Data Officer (CDO)	Decision Maker	s.pa**@apexfina******.com	+1 (212) 55*-**	+1 (212) 55*-**	+1 (212) 55*-**	linkedin.com/in/s*ay-pa******	Apex Financial Intelligence Group Inc.	linkedin.com/company/apex-fin-analytics	Financial Services & Investment Research	200 Park Ave 42nd	Fl	New York	NY	10166	United States	Polars DataFrames Enterprise Cloud Data Warehouse & Lakehouse	High	✓ Verified 2026
TSTheresa SterlingVP of Data Engineering & Infrastructure	Decision Maker	t.ste**@novac**.io	+1 (415) 55*-**	+1 (415) 55*-**	+1 (415) 55*-**	linkedin.com/in/t*esa-st*******	Nova Cloud Communications Inc.	linkedin.com/company/nova-cloud-tech	Cloud Infrastructure & High-Volume Telemetry	350 Mission St	Ste 1800	San Francisco	CA	94105	United States	Polars DataFrames Real-Time Streaming Telemetry & Event Ingestion	High	✓ Verified 2026
MBMaximilian BauerDirector of Business Intelligence & Analytics	Influencer	m.ba**@bavaria****.de	+49 (89) 55*-**	+49 (89) 55*-**	+49 (89) 55*-**	linkedin.com/in/m*ilian-********	Bavaria Digital Retail Group AG	linkedin.com/company/bavaria-retail-ag	Omnichannel Retail & E-Commerce	Maximilianstraße 35	—	Munich	Bavaria	80539	Germany	Polars DataFrames Business Intelligence Enterprise Dashboards	Medium	✓ Verified 2026
COClara OswaldLead Analytics Engineer & Data Architect	Practitioner	c.os**@beaconhe*****.uk	+44 (20) 79*-**	+44 (20) 79*-**	+44 (20) 79*-**	linkedin.com/in/c*a-os******	Beacon Clinical Healthcare Analytics Ltd	linkedin.com/company/beacon-clinical-data	Health Analytics & Clinical Research	25 Finsbury Circus	—	London	Greater London	EC2M 7EE	United Kingdom	Polars DataFrames Automated Transformation & Semantic Layer	High	✓ Verified 2026
RLRobert LangdonHead of Enterprise Data Governance & Compliance	Decision Maker	r.lan**@prudenti*****.com	+1 (312) 55*-**	+1 (312) 55*-**	+1 (312) 55*-**	linkedin.com/in/r*rt-la*******	Prudential Trust & Bancorp Inc.	linkedin.com/company/prudential-trust-bancorp	Commercial Banking & Wealth Management	111 W Monroe St	Ste 2200	Chicago	IL	60603	United States	Polars DataFrames Data Governance, PII Masking & Lineage Tracking	Medium	✓ Verified 2026
NSNadia SolokovaDirector of Machine Learning & Applied AI Data	Influencer	n.sol**@quant***.com	+1 (617) 55*-**	+1 (617) 55*-**	+1 (617) 55*-**	linkedin.com/in/n*a-sol******	Quantum Insurance Risk Modeling LLC	linkedin.com/company/quantum-insurance-data	Property & Casualty Insurance Actuarial Data	100 Cambridge St 14th	Fl	Boston	MA	02114	United States	Polars DataFrames Machine Learning Feature Store & Model Training	High	✓ Verified 2026
AMAlain MercierVP of Global Enterprise Architecture	Decision Maker	a.mer**@telecom****.fr	+33 (1) 44*-**	+33 (1) 44*-**	+33 (1) 44*-**	linkedin.com/in/a*n-mer******	Telecom France Networks SA	linkedin.com/company/telecom-france-networks	Telecommunications & Cellular Data Networks	78 Rue de Vaugirard	—	Paris	Île-de-France	75006	France	Polars DataFrames Multi-Cloud Data Sharing & Private Clean Rooms	High	✓ Verified 2026
HOHiroshi OgawaSenior Data Platform Solutions Architect	Practitioner	h.og**@tokyodig*****.jp	+81 (3) 55*-**	+81 (3) 55*-**	+81 (3) 55*-**	linkedin.com/in/h*shi-o*******	Tokyo Digital Media Systems KK	linkedin.com/company/tokyo-digital-media	Digital Entertainment & Media Streaming Data	GranTokyo South Tower	32F	Tokyo	Tokyo	100-6632	Japan	Polars DataFrames High-Throughput Analytics & Low-Latency SQL	Medium	✓ Verified 2026

Illustrative rows showing 20 of 60+ fields. Full records are unmasked.

We deliver 60+ data fields per record, including intent data. The sample shows a selection only. Tell us which fields you need, including any that aren't listed here, and we'll add them to your list.

Also available:Seniority & departmentJob functionTenure in roleEmployee countAnnual revenueSIC & NAICS codesParent companyWebsite & domainProduct editionContract & renewal timingIntent topics & scoreEmail verification date

How we prove an account runs Polars DataFrames

No scraped résumés or stale directories. Three layers confirm a live installation before a contact reaches your list. Read our verification protocol

Pillar 1 · Telemetry

Live DNS & script signals

We inspect response headers, DNS MX and CNAME records and live JavaScript bundles to confirm a running Polars DataFrames instance.

DNS · CNAME · MX recordsHTTP headers · JS bundles✓ Verified against live infrastructure

Pillar 2 · Operational intent

90-day hiring signals

We track job requisitions that ask for Polars DataFrames skills, which shows ongoing budget and headcount.

Now hiringPolars DataFrames Administrator

Now hiringPolars DataFrames Architect

Now hiringPolars DataFrames Engineer

Pillar 3 · Human & mailbox QA

Pre-delivery SMTP handshake

Each email is tested against the destination mail server, and titles are checked against current company rosters.

SMTP check ✓LinkedIn match ✓Title verified ✓

✓ Deliverable inbox and current role

Deliverability SLA

<3%

Maximum bounce rate, or we replace the contacts. Your sender reputation stays clean.

1:1 hard bounce replacement

Every email that hard-bounces within 30 days of delivery is replaced free, record for record.

24-hour pre-delivery ping

Live SMTP and MX checks right before we send your file.

Instant CRM import

Clean CSV or XLSX, ready for one-click import.

Ready for your outbound stack

Imports straight into the tools you use.

What are you trying to do?

Each campaign needs a different data strategy. Pick your goal to see how we build the audience.

Find companies on rival platforms and reach the people evaluating a move to, or away from, Polars DataFrames.

Conquesting playbook

Pitch integrations, add-ons and complementary tools to companies already running Polars DataFrames.

Ecosystem playbook

Build account lists around Polars DataFrames deployment scale and engage the full committee across departments.

ABM playbook

Conquesting audienceSample

pandas

Apache Spark

DuckDB

→

Polars DataFrames

Know the stack around every account

Polars DataFrames rarely runs alone. Every record is tagged with the tools detected alongside it, so you can segment by complementary stacks.

Python

Apache Arrow

DuckDB

Jupyter

AWS

Rust

Open the coexistence matrix

What Polars DataFrames customers look like

Anonymised profiles of companies in our Polars DataFrames dataset of 5,004+ accounts. Named accounts are shared in your free sample, never on a public page.

Polars DataFrames account profiles · anonymised

Company size	Industry	Country	Employees	Revenue
Mid-market	Quantitative Finance	United States	200–999	$50M–$250M
Enterprise	Research	United States	5,000+	$1B+
Upper mid-market	SaaS	United Kingdom	1,000–4,999	$250M–$1B
SMB	Bioinformatics	United States	50–199	$10M–$50M
Enterprise	Quantitative Finance	Canada	10,000+	$5B+
Mid-market	Research	Germany	500–999	$100M–$250M
Mid-market	SaaS	United States	200–999	$50M–$250M
Enterprise	Bioinformatics	Australia	5,000+	$1B+

Markets we cover

United States

India

United Kingdom

Canada

Germany

Australia

Singapore

France

New Zealand

Netherlands

Plus 170 more countries. Ask for a country-by-country count of Polars DataFrames accounts and we'll include it with your free sample.

Build the list your way

Every list is assembled from source to your product tier, job function, geography and revenue band. Nothing is a fixed export.

Editions & modules

Filter by what they actually run.

+Polars (Open Source)+Polars Lazy API+Polars Streaming Engine+Polars Cloud+Polars GPU Engine

Firmographics

Match revenue, size and industry.

+Industry+$1M–$10B+ revenue+Headcount+Ownership & funding+HQ & branches

Geography

Target any territory.

+US & Canada+US states+UK, DACH, Nordics+APAC & ANZ+Metro clusters

Need multi-attribute segments? Open the Audience Builder

What Polars DataFrames teams are struggling with

Know the friction before you reach out, and position your product where it matters.

Friction 1

pandas performance limits

ImpactLarge data slows notebooks.

OpportunityOffer performance engineering services.

Friction 2

Scaling beyond one machine

ImpactTeams need distributed compute.

OpportunityPitch cloud compute services.

Friction 3

Code migration from pandas

ImpactRewriting pipelines takes time.

OpportunityOffer migration services.

Intent triggers we track

✓Pipeline performance project

✓Polars Cloud trial

✓Data science team growth

✓Spark cost review

“When you talk to companies who already use Snowflake and AWS about a native integration, the conversion rate skyrockets. Data InfoMetrix gave us the exact user list we needed.”

Director of Growth, a data integration companyName withheld at the client's request

Read the 6.8% Response case study

Polars DataFrames list questions, answered

How many companies use Polars DataFrames?

Data InfoMetrix tracks 5,004 companies using Polars DataFrames, with 22,593 verified contacts at those accounts as of October 2026. The largest groups are Quantitative Finance, Research and SaaS. Counts are estimates from our database and market research, so ask us for a live count for your exact criteria.

Who buys and manages Polars DataFrames inside a company?

Polars DataFrames is usually owned by High-Performance DataFrame Engineering. Decision-makers include Chief Data Officer, VP of Data & Analytics and CTO; Data Platform Lead and Analytics Engineering Manager shape the choice, and Data Engineers and Analytics Engineers use it day to day. Our Polars DataFrames list can include any of these roles.

Which Polars DataFrames products or editions can I target?

You can target accounts by the Polars DataFrames products they run, including Polars (Open Source), Polars Lazy API, Polars Streaming Engine, Polars Cloud and Polars GPU Engine. Tell us which edition or module matters and we filter the list to those accounts.

What other software do Polars DataFrames customers use?

Polars DataFrames customers commonly also run Python, Apache Arrow, DuckDB, Jupyter and AWS. Polars DataFrames is often connected to Delta Lake, so users of those tools overlap heavily. We can filter any list by this kind of stack overlap.

What are the main alternatives to Polars DataFrames?

The main alternatives to Polars DataFrames are pandas, Apache Spark and DuckDB. We also keep verified user lists for DuckDB, which is useful for competitive displacement campaigns.

When are Polars DataFrames customers most likely to buy?

Buying triggers we track for Polars DataFrames accounts include pipeline performance project, Polars Cloud trial, data science team growth and spark cost review. A common pain point is pandas performance limits: large data slows notebooks.

Can Data InfoMetrix deliver a free sample Polars contact list before ordering?

Yes. Data InfoMetrix delivers a complimentary 10-record sample tailored to your target Polars criteria—including target job titles, company size, and region—delivered within one business day with no obligation.

How does Data InfoMetrix guarantee email deliverability for Polars contacts?

Data InfoMetrix provides a 99% email deliverability guarantee on all Polars datasets. Every contact record undergoes real-time email verification within a 30 to 45-day refresh cycle, backed by a 1:1 hard bounce replacement policy for 30 days after delivery.

Sourcing and compliance. Business contacts come from public company sources, professional directories and opt-in publishing partners, then go through SMTP verification and human review. Lists are for B2B outreach: include an unsubscribe link and honour opt-outs (CAN-SPAM), and have a lawful basis such as legitimate interest when you email people in the EU or UK (GDPR). Read how we source and verify data