Python Technology Adoption in E-commerce

Discover 42.8k verified e-commerce stores using Python. Analyze technology adoption, market trends, integration patterns, and business insights.

Detected across 58.2k domains. 42.8k verified as active stores with physical delivery.

Verified stores
42.8k
active stores with physical delivery
Top country
United States
5,541 verified stores
Top payment signal
PayPal
2,746 detection occurrences
Top carrier signal
UPS
1,864 detection occurrences
Top category
Health & Beauty
5,649 verified stores

This is a public report preview. Use the catalog, exports or API for account lists. Fields depend on your plan; coverage varies by merchant.

42.8k verified active stores out of 58.2k detected · 73.5% verification rate

E-commerce Platforms Distribution

Custom stores with 95.8% of stores with physical delivery (40,957 stores) , followed by OpenTiendas (1.0%) and Booqable (0.8%) .

Base: 42,769 active verified stores, including those without a detected platform. Rankings show leading platform combinations; displayed percentages need not sum to 100%. Digital products, services, and non-transactional sites are excluded. All stores are verified by our proprietary AI model to ensure accurate e-commerce classification.

Top 10 Platforms (Physical Delivery)

Custom 95.8%

40,957 stores

445 stores

Booqable 0.8%

348 stores

249 stores

212 stores

Makane 0.2%

103 stores

71 stores

D2P Shop 0.1%

45 stores

37 stores

37 stores

Visual Distribution

Distribution of top 10 platforms by stores with physical delivery (June 2026)
E-commerce platforms distribution for stores with physical delivery in Python
Platform Stores with Physical Delivery Percentage
Custom 40957 95.76%
OpenTiendas 445 1.04%
Booqable 348 0.81%
WooCommerce 249 0.58%
Shoppub 212 0.50%
Makane 103 0.24%
Shopify 71 0.17%
D2P Shop 45 0.11%
HCL Commerce 37 0.09%
BigCommerce 37 0.09%
Show broader dataset (all e-commerce signals)

Websites with E-commerce Signals

Custom holds 70.4% share of detected Python e-commerce signals (40,957) , with OpenTiendas at 0.8% and Booqable at 1.7% (June 2026) .

Base: 58,208 domains in this report's signal population, including domains without a detected platform. This is not a store count: it can include redirects and non-transactional sites. Displayed percentages need not sum to 100%.

Top 10 Platforms (All Signals)

Custom 70.4%

40,957 websites

453 websites

Booqable 1.7%

1,016 websites

746 websites

213 websites

Makane 0.3%

146 websites

91 websites

D2P Shop 0.1%

45 websites

1,146 websites

260 websites

Visual Distribution

Distribution of top 10 platforms by websites with signals (June 2026)
E-commerce platforms distribution for websites with signals in Python
Platform Websites with Signals Percentage
Custom 40957 70.36%
OpenTiendas 453 0.78%
Booqable 1016 1.75%
WooCommerce 746 1.28%
Shoppub 213 0.37%
Makane 146 0.25%
Shopify 91 0.16%
D2P Shop 45 0.08%
HCL Commerce 1146 1.97%
BigCommerce 260 0.45%

Categories Distribution

Health & Beauty stores with 13.2% of all stores (5,649 stores) , followed by Clothing & Accessories (12.3%) and Food & Beverages (9.5%) .

Base: 42,769 active verified stores, including stores without a detected category. Percentages use this full population, not only the displayed categories.

Top 10 Categories

Health & Beauty 13.2%

5,649 stores

Clothing & Accessories 12.3%

5,279 stores

Food & Beverages 9.5%

4,069 stores

Electronics & Technology 7.4%

3,147 stores

Sporting Goods & Outdoor Gear 6.4%

2,728 stores

Home & Garden 5.3%

2,283 stores

Tools & Hardware 4.8%

2,060 stores

Business & Industrial 4.3%

1,838 stores

Toys & Games 2.8%

1,209 stores

Gifts & Special Occasions 2.6%

1,115 stores

Visual Distribution

Distribution of top 10 Python store categories (June 2026)
Distribution of categories in Python e-commerce stores
Category Number of Stores Percentage
Health & Beauty 5649 13.21%
Clothing & Accessories 5279 12.34%
Food & Beverages 4069 9.51%
Electronics & Technology 3147 7.36%
Sporting Goods & Outdoor Gear 2728 6.38%
Home & Garden 2283 5.34%
Tools & Hardware 2060 4.82%
Business & Industrial 1838 4.30%
Toys & Games 1209 2.83%
Gifts & Special Occasions 1115 2.61%

Payment Providers Distribution

PayPal accounts for 24.7% of displayed detection occurrences (2,746 occurrences) , followed by Stripe Connect (14.8%) and MasterCard (13.9%) .

Base: 11,106 provider detection occurrences in the displayed ranking, from active stores. One store can contribute multiple occurrences. These percentages are not the share of unique stores or all detections.

Detected from checkout/payment scripts and provider tags; off-site/manual payments excluded where undetectable.

Top 10 Payment Providers

PayPal 24.7%

2,746 occurrences

1,641 occurrences

1,539 occurrences

1,365 occurrences

VISA 8.9%

993 occurrences

Paytm 5.7%

637 occurrences

609 occurrences

Bolt 4.8%

538 occurrences

533 occurrences

505 occurrences

Visual Distribution

Distribution of top 10 Python payment providers (June 2026)
Distribution of payment providers in Python e-commerce stores
Payment Provider Detection Occurrences Share of Displayed Occurrences
PayPal 2746 24.73%
Stripe Connect 1641 14.78%
MasterCard 1539 13.86%
American Express 1365 12.29%
VISA 993 8.94%
Paytm 637 5.74%
Klarna 609 5.48%
Bolt 538 4.84%
Apple Pay 533 4.80%
Google Pay 505 4.55%

Shipping Carriers Distribution

UPS accounts for 24.8% of displayed detection occurrences (1,864 occurrences) , followed by DHL (15.7%) and FedEx (14.2%) .

Base: 7,525 carrier detection occurrences in the displayed ranking, from active stores. One store can contribute multiple occurrences. These percentages are not the share of unique stores or all detections.

Detected from checkout/shipping pages and shipment widgets; cross-border carriers included where visible.

Top 10 Shipping Carriers

UPS 24.8%

1,864 occurrences

DHL 15.7%

1,182 occurrences

FedEx 14.2%

1,070 occurrences

USPS 11.9%

896 occurrences

DPD 9.1%

688 occurrences

GLS 6.6%

493 occurrences

467 occurrences

JNE 4.4%

334 occurrences

278 occurrences

253 occurrences

Visual Distribution

Distribution of top 10 Python shipping carriers (June 2026)
Distribution of shipping carriers in Python e-commerce stores
Shipping Carrier Detection Occurrences Share of Displayed Occurrences
UPS 1864 24.77%
DHL 1182 15.71%
FedEx 1070 14.22%
USPS 896 11.91%
DPD 688 9.14%
GLS 493 6.55%
СДЭК 467 6.21%
JNE 334 4.44%
Hermes 278 3.69%
Colissimo 253 3.36%

Store Launch Timeline

Over the past 12 months, 10,194 new stores were launched in Python , with the highest activity in 2026-05 (1,194 stores) , demonstrating ongoing growth in the e-commerce market.

Last 12 Months

2026-05 11.7%

1,194 stores launched

2026-04 10.8%

1,100 stores launched

2026-03 10.6%

1,084 stores launched

2026-02 7.9%

802 stores launched

2026-01 9.7%

993 stores launched

2025-12 8.4%

858 stores launched

2025-11 8.1%

822 stores launched

2025-10 8.3%

843 stores launched

2025-09 7.7%

784 stores launched

2025-08 7.1%

720 stores launched

2025-07 6.6%

671 stores launched

2025-06 3.2%

323 stores launched

Monthly Trend

Monthly store launches in Python (June 2026)
Monthly store launches in Python over the past 12 months
Month Stores Launched Percentage
2026-05 1194 11.71%
2026-04 1100 10.79%
2026-03 1084 10.63%
2026-02 802 7.87%
2026-01 993 9.74%
2025-12 858 8.42%
2025-11 822 8.06%
2025-10 843 8.27%
2025-09 784 7.69%
2025-08 720 7.06%
2025-07 671 6.58%
2025-06 323 3.17%

Active Stores by Launch Year

Currently, 39,523 active stores in Python are tracked in our database , with the most stores launched in 2026 (5,272 stores still active) . This shows the distribution of currently active stores by their original launch year.

Active Stores by Launch Year

2026 13.3%

5,272 active stores

2025 20.0%

7,918 active stores

2024 12.1%

4,800 active stores

2023 10.2%

4,039 active stores

2022 5.7%

2,266 active stores

2021 5.8%

2,307 active stores

2020 5.6%

2,233 active stores

2019 3.7%

1,473 active stores

2018 3.4%

1,359 active stores

2017 3.0%

1,197 active stores

2016 1.7%

674 active stores

2015 1.5%

596 active stores

2014 1.4%

548 active stores

2013 2.6%

1,045 active stores

2012 1.3%

529 active stores

Distribution by Launch Year

Active stores in Python by original launch year (June 2026)
Active stores in Python by launch year
Launch Year Active Stores Percentage
2026 5272 13.34%
2025 7918 20.03%
2024 4800 12.14%
2023 4039 10.22%
2022 2266 5.73%
2021 2307 5.84%
2020 2233 5.65%
2019 1473 3.73%
2018 1359 3.44%
2017 1197 3.03%
2016 674 1.71%
2015 596 1.51%
2014 548 1.39%
2013 1045 2.64%
2012 529 1.34%

Social Media Usage

Facebook accounts for 28.9% of displayed detection occurrences (10,524 occurrences) , followed by Instagram (25.1%) and YouTube (10.9%) .

Base: 36,397 social-platform detection occurrences in the displayed ranking, from active stores. One store can contribute multiple occurrences. These percentages are not the share of unique stores or all detections.

Links and embeds detected on public pages; private/hidden profiles not counted.

Top 10 Social Platforms

Facebook 28.9%

10,524 occurrences

Instagram 25.1%

9,126 occurrences

YouTube 10.9%

3,960 occurrences

LinkedIn 8.9%

3,235 occurrences

Twitter 8.4%

3,059 occurrences

WhatsApp 6.1%

2,232 occurrences

Pinterest 4.3%

1,578 occurrences

TikTok 4.2%

1,542 occurrences

X 3.0%

1,081 occurrences

Vimeo 0.2%

60 occurrences

Visual Distribution

Distribution of social media platforms in Python (June 2026)
Distribution of social media platforms in Python e-commerce stores
Social Platform Detection Occurrences Share of Displayed Occurrences
Facebook 10524 28.91%
Instagram 9126 25.07%
YouTube 3960 10.88%
LinkedIn 3235 8.89%
Twitter 3059 8.40%
WhatsApp 2232 6.13%
Pinterest 1578 4.34%
TikTok 1542 4.24%
X 1081 2.97%
Vimeo 60 0.16%

Geographic Distribution

United States stores with 13.0% (5,541 active verified stores) , followed by India (6.2%) and Belgium (4.7%) .

Base: 42,769 active verified stores, including stores with unknown country. Percentages use the full population, not only the displayed countries.

Top 10 Countries

5,541 active verified stores

India 6.2%

2,660 active verified stores

2,012 active verified stores

Spain 4.5%

1,932 active verified stores

1,848 active verified stores

1,805 active verified stores

1,601 active verified stores

1,379 active verified stores

1,042 active verified stores

933 active verified stores

Visual Distribution

Geographic distribution of stores using Python (June 2026)
Geographic distribution of stores using Python
Country AI-Verified Store Count Market Share
United States 5541 12.96%
India 2660 6.22%
Belgium 2012 4.70%
Spain 1932 4.52%
Vietnam 1848 4.32%
France 1805 4.22%
Russia 1601 3.74%
Germany 1379 3.22%
Indonesia 1042 2.44%
United Kingdom 933 2.18%

Data Coverage

Coverage among all domains with this detected signal, including non-active profiles; not just active stores.

Field Rows count Coverage
Website 58.2k 100.00%
Emails 34.2k 58.83%
Phones 25.4k 43.71%
Ecommerce platform 48.2k 82.89%
Couriers 17.3k 29.70%
Payment Providers 18.4k 31.58%
Categories 34.7k 59.56%
Social Media 21.8k 37.40%

Coverage reflects detectability from public website analysis. Payment providers and shipping carriers are identified from checkout pages and shipping widgets. Stores using off-site payment flows or custom shipping integrations may not be detected. Actual adoption rates are higher than shown.

Market Intelligence

On-demand market analysis

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  • Periodic index updates
  • Market sizing and segmentation
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Strategic Intelligence

Custom research and competitive analysis

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Market Intelligence

Understand the Python e-commerce landscape:

Analyze 42.8k verified e-commerce stores on Python - technology adoption, market share, competitive positioning.

Track Competitive Shifts

Monitor platform migrations, technology adoption, and market share changes across Python e-commerce.

Explore Data

Size Your Addressable Market

Segment stores by country, platform, category, PSP, carrier, technology, and merchant-size signals to quantify your market opportunity.

Explore Data

Monitor Adoption Trends

Identify which payment providers, shipping carriers, and platforms are gaining or losing traction.

Explore Data

Data Access

  • Interactive search and filtering via web app
  • REST API for programmatic access and automation
  • Index updates; historical datasets by agreement

Use Cases

  • Competitive intelligence and market sizing
  • Technology adoption and platform migration analysis
  • Sales prospecting and account-based targeting
  • Investment research and due diligence

Frequently Asked Questions

What data is included in the Python e-commerce intelligence?

We track 42.8k verified Python e-commerce stores whose profiles can include: e-commerce platform, product counts, categories, payment providers, shipping carriers, technology stack, merchant-size signals, and available business contact fields.

How is the data collected and how often is it updated?

We rebuild the searchable index typically every 2–4 weeks. Observation dates vary by source and field; a rebuild does not mean every website was observed again. This public report has its own publication date and may use an older snapshot than the live catalog. Coverage varies by field and market. A missing payment or carrier signal means we did not detect it, not that the merchant does not use it.

How do I access the data?

Self-service plans give you instant access through our web app and REST API. You can search, filter, and segment stores interactively or integrate programmatically into your workflows. View plans →

Can I filter by specific segments?

Yes. You can segment by country, platform, product category, payment provider, shipping carrier, technology stack, store age, cross-border signals, and merchant-size bands. Some advanced signals are plan-gated, and the same field contract is used by the web app, exports, and API.

Do you offer enterprise or custom licensing?

Yes. For large-scale data needs, custom market reports, or dedicated support, contact us to discuss enterprise pricing.

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