Streamlit
データ視覚化 · 918.5K 月間訪問数
Streamlitは、開発者やデータサイエンティストが機械学習やデータサイエンスのための美しいカスタムWebアプリを数分で構築・共有できるようにする、オープンソースのPythonフレームワークです。Streamlit Community Cloudは、これらの公開アプリケーションをデプロイ、管理し、世界と共有するための無料プラットフォームを提供し、協力的なイノベーション環境を育んでいます。
VS
victordibia
データ視覚化 · 22.4K 月間訪問数
応用MLとHCIの第一人者であるVictor Dibiaによる包括的なリソースハブ。AutoGen StudioやLIDAなどのオープンソースAIツール、生成AI、マルチエージェントシステム、ヒューマンコンピュータインタラクションに関する詳細な記事、研究論文、講演を特集しています。開発者、研究者、AI愛好家にとって貴重なプラットフォームです。
製品概要
Streamlit 製品概要
Streamlitは、開発者やデータサイエンティストが機械学習やデータサイエンスのための美しいカスタムWebアプリを数分で構築・共有できるようにする、オープンソースのPythonフレームワークです。Streamlit Community Cloudは、これらの公開アプリケーションをデプロイ、管理し、世界と共有するための無料プラットフォームを提供し、協力的なイノベーション環境を育んでいます。
Preview
victordibia 製品概要
応用MLとHCIの第一人者であるVictor Dibiaによる包括的なリソースハブ。AutoGen StudioやLIDAなどのオープンソースAIツール、生成AI、マルチエージェントシステム、ヒューマンコンピュータインタラクションに関する詳細な記事、研究論文、講演を特集しています。開発者、研究者、AI愛好家にとって貴重なプラットフォームです。
Preview
Detailed feature comparison
| Feature | Streamlit | victordibia |
|---|---|---|
| 主要カテゴリー | データ視覚化 | データ視覚化 |
| 追加日 | 2025-08-17 | 2025-08-04 |
| 価格 | フリーミアム | 無料 |
| 公式サイト | share.streamlit.io | victordibia.com |
| 製品タイプ | ウェブサイト | ウェブサイト |
| Performance data | ||
| ユーザー評価 | 未確認 | 未確認 |
| コメント | 0 | 0 |
| 月間訪問数 | 918.5K | 22.4K |
| 月間成長率 | 6.5% | 33.6% |
| お気に入り | 125 | 128 |
| Details | 詳細を見る | 詳細を見る |
Streamlit vs victordibia monthly traffic
Compare Streamlit and victordibia by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
How to interpret the traffic data
In the Streamlit vs victordibia monthly traffic comparison, Streamlit currently shows 918.5K visits and victordibia shows 22.4K; Streamlit has about 41 times the visible traffic of victordibia, an absolute difference of about 896.1K visits. This reflects visible reach, not feature quality or paid users.
Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.
Streamlit monthly traffic:
Latest traffic
月間訪問数
918.5K
平均滞在時間
3:29
訪問あたりページ数
3.41
直帰率
56.87%
Data updated 2026-06-15
Monthly traffic trend
- 2025/9: 501.6K 月間訪問数
- 2026/1: 717.2K 月間訪問数
- 2026/2: 702.3K 月間訪問数
- 2026/3: 847.5K 月間訪問数
- 2026/4: 862.8K 月間訪問数
- 2026/5: 918.5K 月間訪問数
主要地域
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 44.68% | 410.4K |
| 🇮🇳India | 25.68% | 235.9K |
| 🇰🇷Korea, Republic of | 12.59% | 115.6K |
| 🇬🇧United Kingdom | 8.9% | 81.7K |
| 🇵🇰Pakistan | 8.15% | 74.9K |
流入元
| Source type | Percentage | Traffic |
|---|---|---|
| ダイレクト | 57.82% | 531.1K |
| 参照元 | 40.37% | 370.8K |
| Eメール | 1.81% | 16.6K |
検索キーワード
share.streamlitstreamlitstreamlit cloudstreamlit community cloudstreamlit login
victordibia monthly traffic:
Latest traffic
月間訪問数
22.4K
平均滞在時間
0:22
訪問あたりページ数
1.61
直帰率
39.19%
Data updated 2026-06-15
Monthly traffic trend
- 2025/9: 13.3K 月間訪問数
- 2026/1: 12.2K 月間訪問数
- 2026/2: 11.6K 月間訪問数
- 2026/3: 11.2K 月間訪問数
- 2026/4: 16.8K 月間訪問数
- 2026/5: 22.4K 月間訪問数
主要地域
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 37.7% | 8.5K |
| 🇮🇳India | 25.61% | 5.7K |
| 🇻🇳Vietnam | 14.63% | 3.3K |
| 🇬🇧United Kingdom | 11.38% | 2.6K |
| 🇮🇩Indonesia | 10.68% | 2.4K |
検索キーワード
anthropic designblender export to react fiberclaude.ai/designclaude designhandtrack.js
Traffic-based selection guidance: If public market visibility is an important first-pass criterion, investigate Streamlit first. The final choice should still follow taxonomy, use case, and a real trial because higher traffic does not prove broader capabilities or better workflow fit.
Usage comparison
Compare the core capabilities of Streamlit and victordibia
Streamlit Core features
データ視覚化
ローコード・ノーコード
アプリビルダー
victordibia Core features
データ視覚化
ローコード・ノーコード
研究
ライティング
Use cases
Streamlit Use cases
データ視覚化
開発者ツール
機械学習
オープンソース
アプリビルダー
ダッシュボード
データサイエンス
Python
Webアプリ
victordibia Use cases
データ視覚化
開発者ツール
機械学習
オープンソース
AI教育
AI研究
AutoGen
生成AI
HCI
マルチエージェントシステム
Streamlit vs victordibia：In-depth comparison and selection guidance
First decide whether the products solve the same kind of need
This in-depth Streamlit vs victordibia comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Streamlit is primarily listed under “データ視覚化”, while victordibia is primarily listed under “データ視覚化”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Pricing (Streamlit: Freemium; victordibia: Free); Monthly visits (Streamlit: 918.5K; victordibia: 22.4K); Monthly growth (Streamlit: 6.5%; victordibia: 33.6%); Favorites (Streamlit: 125; victordibia: 128); Website (Streamlit: share.streamlit.io; victordibia: victordibia.com). These facts are more useful for selection than brand visibility alone.
What market visibility and monthly traffic mean
In the Streamlit vs victordibia monthly traffic comparison, Streamlit currently shows 918.5K visits and victordibia shows 22.4K; Streamlit has about 41 times the visible traffic of victordibia, an absolute difference of about 896.1K visits. This reflects visible reach, not feature quality or paid users.
Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.
If public market visibility is an important first-pass criterion, investigate Streamlit first. The final choice should still follow taxonomy, use case, and a real trial because higher traffic does not prove broader capabilities or better workflow fit.
Product positioning, use cases, and roles
Streamlit and victordibia currently overlap in shared categories: データ視覚化、ローコード・ノーコード; shared tags: データ視覚化、開発者ツール、機械学習、オープンソース. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
Streamlit's unique categories/tags are アプリビルダー、ダッシュボード、データサイエンス、Python、Webアプリ; victordibia's are 研究、ライティング、AI教育、AI研究、AutoGen、生成AI、HCI、マルチエージェントシステム. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
What ratings, comments, and favorites can tell you
Streamlit has no verified rating, 0 comments, 125 favorites, and 125 likes；victordibia has no verified rating, 0 comments, 128 favorites, and 120 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Selection guidance by actual need
When to evaluate Streamlit first
Put Streamlit on the priority trial list when the task aligns with “データ視覚化” and especially アプリビルダー、ダッシュボード、データサイエンス、Python、Webアプリ. This follows recorded positioning and does not imply unlisted capabilities are absent.
Streamlit also currently records: pricing is freemium, product type is website, 918.5K verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
When to evaluate victordibia first
Put victordibia on the priority trial list when the task aligns with “データ視覚化” and especially 研究、ライティング、AI教育、AI研究、AutoGen、生成AI. This follows recorded positioning and does not imply unlisted capabilities are absent.
victordibia also currently records: pricing is free, product type is website, 22.4K verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
How to validate the recommendation before deciding
The available data describes positioning, public visibility, and community signals, but it cannot prove output quality, speed, integration effort, privacy, or long-term cost in your workflow. Before deciding, run the same representative tasks in Streamlit and victordibia, then record completion time, accuracy, manual corrections, and the real paid threshold. A like-for-like trial turns this comparison into a defensible adoption decision.
比較 FAQ
How should I choose between Streamlit and victordibia?
Compare positioning, pricing, taxonomy, and traffic maturity, then verify the latest details on each official website.
Where does this comparison data come from?
The factual baseline is derived from product, taxonomy, traffic, and community data. Reviewed editorial conclusions show their source and verification date.
What do unknown fields mean?
Unknown means there is not enough reliable evidence; the page does not fill gaps with assumptions.