op app 
op is a data-analysis application for using code and AI assistance to explore datasets, build analyses, and produce results more easily.
Starting price Free + paid plans
Tool Information
op is a data-analysis application for using code and AI assistance to explore datasets, build analyses, and produce results more easily.
Users load governed data, write or generate code, inspect queries and calculations, reproduce outputs, visualize findings, and share only validated results.
Free or trial access is available with paid capacity; stable numeric public pricing was not verified.
Generated code can be wrong, insecure, or irreproducible and can expose sensitive data. Sandboxing, access control, versioning, and analyst review are required.
F.A.Q (3)
Pros and Cons
Pros
- op combines a spreadsheet view with code notebooks and AI assistance
- The table stays visually synchronized with the working dataframe
- Natural-language questions produce context-relevant analysis code
- Generated code reduces time spent looking up pandas syntax
- Users can inspect the actual program instead of receiving only an opaque answer
- The notebook format supports iterative exploration
- A spreadsheet-like interface keeps source values visible beside the computation
- The hybrid workflow lowers the barrier between no-code tables and Python analysis
- Generated snippets can be modified by an experienced analyst
- Questions can be refined as the investigation develops
- The approach supports cleaning; aggregation; and visualization tasks
- Executable code makes an analysis more reproducible than a prose-only chatbot response
- The browser app requires no local notebook installation
- A free trial is offered
- No credit card is required to start that trial
- The focused design suits analysts who want help with code rather than a fully automatic BI layer
Cons
- The official public page provides only a brief product description
- Current plan prices and limits are not disclosed on that page
- AI-generated code can execute successfully while answering the wrong analytical question
- A dataframe preview may conceal truncated rows or coerced data types
- Generated pandas operations can consume excessive memory on large files
- Notebook execution order can make a result difficult to reproduce
- Users still need enough Python knowledge to review and repair code
- Charts can use misleading scales; filters; or aggregations
- Uploaded business data may include regulated or confidential records
- Dependencies or library versions can make suggested code fail
- The service does not replace a governed data warehouse or semantic metric layer
- No broad connector; collaboration; or enterprise-governance catalog is documented publicly
- The generic name makes product support and current status harder to research
- A trial without published conversion terms makes future cost planning difficult
- Insights inherit missing values; sampling bias; and errors in the source dataset
- Analysts must verify code; types; units; statistics; data rights; and every reported conclusion
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Overview
Before you subscribe
- Check the currency, billing period, automatic renewal, usage limits and seats at checkout — prices and promotions change.
- App-store pricing can differ from website pricing by country, device and tax.
- Share only authorised data, and read the provider's encryption, retention and model-training terms first.
- AI output can be incomplete or wrong. Verify facts, citations, calculations and code before you publish or ship.
- Commercial usage rights vary by plan, provider terms and jurisdiction.
- None of these tools replaces a qualified professional for medical, legal or financial decisions.
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