Execute pandas data analysis code securely with automatic safety checks and smart suggestions. Returns results or error messages.
MIT
203,815 tools. Last updated 2026-06-14 21:10
"pandas" matching MCP tools:
- Execute SQL queries against the in-process chDB OLAP engine to analyze data from Parquet, CSV, JSON, and pandas DataFrames. Returns results in multiple formats with configurable limits.Apache 2.0
- Get all lap times for a Formula 1 driver in a session, with summary statistics and optional CSV export for data analysis.MIT
- Analyze your code for breaking changes before upgrading a dependency. Compares API differences between current and target versions and reports line-level issues with severity and fix hints.MIT
- Filter rows in a local .xlsx file using AND-combined predicates (eq, contains, gt, etc.). Server-side formula evaluation returns accurate matching rows as a markdown table.MIT
- Interpret column values to reveal unique entries, counts, data types, and nulls. Useful for understanding categorical fields, codes, or abbreviations.MIT
Matching MCP Servers
- AlicenseAqualityFmaintenanceEnables LLMs to safely execute pandas code for data analysis and generate interactive visualizations through the Model Context Protocol.Last updated444MIT
- AlicenseAqualityAmaintenanceAn MCP server for chDB, the in-process SQL OLAP engine powered by ClickHouse. Lets agents query Parquet, CSV, JSON, and pandas DataFrames with one tool — no separate server, no Docker.Last updated7Apache 2.0
- Retrieve sampled telemetry data for a specific F1 lap, including speed, throttle, brake, gear, and DRS. Optionally export to CSV for analysis.MIT
- Recover cell formatting (number formats, fonts, fills) discarded by pandas, enabling LLMs to interpret date serials, currency, and percentages correctly.MIT
- Count unique values in a spreadsheet column, sorted by frequency with percentage. Shows distribution of categorical data in a markdown table.MIT
- Analyze tire stint data for Formula 1 races, providing per-driver stint details, pace calculations, and strategy summaries. Export to CSV for data analysis.MIT
- List external workbook references in an .xlsx file, identify absolute paths and network shares that break when the file moves. Audit formula dependencies before sharing workbooks.MIT
- Retrieve formatted Python package documentation from PyPI, extracting installation instructions, usage examples, and API references to support development tasks.MIT
- Extract every form control (Check Box, Drop-down, etc.) from an Excel workbook with linked cell, current value, and source ranges. Ideal for documenting interactive dashboards or auditing user-changeable cells.MIT
- Identify merged cell regions in Excel workbooks with master values, ranges, and layout heuristics to recover visual structure lost in pandas reads.MIT
- Check which Python third-party libraries are installed on the device before importing them in scripts. Lists packages available in the AScript App environment.MIT
- Send input to a running process and automatically receive the response. Ideal for analyzing local files via REPLs like Python or bash.
- Evaluate Excel formulas and cell references in a .xlsx file to retrieve live computed values, bypassing stale caches.MIT
- Export tracked ENCODE experiments as a CSV, TSV, or JSON table with metadata, publication counts, and PMIDs. Ideal for manuscripts, reports, and further literature analysis.AGPL 3.0
- Submit a Python function for remote execution on a Globus Compute endpoint and receive a task ID for asynchronous tracking.MIT
- Reshape a flat table from a .xlsx file into a 2D matrix, aggregating values across index and columns. Choose from sum, mean, min, max, count, or count_distinct.MIT