DataFrame Doctor: Validate datasets fast
A lightweight, browser-first diagnostic for quickly validating CSV and Excel datasets. Inspect structure, detect issues, and preview data before analysis or modeling — built for engineers, analysts, and ML practitioners.
Perfect for
- ✓Preparing datasets for ML assignments
- ✓Auditing CSV dumps before importing into Python
- ✓Validating Excel exports from CRM/ERP tools
- ✓Sanitizing messy data before ETL pipelines
Analysis
Structure & Types
Instant dtypes and shape report.
Detection
Quality Issues
Flags problems before modeling.
Privacy
In-Memory
Never stored or logged.
How it fits into your workflow
1. Drop your CSV/XLSX
Upload files up to ~10MB. The tool parses headers, infers column types, and checks for common issues like missing values or mixed dtypes.
2. Review structure & issues
See a breakdown of column names, inferred types, null counts, and data-quality warnings. Preview the first few rows inline.
3. Export JSON for CI
Download the report as structured JSON to integrate into your data pipelines, validation scripts, or CI checks.
💡 Extensible: The backend is Flask + Pandas. You can fork and add custom validation rules, schema checks, or connect to your own ETL toolchain.
Upload & analyze your dataset
Drop your CSV or Excel file below to get an instant structural analysis. The tool runs in-memory and never stores your data.
Drop a CSV or XLSX file here, or click to choose. The tool inspects structure, dtypes, missing values, and highlights common data-quality issues before you start analysis.
Accepted: CSV & XLSX · Ideal size: up to 10MB · Recommended: <200 columns · Files are processed in-memory and not stored or logged. Most uploads analyze in under 150ms for small files.
Up to ~10MB recommended. We don’t store your data.
API for developers
Integrate DataFrame Doctor into your CI/CD pipelines, validation scripts, or data processing workflows with our simple REST API.
Flask + Pandas Backend
This tool runs a Flask + Pandas backend with CORS enabled. POST files to /analyze to receive a stable, predictable JSON report suitable for pipelines, CI checks, or quick validation scripts.
Example cURL Request
curl -F "file=@/path/to/data.csv" https://threensofts-data-tools.onrender.com/analyzeResponse Format:
Returns a JSON object with success boolean and data object containing:
- filename, shape (rows × columns)
- columns array with dtypes mapping
- missing_values count per column
- issues array flagging quality problems
- preview with first N rows
Error Handling:
On failure, returns success: false with message describing the error.