records-analyser

Profiles structured data files and returns row counts, column statistics, schema inference, missing value counts, and sample rows.

Part of the analyser family.

Install

pip install records-analyser

Requires Python 3.11+.

Usage

Python

from records_analyser import RecordsAnalyser

result = RecordsAnalyser().analyse("survey.csv")

profile = result["profile"]
print(f"Rows: {profile['rows']}, Columns: {profile['columns']}")
for col, info in profile["column_profiles"].items():
    print(f"  {col}: {info['type']}, missing={info['missing']}")

CLI

# Human-readable summary
records-analyser analyse data.csv

# SQLite — profiles all tables
records-analyser analyse results.db

# Machine-readable JSON
records-analyser analyse data.xlsx --json

# Start the HTTP server
records-analyser serve --port 8003

HTTP API

curl -X POST http://localhost:8003/analyse \
  -F "file=@data.csv"

Supported formats

JSON and YAML files that look like configuration objects (not arrays of records) include a warning suggesting document-analyser for prose content.

Output

{
  "format": "csv",
  "file_path": "/path/to/survey.csv",
  "file_size": 4096,
  "profile": {
    "rows": 150,
    "columns": 4,
    "column_profiles": {
      "age": {
        "type": "numeric",
        "min": 18.0, "max": 72.0, "mean": 34.2,
        "median": 32.0, "std": 11.4,
        "q25": 25.0, "q75": 42.0,
        "missing": 2, "missing_pct": 1.3
      },
      "country": {
        "type": "categorical",
        "unique": 12,
        "top_values": {"Australia": 45, "USA": 38},
        "missing": 0, "missing_pct": 0.0
      }
    },
    "sample": [{"age": 28, "country": "Australia"}]
  }
}

SQLite results include a tables key with one profile per table.

The analyser family

Low-level analysis tools. Each accepts files directly and returns structured JSON. Build your own UI or pipeline on top.

Licence

MIT