data-analysis

To compute summary statistics for a CSV file, you can use a script that calculates row counts, column counts, data types, non-null counts, and basic numeric statistics. It processes structured datasets to generate a structured JSON payload containing numeric stats and anomalies.

Anomaly detection in CSV data identifies outliers and summarizes key trends across your structured datasets. It works by computing basic numeric statistics and non-null counts, then gracefully handling non-numeric values to produce a formatted report highlighting anomalies.

Yes, you can analyze JSON files to generate structured data reports. The analysis identifies key data characteristics from CSV and JSON files by computing counts, data types, and numeric statistics, resulting in a human-readable report suitable for dashboards.

Yes, the CSV analysis tool gracefully handles non-numeric values. While computing basic numeric statistics for numeric columns, it processes mixed data types seamlessly to ensure your structured JSON payload accurately reflects data types and non-null counts.

The best way to turn raw CSV data into markdown tables is using a script that computes summary statistics and detects anomalies. It generates human-readable reports and markdown tables directly from your structured datasets, suitable for presentations and dashboards.