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explore-data

Profile datasets to assess shape, quality, and patterns.

4|4|Updated Dec 15, 2024
Search Tags:#quality#profiling#exploration#data-analysis#dataset#profile
One-click install
npx skills add https://github.com/adrianliechti/wingman-chat --skill explore-data-adrianliechti
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: explore-data
Source: https://github.com/adrianliechti/wingman-chat/tree/main/skills/data/explore-data
Command: npx skills add https://github.com/adrianliechti/wingman-chat --skill explore-data-adrianliechti

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.

Core Features & Use Cases

  • Profiling: Generate a complete dataset profile including row counts, column counts, data types, and null statistics.
  • Quality Assessment: Identify duplicates, suspicious values, and potential data integrity issues.
  • Guided Analysis: Recommend dimensions and metrics to prioritize for initial exploration.

Quick Start

Upload a dataset (warehouse table or file) and run the explore-data skill to generate a profile.

Frequently Asked Questions about explore-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I profile a dataset to check data quality and null rates?▼

To profile a dataset and check data quality, you can generate a complete profile including row counts, column data types, and null statistics to understand its overall shape and integrity.

Can I run data profiling on CSV, Excel, Parquet, and JSON files?▼

Yes, data profiling works with CSV, Excel, Parquet, and JSON files, as well as warehouse tables, computing per-column statistics and classifications to assess rows, columns, and distributions.

How do I identify duplicates and suspicious values in a new table?▼

You can identify duplicates and suspicious values by inspecting the dataset to detect potential data integrity issues, calculating null rates, and reviewing column distributions for anomalies.

What is the best way to classify columns as dimensions or metrics for analysis?▼

The best way to classify columns as dimensions or metrics is by computing per-column statistics that categorize fields into dimension, metric, date, or identifier classifications for guided analysis.

Does data exploration require any specific environment setup or dependencies?▼

Data exploration requires no specific dependencies, allowing you to directly upload a warehouse table or file and run the profiling process to output a concise data profile with quality flags.

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