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investigate-dataset

Inspect HuggingFace, CSV, and JSON datasets for schema and quality.

617|397|Updated Oct 2, 2024
Search Tags:#data quality#JSON#pandas#HuggingFace#investigate#CSV#datasets
One-click install
npx skills add https://github.com/UKGovernmentBEIS/inspect_evals --skill investigate-dataset
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: investigate-dataset
Source: https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/.claude/skills/investigate-dataset
Command: npx skills add https://github.com/UKGovernmentBEIS/inspect_evals --skill investigate-dataset

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps data practitioners understand the structure, fields, and quality of datasets from HuggingFace, CSV files, and JSON/JSONL files, enabling better data governance and evaluation workflows.

Core Features & Use Cases

  • Dataset exploration: Inspect schema, features, and sample records for HuggingFace datasets and flat files (CSV/JSON).
  • Quality assessment: Identify missing values, data-type issues, and distributional irregularities to guide cleaning and preprocessing.
  • In-memory analysis: Convert raw records to Python-friendly representations for rapid prototyping and evaluation.
  • Use Case: Before model training, quickly explore a new dataset to understand its columns, data types, and value distributions to inform feature engineering.

Quick Start

Install the necessary Python packages (e.g., pandas and datasets). Then load and inspect a dataset using lightweight commands:

  • from datasets import load_dataset
  • ds = load_dataset("org/dataset-name", split="train")
  • df = ds.to_pandas() # optional for small datasets
  • print(df.head())

Frequently Asked Questions about investigate-dataset

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

FAQPage Schema
How do I inspect the schema and fields of a HuggingFace dataset?▼

You inspect a HuggingFace dataset by loading the data split and converting it to a pandas DataFrame to view sample records, enabling rapid schema discovery and feature exploration for model evaluations.

What is the best way to check data quality in CSV and JSONL files?▼

Checking data quality in CSV and JSONL files involves loading raw records into memory to identify missing values, data-type issues, and distributional irregularities. This guides necessary data cleaning and preprocessing steps.

Can I use pandas to explore JSON datasets for model evaluation?▼

Yes, you can use pandas to explore JSON datasets for model evaluation by converting raw records into Python-friendly in-memory representations, allowing you to generate basic statistics and inspect sample data.

How do I identify missing values and data-type issues before model training?▼

You identify missing values and data-type issues by applying data quality checks to your loaded dataset, generating basic statistics to spot distributional irregularities that inform feature engineering and preprocessing.

Does this approach work with both flat files and HuggingFace datasets?▼

Yes, this approach works with both flat files like CSV and JSON/JSONL and HuggingFace datasets, enabling schema discovery, sample inspection, and data quality checks across different data formats.

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