stratified_sampler
OfficialEfficiently sample structured data across specified categories
Authorcas-bigdatalab
Version1.0.0
Installs0
System Documentation
What problem does it solve?
The stratified_sampler Skill addresses the need for precise and representative sampling of structured data, particularly useful for ensuring balanced representation across categories.
Core Features & Use Cases
- Stratified Sampling: Divides the data into strata based on specified categories and samples from each stratum to create a representative subset.
- Supports Multiple Formats: Operates on JSONL, JSON, CSV, TSV, XLSX, and XLS data formats.
- Use Case: For instance, when you need a balanced training dataset from customer transactions where the number of transactions per customer segment varies.
Quick Start
Execute the stratified_sampler skill by running the following command: python scripts/run_stratified_sampler.py --input data.csv --output sampled.csv --strata_field category --sample_size 100
Dependency Matrix
Required Modules
None requiredComponents
scripts
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: stratified_sampler Download link: https://github.com/cas-bigdatalab/piflow/archive/main.zip#stratified-sampler Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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