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v0.4.0
Documentation

Getting Started

  • Introduction
  • Installation
  • Quick Start
  • Tutorial Notebooks
  • Benchmarks
  • Development Setup
  • Contributing

Core Concepts

  • Architecture Overview
  • Dataset Layer
  • Connectors
  • Profiling Engine
  • Rule Engine
  • Dataset Review Engine
  • ML Readiness Score
  • Target Leakage Detection
  • Dataset Diff Engine
  • Target Column Concept
  • Mental Model & Workflow
  • Interpreting Findings
  • Workflow Cheat Sheet
  • Beginner Glossary

Python SDK

  • load()
  • profile()
  • analyze()
  • review()
  • diff()
  • score()
  • plan()
  • Dataset
  • Data Models
  • Profile Models
  • Rule & Finding Models
  • Review Models
  • Score Models
  • Leakage Models
  • Diff Models
  • Exceptions
  • Plugins

CLI Reference

  • analyze
  • review
  • diff
  • score
  • plan
  • Configuration

Guides

  • CI/CD Integration
  • Custom Rules
  • Writing Plugins

Resources

  • Release Notes
  • FAQ
  • Troubleshooting
DocsGuide

Getting Started

  • Introduction
  • Installation
  • Quick Start
  • Tutorial Notebooks
  • Benchmarks
  • Development Setup
  • Contributing

Core Concepts

  • Architecture Overview
  • Dataset Layer
  • Connectors
  • Profiling Engine
  • Rule Engine
  • Dataset Review Engine
  • ML Readiness Score
  • Target Leakage Detection
  • Dataset Diff Engine
  • Target Column Concept
  • Mental Model & Workflow
  • Interpreting Findings
  • Workflow Cheat Sheet
  • Beginner Glossary

Python SDK

  • load()
  • profile()
  • analyze()
  • review()
  • diff()
  • score()
  • plan()
  • Dataset
  • Data Models
  • Profile Models
  • Rule & Finding Models
  • Review Models
  • Score Models
  • Leakage Models
  • Diff Models
  • Exceptions
  • Plugins

CLI Reference

  • analyze
  • review
  • diff
  • score
  • plan
  • Configuration

Guides

  • CI/CD Integration
  • Custom Rules
  • Writing Plugins

Resources

  • Release Notes
  • FAQ
  • Troubleshooting
HomeDocsWorkflow Cheat Sheet

Core Concepts

Workflow Cheat Sheet

Quick reference mapping developer questions to Featuresmith APIs

Use this cheat sheet as a rapid lookup guide to map common data engineering tasks directly to Featuresmith SDK entrypoints and CLI commands.

SDK & CLI Task Mapping

Task / QuestionPython SDK FunctionCLI Command
Ingest CSV, Excel, Parquet, or DataFramefs.load(source)featuresmith analyze data.csv
Extract raw statistical profile without rulesfs.profile(dataset)featuresmith analyze data.csv
Check atomic quality rules & target leakagefs.analyze(ds, target_column="y")featuresmith analyze data.csv --target y
Run complete 10-reviewer dataset code reviewfs.review(ds, target_column="y")featuresmith review data.csv --target y
Run diff-aware review (compare with previous snapshot)fs.review(ds, previous=prev, target_column="y")featuresmith review data.csv --previous prev.csv --target y
Extract 0–100 ML Readiness Scorecardfs.score(review_result)featuresmith review data.csv --target y
Compare snapshot versions (Dataset Diff)fs.diff(old, new, target_column="y")featuresmith diff old.csv new.csv --target y
Extract RuleFinding objects from difffs.diff_findings(diff_result)featuresmith diff old.csv new.csv --format json
Render text report for console / logsfs.render(review_result)featuresmith review data.csv
Render diff text report for consolefs.render_diff(diff_result)featuresmith diff old.csv new.csv

Explore

  • Quick Start
  • Python SDK
  • CLI Reference
  • Examples
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Featuresmith

Open-source data profiling and validation for Python engineers.

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  • CLI Reference

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