Data Preparation
The Data Editor is where raw files become analysis-ready datasets — load, inspect, clean, transform, and merge your data, then send it straight to the labs.
Overview
The Data Editor is a spreadsheet-style workspace built for getting data ready to analyze. Open a file, work through it tab by tab, and apply cleaning and transformation steps in place — with undo / redo and keyboard shortcuts throughout. When the dataset is ready, hand it to an analysis lab with Send to Statistical Lab.
New here? Start with the Loading Data guide to get a file in, then come back to follow the workflow below.
What you need
What you get
What the Data Editor does
Everything you need to prepare a dataset lives in one place. Here is the full set of capabilities — each is covered in detail on its own page below.
Load
Drag-and-drop or click to upload CSV and Excel files, open several datasets in separate tabs, or start from a built-in sample dataset.
Edit
Edit cells and headers, add or remove rows and columns, build formula-based derived columns, bulk-rename, search, sort, and filter per column.
Clean
Fill missing values with several methods, remove outliers using IQR or Z-score bounds, and drop duplicate rows.
Transform
Apply numeric transforms, convert scales (Z-score, MinMax, Robust), and encode categorical columns.
Merge
Join two tabs together by choosing a join type and a join key.
Reproduce
A transformation Pipeline records every step and re-applies it; a query-based extract pulls a reproducible subset.
Typical workflow
Most preparation follows the same five steps. You do not have to use every step — skip the ones your data does not need — but this is the order that keeps things predictable.
Load
Upload a CSV or Excel file, or open a sample dataset. Each dataset gets its own tab.
Inspect
Scan column types, search, sort, and filter to understand what you are working with.
Clean
Fill missing values, remove outliers, and drop duplicate rows.
Transform
Scale numeric columns, encode categories, and add derived columns as needed.
Analyze
Send the prepared dataset to a lab — for example, "Send to Statistical Lab".
Tip: turn on the transformation Pipeline before you start cleaning and transforming. It records each step so you can re-apply the exact same preparation to a refreshed file later — or to a query-based extract.
Detailed guides
Each part of the workflow has a dedicated, task-oriented guide. Start with Loading Data, then move through the rest as you need them.
Upload methods, supported formats, sample datasets, and what happens after a file lands.
Edit cells and headers, manage rows and columns, build derived columns, search, sort, and filter.
Fill missing values, remove outliers with IQR or Z-score bounds, and remove duplicate rows.
Numeric transforms, scale conversion, and categorical encoding.
Join two tabs by selecting a join type and join key.
Common questions
데이터 준비가 끝났다면
Data Editor에서 Send to Statistical Lab 버튼을 누르면 현재 탭의 데이터가 바로 분석 화면으로 이동합니다. 또는 Statistical Lab을 직접 열고 파일을 드래그해서 올려도 됩니다.
Statistical Lab 사용 가이드 보기