Data Preprocessor — IntelliJ Plugin

An in-IDE data-cleaning and code-generation toolkit for data scientists — no Python environment needed.

🔗 Install from JetBrains Marketplace →

---

---

What It Does

Data Preprocessor brings your data-cleaning workflow directly into IntelliJ IDEA. Load CSV, Excel, or JSON data, profile every column, apply transformations through a point-and-click UI, and get ready-to-run pandas Python code generated for you — all without leaving the IDE.

Tab	What you get
Preview	Configurable table preview of your loaded dataset
Profile	Per-column stats: type, null count, unique values, mean, median, std, min, max, mode
Clean	Point-and-click operations that build a reproducible, editable pipeline
Code	Auto-generated pandas script — save anywhere as .py or copy to clipboard

---

Installation

From the IDE (recommended)

1. Open IntelliJ IDEA (any edition, 2023.3+)
2. Go to Settings → Plugins → Marketplace
3. Search for "Data Preprocessor"
4. Click Install and restart the IDE

From JetBrains Marketplace website

Visit plugins.jetbrains.com/plugin/31226-data-preprocessor and click Get.

---

Features

• Load CSV, Excel, and JSON files — browse and open data without leaving the IDE; also available via right-click on supported files in the Project view
• Column Profiler — type, null count, unique count, mean, median, std deviation, min, max, mode
• Missing value handling — drop rows, fill with mean / median / mode, or supply a custom value
• Remove duplicates — deduplicate rows in one click
• Outlier removal — IQR fence method (1.5 × IQR) to drop statistical outliers
• Normalization — Min-Max scaling [0, 1], Z-Score standardisation (mean=0, std=1), or Robust Scaler using median/IQR
• Type casting — cast any column to int, float, boolean, or string
• Pipeline editing — reorder, remove, clear, undo, and redo cleaning steps before applying them
• Python code generation — one click produces a complete, ready-to-run pandas script that mirrors every cleaning step you applied
• Save as .py — choose where to save the generated script; it opens directly in the IntelliJ editor
• Export cleaned CSV — choose where to save the cleaned dataset
• Copy cleaned data as TSV — copy the applied result for direct paste into Excel or Google Sheets
• Settings page — configure preview row limit, default normalization operation, and default train/test ratio under Settings → Tools → Data Preprocessor

---

Screenshots

CSV Preview tab	Column Profiler tab
Clean operations panel	Generated pandas code

---

Usage

Loading a file

• Open the Data Preprocessor tool window from the right-side panel, or
• Right-click any .csv file in the Project view → Open in Data Preprocessor

Building a cleaning pipeline

1. Switch to the Clean tab
2. Select a column and operation from the dropdowns, then click Add step
3. Repeat for as many steps as needed
4. Reorder steps with ↑ Up / ↓ Down, or use Undo / Redo while editing
5. Click ▶ Apply steps to preview the cleaned result
6. Click 🐍 Generate Python code to create the pandas script

The Code tab populates with a complete pandas script. Use Save as .py to choose a destination and open it in the editor, or copy and paste it into your notebook.

Exporting results

• 📤 Export cleaned CSV — opens a save dialog and writes the applied result as CSV
• Copy cleaned data as TSV — copies headers and cleaned rows for spreadsheet paste
• Save as .py — opens a save dialog for preprocess_<filename>.py and opens it in the editor automatically

Settings

Open Settings → Tools → Data Preprocessor to configure:

• Preview row limit — limits how many rows the Preview tab renders
• Default normalization — selects the default normalization operation in the Clean tab
• Default train/test ratio — pre-fills the Train / Test split ratio field

---

Project Structure

src/main/java/com/datapreprocessor/
├── model/
│   ├── DataSet.java                             # In-memory tabular data model
│   └── ColumnProfile.java                       # Per-column statistics
├── engine/
│   ├── DataLoader.java                          # CSV → DataSet (Apache Commons CSV)
│   ├── DataCleaner.java                         # All cleaning & transformation logic
│   ├── CodeGenerator.java                       # Generates pandas Python code
│   └── DataExporter.java                        # CSV and .py file export
├── settings/
│   ├── DataPreprocessorSettings.java            # Persistent plugin settings
│   └── DataPreprocessorConfigurable.java        # Settings UI under Tools
├── actions/
│   ├── OpenDataFileAction.java                  # Right-click "Open in Data Preprocessor"
│   └── GeneratePreprocessingCodeAction.java     # Insert code at editor caret
└── toolwindow/
    ├── DataPreprocessorToolWindowFactory.java
    └── DataPreprocessorToolWindow.java          # Main Swing UI (4 tabs)

---

Building from Source

Prerequisites

• JDK 17+
• Use the included Gradle wrapper — do not use a system-installed Gradle

Run in a sandboxed IDE

./gradlew runIde

Build distributable ZIP

./gradlew buildPlugin
# Output: build/distributions/data-preprocessor-plugin-*.zip

Publish to JetBrains Marketplace

export PUBLISH_TOKEN=<your-marketplace-token>
./gradlew publishPlugin

Note: Always run ./gradlew (the wrapper), not gradle. The build requires Gradle 8.6 — the wrapper pins this automatically. Using a system Gradle 9.x will fail.

---

Extending the Plugin

To add a new cleaning operation:

1. Add a variant to CodeGenerator.Operation
2. Implement the logic in DataCleaner
3. Add the pandas translation in CodeGenerator.toCode()
4. Add a label in the opSelector combo box in CleanPanel
5. Handle the new index in CleanPanel.addStep() and CleanPanel.applySteps()

---

Privacy Policy

Data Preprocessor does not collect, store, or transmit any user data. All data processing is performed entirely locally within your IDE. No CSV content, file paths, column names, or usage metrics are ever sent anywhere.

Full privacy policy: https://plugins.jetbrains.com/plugin/31226-data-preprocessor

---

License

MIT

---