Code Editor Guide

Data analysis,in your browser

Write Python, analyze data with Pandas, create visualizations—all without leaving Helse. This guide covers everything you need to know about the Code Editor.

Python Runtime

Full Python 3.9 environment running directly in your browser. No installation required.

Pandas Built-in

Pandas comes pre-installed. Load, clean, and analyze CSV/JSON data instantly.

Visualizations

Create charts with matplotlib and seaborn. Results display inline.

Version History

Every change is tracked. Restore previous versions at any time.

Auto-Complete

Intelligent code suggestions that understand pandas and data science patterns.

Keyboard Shortcuts

Standard IDE shortcuts work as expected. Ctrl+Enter to run, Ctrl+/ to comment.

Quick Start Example

Your first data analysis

Here's a simple example to get you started with the Code Editor:

analysis.py
import pandas as pd
import matplotlib.pyplot as plt

# Load your data
df = pd.read_csv('data/research_data.csv')

# Quick exploration
print(df.head())
print(df.describe())

# Create a visualization
df.groupby('category')['value'].mean().plot(kind='bar')
plt.title('Average Value by Category')
plt.show()

# Export processed data
df.to_csv('output/processed_data.csv', index=False)

Keyboard Shortcuts

Work faster with these essential shortcuts (use Cmd on Mac):

Run current cell/file
Ctrl+Enter
Save file
Ctrl+S
Toggle comment
Ctrl+/
Duplicate line
Ctrl+D
Delete line
Ctrl+Shift+K
Find in file
Ctrl+F
Find and replace
Ctrl+H
Indent
Tab
Outdent
Shift+Tab
Undo
Ctrl+Z
Redo
Ctrl+Shift+Z
Trigger autocomplete
Ctrl+Space

Current Limitations

The browser-based Code Editor has some constraints compared to a full desktop IDE:

  • Cannot install custom packages (limited to pre-installed libraries)
  • Maximum execution time of 60 seconds per run
  • No network access from Python code (use Helse's API integrations instead)
  • Large dataset processing may be slower than native Python
  • No GPU acceleration available

Frequently Asked Questions

What Python libraries are available?

The Code Editor includes pandas, numpy, matplotlib, seaborn, scipy, and scikit-learn. These cover most data analysis needs. For specialized libraries, export your data and use a local environment.

How do I upload data files?

Drag and drop CSV, JSON, or Excel files into the file explorer panel. Files are stored in your project and can be accessed from any code file using relative paths like 'data/myfile.csv'.

Can I share code with my team?

Yes! All code files in a project are shared with team members. Multiple people can edit the same file with real-time sync and conflict resolution.

Is there a file size limit?

Individual files are limited to 10MB. For larger datasets, we recommend splitting them into smaller chunks or using data sampling for initial exploration.

How do I export my analysis?

Use the File menu to export as .py files. You can also copy generated charts as images or export data frames to CSV directly from the output panel.

Does code run on a server?

Code runs in your browser using WebAssembly (Pyodide). Your data never leaves your device unless you explicitly share it. This ensures privacy and low latency.

Ready to start analyzing?

Open the Code Editor and try analyzing your first dataset.