Code Editor

Data analysis, beautifully simplified

Write Python, analyze with Pandas, visualize results—all in your browser with intelligent tools that understand your research.

Everything you need to analyze data

A complete Python environment optimized for research workflows.

Smart Code Completion

Intelligent autocomplete predicts entire lines and functions. Get the most relevant Pandas operations suggested as you type.

Built-in Pandas

Filter, transform, merge—manipulate complex datasets with zero setup. Just start coding.

Instant Visualizations

Create charts and graphs inline. Explore patterns and communicate insights visually.

Version History

Experiment freely. Every change is tracked so you can always return to what worked.

Code Explanations

Don't understand a code block? Get clear explanations of what each analysis step does in plain language.

Live Collaboration

Work on the same analysis with teammates in real-time. Share insights as you discover them.

Live Preview

See it in action

Python 3.9
main_analysis.py
1import pandas as pd
2
3# Load the dataset
4df = pd.read_csv('data/research_data.csv')
5
6# Display basic info
7print("Dataset Info:")
8print(df.info())
9
10# Display first 5 rows
11print("\nFirst 5 rows:")
12print(df.head())
13
14# Perform a simple analysis (e.g., count by category)
15# print("\nValue Counts for 'category':")
16# print(df['category'].value_counts())
$
python main_analysis.py
Dataset Info:
<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5 entries, 0 to 4\nData columns (total 3 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 id 5 non-null int64 \n 1 category 5 non-null object\n 2 value 5 non-null int64 \ndtypes: int64(2), object(1)\nmemory usage: 248.0+ bytes\nNone
\nFirst 5 rows:
id category value\n0 1 A 100\n1 2 B 150\n2 3 A 120\n3 4 C 200\n4 5 B 110
Execution finished in 0.23s.

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FAQ: Working with Your Data in Helse

Q1: What kind of documents can I create and manage in Helse?

Helse is perfect for all your writing needs! You can create, edit, and collaborate on research papers, articles, class notes, project reports, and pretty much any text-based document. We support common formats like TXT, Markdown, and HTML, and you can easily export your work to PDF, DOCX, and LaTeX.

Q2: Can Helse help me understand and analyze the text from my research articles or other documents?

Absolutely! Helse has smart features that can help you make sense of imported documents. It can assist with things like summarizing key findings, identifying main themes, and generally getting a quicker understanding of textual information to support your research.

Q3: I have data in spreadsheets, like CSV files from experiments or surveys. Can I work with those in Helse?

Yes, you can! Helse includes a handy web-based Python IDE (think of it as a code editor in your browser) that comes with the popular pandas library. This is great for working with data from CSV or JSON files. You can use it to:

  • Clean up your data.
  • Do some basic analysis or calculations.
  • Prepare tables or summaries to include in your written reports.
It's best for datasets that are manageable in size – perfect for a lot of common research tasks!

Q4: What about other types of files, like images, videos, or very large scientific datasets?

You can certainly store various files related to your projects in Helse's central library to keep things organized. However, Helse's direct processing tools (like the Python IDE and analysis features) are primarily designed for text-based information and smaller, structured data files (like CSVs).

For very large or specialized files (e.g., raw genomic sequences, high-resolution microscopy images, large video files), you'd typically use dedicated software designed for those types of data first. Then, you can bring your findings, analyses, summaries, and smaller data tables into Helse to write about them and collaborate with your team.

Q5: Can I run any Python script I want in Helse's built-in code editor?

Helse's Python editor is super useful for scripts that work with data like text, CSVs, and JSONs, especially using libraries like pandas. It's designed to help you with data manipulation and analysis tasks that directly support your research writing and project work within Helse.

However, it's a browser-based environment, so it's not set up for Python scripts that require massive computing power, extremely long run times, or specialized external software and libraries that can't run in a web browser.

Q6: So, what's the "sweet spot" for data that Helse processes best?

Helse really shines when you're working with all the data and information that goes into your research writing, project management, and collaboration. This includes:

  • Your written documents, notes, and drafts.
  • Textual information you want to analyze or summarize.
  • Manageable datasets (like CSVs or JSONs) that you want to explore, clean, or perform light analysis on using Python.
  • Your bibliographic data and references.