Getting started with interactive computing on Panthaion

Updated April 10, 2026

Scientists, researchers, and data enthusiasts use Panthaion to publish, share, and discover verified climate and environmental datasets. In under 30 minutes - no coding experience required - you'll go from a blank workspace to a working analysis with charts you can share with colleagues. This guide walks you through every step.

Scientists, researchers, and data enthusiasts use Panthaion to publish, share, and discover verified climate and environmental datasets. This guide walks you through everything you need to feel confident from your very first session.

Step 1: Open a Python workspace

From the Panthaion dashboard, click Analyze with Python. A blank workspace will open with a single empty cell waiting for input. Think of it as a live document that mixes written text, code, and results all in one place.

Tip

Give your workspace a descriptive name straight away — click the title at the top and type something meaningful so it's easy to find later.

Step 2: Understand cells

Everything lives inside a cell. There are two kinds: code cells run your code and display output directly below, and markdown cells render formatted text and headings for notes and explanations. Switch a cell's type with the toolbar dropdown, or press M for markdown and Y for code.

Step 3: Run your first cell

Click inside any code cell and type a simple expression. Then run it:

print("Hello, world!")

Use Shift + Enter to run and move to the next cell, Ctrl + Enter to run and stay, or Alt + Enter to run and insert a new cell below. The result appears immediately. A [*] means the cell is still running.

Step 4: Load and inspect a dataset

Most analyses start by loading a dataset. Use the file browser to upload a CSV or Parquet file, or connect directly to any dataset from the Panthaion Ecosystem.

import pandas as pd df = pd.read_csv("my_dataset.csv") df.head() # displays the first five rows

From there you can check column names, data types, and spot missing values before any analysis begins.

Step 5: Visualize your data

Charts render inline, directly beneath the cell that produces them.

import matplotlib.pyplot as plt df["temperature"].plot(title="Temperature over time") plt.show()

For interactive charts with hover, zoom, and filter, use plotly.express. Right-click any chart to save it as an image for reports or publications.

Step 6: Keep the kernel in mind

The kernel is the engine running your code. Variables and imports persist across cells as long as it stays active. If things behave unexpectedly, go to Kernel > Restart kernel and re-run cells from the top.

Good habit

Before sharing, restart the kernel and run all cells. If it runs cleanly top to bottom, it will work for anyone who opens it.

Step 7: Save and share

Panthaion saves automatically. To share, click Share in the top-right and choose view or edit access. To export, go to File > Export and choose HTML for non-technical colleagues, PDF, or the raw file for collaborators who want to run the code themselves.