Jupyter

Overview

Jupyter is an interactive computing platform that combines code execution, rich output (tables, plots, widgets), and narrative text in notebook documents. It supports multiple kernels (Python, R, Julia), integrates with matplotlib, plotly, and ipywidgets for visualization, and enables reproducible research through nbconvert for report generation and papermill for parameterized batch execution.

Instructions

• When building notebooks, organize cells with a clear flow: imports, data loading, exploration, analysis, and conclusions, using Markdown cells for narrative context between code cells.
• When sharing notebooks, restart the kernel and "Run All" to ensure cells execute in order, then use nbconvert to generate HTML, PDF, or slides with --no-input for non-technical audiences.
• When managing environments, install kernels from virtual environments with python -m ipykernel install --user --name=myenv and pin dependencies with %pip install package==1.2.3 in the first cell.
• When developing iteratively, use %autoreload 2 to auto-reload imported modules on change, and extract proven code into .py modules for reuse.
• When version controlling, use jupytext to pair .ipynb with .py files that diff cleanly, or use nbstripout to strip output before Git commits.
• When running in production, use papermill to parameterize and execute notebooks programmatically for batch report generation.

Examples

Example 1: Build an exploratory data analysis notebook