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Jupyter Notebooks

Interactive computing environment combining code, visualizations, and narrative text

Developer Tools & Best Practices Essential Core Tool
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Quick Info
  • Category: Developer Tools & Best Practices
  • Level: Essential
  • Type: Core Tool

Why We Recommend Jupyter Notebooks

Jupyter notebooks allow you to combine code, results, and explanations in a single document. This makes them perfect for exploratory data analysis, teaching, and creating reproducible research workflows. You can iterate quickly, visualize results inline, and document your thought process as you work.

Common Use Cases

  • Exploratory data analysis and prototyping
  • Document analysis workflows with code and explanations
  • Share reproducible analyses with collaborators
  • Create tutorial materials and teaching content

Getting Started

Jupyter (formerly IPython Notebook) is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It’s become the standard tool for interactive data science and research computing.

Why Jupyter?

  • Interactive: Write code, run it, see results immediately
  • Literate Programming: Mix code with markdown explanations
  • Visualization: Display plots and figures inline
  • Reproducible: Share notebooks with all code and outputs
  • Multi-Language: Supports Python, R, Julia, and more

Key Features

Code Cells

Execute Python code interactively:

import numpy as np
import matplotlib.pyplot as plt

# Generate data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Plot inline
plt.plot(x, y)
plt.show()

Markdown Cells

Document your analysis with rich text:

  • Headers, lists, bold, italic
  • LaTeX equations: $E = mc^2$
  • Links and images
  • Code blocks with syntax highlighting

Rich Output

Display diverse content types:

  • Plots and figures
  • DataFrames (pretty HTML tables)
  • Images and videos
  • Interactive widgets
  • HTML and JavaScript

Installation & Usage

Install Jupyter:

conda install jupyter
# or
pip install jupyter

Launch notebook server:

jupyter notebook
# or for JupyterLab (modern interface)
jupyter lab

Jupyter Lab vs Classic Notebooks

JupyterLab (recommended):

  • Modern interface with multiple panels
  • Integrated file browser and terminal
  • Extensions and themes
  • Better for complex projects

Classic Notebook:

  • Simpler, single-document interface
  • Lighter weight
  • Good for quick analyses

Common Workflows in Research

Exploratory Analysis

# Load data
import pandas as pd
data = pd.read_csv('experiment.csv')

# Quick exploration
data.head()
data.describe()

# Visualize
data.groupby('condition')['reaction_time'].hist(alpha=0.5)

Parameterized Notebooks

Use tools like Papermill to run notebooks with different parameters:

papermill analysis.ipynb output.ipynb -p subject_id S01

Publishing Results

Convert notebooks to:

  • HTML reports
  • PDF documents
  • Slideshows
  • Blog posts (with Hugo, Jekyll)

Best Practices

  • Restart & Run All: Ensure notebooks execute in order
  • Clear Outputs: Before committing to version control
  • Short Cells: Keep individual cells focused
  • Document: Explain what and why, not just how
  • Version Control: Commit .ipynb files to Git
  • Virtual Environments: Use separate environments per project

Tips

  • Use keyboard shortcuts (Esc for command mode, Enter for edit mode)
  • Learn magic commands: %matplotlib inline, %timeit, %%time
  • Use tab completion and ? for documentation
  • Split complex analyses across multiple notebooks
  • Use %load_ext autoreload for developing packages
  • Consider nbconvert for generating reports

Resources

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