MLA 007 Jupyter Notebooks

Jupyter Notebooks, originally conceived as IPython Notebooks, enable data scientists to combine code, documentation, and visual outputs in an interactive, browser-based environment supporting multiple languages like Python, Julia, and R. This episode details how Jupyter Notebooks structure workflows into executable cells - mixing markdown explanations and inline charts - which is essential for documenting, demonstrating, and sharing data analysis and machine learning pipelines step by step. Links • Notes and resources at ocdevel.com/mlg/mla-7 Try a walking desk • stay healthy & sharp while you learn & code Overview of Jupyter Notebooks •

Historical Context and Scope •

• Jupyter Notebooks began as IPython Notebooks • focused solely on Python. • The project was renamed Jupyter • to support additional languages - namely Julia ("JU"), Python ("PY"), and R ("R") - broadening its applicability for data science and machine learning across multiple languages. • •

Interactive, Narrative-Driven Coding •

• Jupyter Notebooks allow for the mixing of executable code, markdown documentation, and rich media outputs within a browser-based interface. • The coding environment is structured as a sequence of cells • where each cell can independently run code and display its output directly underneath. • Unlike traditional Python scripts, which output results linearly and impermanently, Jupyter Notebooks preserve the stepwise development process and its outputs for later review or publication. • Typical Workflow Example Stepwise Data Science Pipeline Construction • • Import necessary libraries: Each new notebook usually starts with a cell for imports (e.g., matplotlib • , scikit-learn • , keras • , pandas • ). • Data ingestion phase: Read data into a pandas DataFrame via read_csv • for CSVs or read_sql • for databases. • Exploratory analysis steps: Use DataFrame methods like .info() • and .describe() • to inspect the dataset; results are rendered below the respective cell. • Model development: Train a machine learning model - for example using Keras - and output performance metrics such as loss, mean squared error, or classification accuracy directly beneath the executed cell. • Data visualization: Leverage charting libraries like matplotlib • to produce inline plots (e.g., histograms, correlation matrices), which remain visible as part of the notebook for later reference. • Publishing and Documentation Features •

Markdown Support and Storytelling •

• Markdown cells enable the inclusion of formatted explanations, section headings, bullet points, and even inline images and videos, allowing for clear documentation and instructional content interleaved with code. • This format makes it simple to delineate different phases of a pipeline (e.g., "Data Ingestion", "Data Cleaning", "Model Evaluation") with descriptive context. • •

Inline Visual Outputs •

• Outputs from code cells, such as tables, charts, and model training logs, are preserved within the notebook interface, making it easy to communicate findings and reasoning steps alongside the code. • Visualization libraries (like matplotlib • ) can render charts directly in the notebook without the need to generate separate files. • •

Reproducibility and Sharing •

• Notebooks can be published to platforms like GitHub, where the full code, markdown, and most recent cell outputs are viewable in-browser. • This enables transparent workflow documentation and facilitates tutorials, blog posts, and collaborative analysis. • Practical Considerations and Limitations •

Cell-based Execution Flexibility •

• Each cell can be run independently, so developers can repeatedly rerun specific steps (e.g., re-trying a modeling cell after code fixes) without needing to rerun the entire notebook. • This is especially useful for iterative experimentation with large or slow-to-load datasets. • •

Primary Use Cases •

• Jupyter Notebooks excel at "storytelling" - presenting an analytical or modeling process along with its rationale and findings, primarily for publication or demonstration. • For regular development, many practitioners prefer traditional editors or IDEs (like PyCharm or Vim) due to advanced features such as debugging, code navigation, and project organization. • Summary Jupyter Notebooks serve as a central tool for documenting, presenting, and sharing the entirety of a machine learning or data analysis pipeline - combining code, output, narrative, and visualizations into a single, comprehensible document ideally suited for tutorials, reports, and reproducible workflows.