Computational notebook

As explained in Effective Data Analysis

A computational document or environment that combines executable code, explanatory text, and dynamically generated interactive output.

A computational notebook combines executable code, explanatory text, and interactive output in a document for exploring analytics problems and communicating results. section 12.2.1

Notebook workflows support iterative analysis, from querying and combining data through manipulation, visualization, and report preparation. Completed work can be rendered with only the results intended for stakeholders. section 12.2.1

Interactive notebook platforms can support:

• Collaborating and pairing with coworkers on the same project. section 12.2.1
• Querying a data warehouse and combining its data with information retrieved from an API. section 12.2.1
• Creating reports with interactive charts and preserving records of published deliverables. section 12.2.1
• Enabling stakeholders to perform simple exploratory analysis through native visualization features. section 12.2.1

Common notebook environments

Jupyter Notebook, Google Colab, and RStudio provide code and markdown cells for exploring and iterating on analytics problems. section 12.2.1

Figure 12.3 Notebooks are nearly ubiquitous in the data analytics space.

Use the figure to orient the notebook as a common environment in modern data analytics. section 12.2.1

Notebook tools complement dashboards when analysis is exploratory, collaborative, or too complex for a fixed published view; tool choice should reflect stakeholder needs and workflow pain points. section 12.0 section 12.2.1

How Effective Data Analysis covers it

How deep it goes

Explains notebook capabilities and workflow benefits without descending into implementation details or platform-specific configuration.

Examples

Uses Jupyter Notebook, Google Colab, RStudio, code cells, markdown cells, interactive charts, and rendered stakeholder reports.

What you can do after

Apply Jupyter Notebook, Google Colab, or RStudio to explore data, create visualizations, collaborate, and render reports.

How it connects

Connects notebooks with exploratory analysis, dashboards, data visualization, APIs, data warehouses, and self-service analytics.

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Also explained in

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  Investing for ProgrammersA data science notebook is an interactive document that combines executable code, outputs, visualizations, and explanatory text for data analysis.
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  The Quick Python Book, Fourth EditionJupyter Notebook is an interactive computational document format and web application that combines executable code, results, and explanatory text.
• 

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  Architecting an Apache Iceberg LakehouseJupyter is an open-source interactive computing environment for combining executable code, narrative text, data, and visualizations.

Compare how 4 books explain computational notebook →

Related concepts

• Pythonuses · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
• cellhas part · A notebook cell is a discrete unit containing executable code, formatted text, or other supported content that can be edited and executed independently.
• IDEkind of · An integrated development environment (IDE) is a software application that combines source-code editing with tools for developing, building, running, or debugging programs.
• spaCyuses · spaCy is an open-source Python library for natural language processing, providing tools and pretrained pipelines for tasks such as tokenization and linguistic analysis.
• SQLiteuses · SQLite is a self-contained, serverless relational database engine implemented as a software library that stores and queries databases in files or memory.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.