A junior data analyst needs to share a step-by-step walkthrough that shows Python data-cleaning code, the resulting charts, and explanatory text in the same file. The sales team should be able to open the file in a web browser and re-run all steps on another laptop without installing a full IDE. Which type of coding environment would BEST meet these requirements?
A stand-alone text editor that saves .py scripts (for example, Notepad++ or an unextended VS Code)
A browser-based notebook environment (for example, Jupyter Notebook or Google Colab)
A visual business-intelligence desktop application that imports CSV files (for example, Tableau)
A command-line Python REPL started from the operating-system shell
Notebook environments such as Jupyter Notebook or Google Colab embed executable code cells, their output (including visualizations), and Markdown narrative in a single .ipynb document. Because notebooks run in the browser and can be re-executed end-to-end, they are easy to share and reproduce without heavy local setup. A plain text editor only stores script files and cannot display live charts; BI desktop tools focus on drag-and-drop visualizations rather than exposing the transformation code; a command-line Python REPL is interactive but does not combine code, output, and formatted text in one shareable document.
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How do Jupyter Notebooks and Google Colab work, and why are they suitable for sharing Python workflows?
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Can Jupyter Notebooks or Google Colab handle different types of data visualizations directly?
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CompTIA Data+ DA0-002 (V2)
Data Concepts and Environments
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