Reproducible data analysis and clearer charts.
What it does
Guides the creation of reproducible data analyses, visualizations, and well-organized Python notebooks.
When to use it
Use for exploratory data analysis, data pipelines, statistical visualization, notebook development, dataset cleaning, or analytics dashboards.
How to use it
It changes how the agent writes data analysis code, visualizations, and notebooks in the project.
What you provide
- An existing project
Latest review
What you need · 5
Lists pandas as a core dependency for data manipulation and analysis.
Lists numpy as a core dependency for numerical computing.
Lists matplotlib as a core dependency for plotting.
Lists seaborn as a core dependency for statistical data visualization.
Access · 0
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