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Found 39 Skills
Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Write Milvus application-level Jupyter notebook examples using a Markdown-first workflow with jupyter-switch for format conversion.
Convert a Jupyter notebook (.ipynb) to a marimo notebook (.py).
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
Read, modify, execute, and convert Jupyter notebooks programmatically. Use when working with .ipynb files for data science workflows, including editing cells, clearing outputs, or converting to other formats.
Best practices for creating comprehensive Jupyter notebook data analyses with statistical rigor, outlier handling, and publication-quality visualizations
Iterative Python via live Jupyter kernel (hamelnb).
Manage Jupyter notebooks — create, execute cells, manage kernels via the container's Jupyter Server REST API.
Deploys Jupyter notebooks on TrueFoundry infrastructure with optional GPU support. Use when launching JupyterLab environments, setting up ML development workspaces, or running cloud-hosted notebooks for data exploration.
Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.
Deploy the Cortex CLASSIFY_TEXT tutorial notebook to the user's Snowflake account and provide a link to open it in Snowsight. Use when user wants to learn text classification through a Jupyter notebook experience.