Loading...
Loading...
Found 6 Skills
Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.
Python data analysis with pandas, numpy, and analytics libraries
Use when implementing data analysis pipelines, statistical tests, or bioinformatics workflows in code (Python/R), particularly for genomics, transcriptomics, proteomics, or other -omics data.
Write Milvus application-level Jupyter notebook examples using a Markdown-first workflow with jupyter-switch for format conversion.
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.
Manage Jupyter notebooks — create, execute cells, manage kernels via the container's Jupyter Server REST API.