{"categories":[{"label":"Visualization","url":"https://skillfed.io/packages/category/scientific-engineering-visualization"},{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"}],"enrichment":{"capability":"Scanpy is a toolkit for preprocessing, visualizing, clustering, and analyzing single-cell gene expression data, handling datasets from hundreds to millions of cells efficiently.","skillfed_tags":["single-cell-genomics","bioinformatics","data-analysis"],"use_cases":["Preprocessing and quality control of raw single-cell RNA-seq count matrices before downstream analysis.","Unsupervised clustering and visualization of cell populations to identify cell types or states.","Differential expression testing between cell groups to find marker genes or disease-associated changes.","Trajectory inference to reconstruct developmental or differentiation paths from snapshot data.","Integration with external tools via anndata format for multi-tool pipelines in single-cell studies."],"what_it_does":"Scanpy is a Python toolkit for single-cell genomics that handles the full workflow of analyzing gene expression data: reading and preprocessing raw counts, normalizing and scaling, dimensionality reduction, clustering, and statistical testing for differential expression. It is built on top of anndata for efficient data representation and integrates with the broader scverse ecosystem. The package is designed to scale from small pilot studies to datasets with over one million cells, and experimental dask support allows some operations on data larger than available memory.\n\nThe toolkit combines visualization (via matplotlib and seaborn), unsupervised learning (via scikit-learn and custom algorithms), and statistical inference (via statsmodels and scipy) into a unified API. It is actively maintained, production-stable, and widely used in academic and research settings for exploratory analysis, cell type discovery, and trajectory inference.","worth_installing":"Yes. Scanpy is a mature, actively maintained toolkit with no known vulnerabilities, permissive licensing, and low install friction. It is the standard choice for single-cell gene expression analysis in Python. Install it if you work with single-cell RNA-seq or similar high-dimensional genomic data; the large dependency tree is a one-time cost for a comprehensive, well-documented analysis platform."},"id":"scanpy","links":{"html":"https://skillfed.io/packages/scanpy","md":"https://skillfed.io/packages/scanpy.md","pypi":"https://pypi.org/project/scanpy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-24","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"scanpy","python_support":"supports_current","summary":"Single-Cell Analysis in Python."},"popularity":{"monthly_downloads":1038260,"position":4459,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.12.3"}