Scientific Packages

Bioinformatics & Genomics

AnnData - Annotated data matrices for single-cell genomics and h5ad files
Arboreto - Gene regulatory network inference using GRNBoost2 and GENIE3
BioPython - Sequence manipulation, NCBI database access, BLAST searches, alignments, and phylogenetics
BioServices - Programmatic access to 40+ biological web services (KEGG, UniProt, ChEBI, ChEMBL)
Cellxgene Census - Query and analyze large-scale single-cell RNA-seq data
gget - Efficient genomic database queries (Ensembl, UniProt, NCBI, PDB, COSMIC)
pysam - Read, write, and manipulate genomic data files (SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences) with pileup analysis, coverage calculations, and bioinformatics workflows
PyDESeq2 - Differential gene expression analysis for bulk RNA-seq data
Scanpy - Single-cell RNA-seq analysis with clustering, marker genes, and UMAP/t-SNE visualization

Datamol - Molecular manipulation and featurization with enhanced RDKit workflows
DeepChem - Molecular machine learning, graph neural networks, and MoleculeNet benchmarks
DiffDock - Diffusion-based molecular docking for protein-ligand binding prediction
MedChem - Medicinal chemistry analysis, ADMET prediction, and drug-likeness assessment
Molfeat - 100+ molecular featurizers including fingerprints, descriptors, and pretrained models
PyTDC - Therapeutics Data Commons for drug discovery datasets and benchmarks
RDKit - Cheminformatics toolkit for molecular I/O, descriptors, fingerprints, and SMARTS

matchms - Processing and similarity matching of mass spectrometry data with 40+ filters, spectral library matching (Cosine, Modified Cosine, Neutral Losses), metadata harmonization, molecular fingerprint comparison, and support for multiple file formats (MGF, MSP, mzML, JSON)
pyOpenMS - Comprehensive mass spectrometry data analysis for proteomics and metabolomics (LC-MS/MS processing, peptide identification, feature detection, quantification, chemical calculations, and integration with search engines like Comet, Mascot, MSGF+)

PyMC - Bayesian statistical modeling and probabilistic programming
PyMOO - Multi-objective optimization with evolutionary algorithms
PyTorch Lightning - Structured PyTorch training with automatic optimization
scikit-learn - Machine learning algorithms, preprocessing, and model selection
statsmodels - Statistical modeling and econometrics (OLS, GLM, logit/probit, ARIMA, time series forecasting, hypothesis testing, diagnostics)
Torch Geometric - Graph Neural Networks for molecular and geometric data
Transformers - Hugging Face transformers for NLU, image classification, and generation
UMAP-learn - Dimensionality reduction and manifold learning

Astropy - Astronomy and astrophysics (coordinates, cosmology, FITS files)
COBRApy - Constraint-based metabolic modeling and flux balance analysis
Pymatgen - Materials structure analysis, phase diagrams, and electronic structure

Dask - Parallel computing for larger-than-memory datasets with distributed DataFrames, Arrays, Bags, and Futures
Matplotlib - Publication-quality plotting and visualization
Polars - High-performance DataFrame operations with lazy evaluation
Seaborn - Statistical data visualization with dataset-oriented interface, automatic confidence intervals, publication-quality themes, colorblind-safe palettes, and comprehensive support for exploratory analysis, distribution comparisons, correlation matrices, regression plots, and multi-panel figures
ReportLab - Programmatic PDF generation for reports and documents

deepTools - NGS data analysis (ChIP-seq, RNA-seq, ATAC-seq) with BAM/bigWig files
FlowIO - Flow Cytometry Standard (FCS) file reading and manipulation
scikit-bio - Bioinformatics sequence analysis and diversity metrics
Zarr - Chunked, compressed N-dimensional array storage