Data

Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.

Event Store Design provides patterns and guidance for designing and implementing event stores in event-sourced systems. Use it when choosing event store technologies, implementing persistence, or optimizing event storage and retrieval.

Projection Patterns builds read models and projections from event streams. Use it for implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.

Ad Campaign Analyzer turns raw ad campaign performance data into clear decisions on what to cut, scale, and test. Use it to diagnose wasted ad spend, identify top performers, check statistical significance, and reallocate budget across channels.

Arize Dataset creates, manages, and queries Arize datasets and examples, covering CRUD, appending examples, exporting data, and file-based dataset creation with the ax CLI. Use when the user needs evaluation data or mentions create, list, export, append, or dataset version.

Dask scales pandas and NumPy workflows to larger-than-memory datasets and across clusters. Use it when you need parallel file processing, distributed ML, or to scale existing pandas/NumPy code beyond memory.

Database Lookup queries documented public database APIs to retrieve reproducible facts from named sources. Use it when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly rather than inferred.

Datamol provides a Pythonic wrapper around RDKit for molecular cheminformatics, including SMILES parsing, standardization, descriptors, fingerprints, clustering, and 3D conformers. Use it for drug discovery and molecular data workflows.

DeepChem enables molecular property prediction, drug discovery, and materials design through molecular featurization, MoleculeNet benchmarks, and training of classical ML and graph neural network models. Use it for ADMET/toxicity prediction, transfer learning with pretrained chemistry models, or benchmarking on datasets like Tox21 and BBBP.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use it when working with temporal data or time-indexed observations requiring specialized algorithms beyond standard ML approaches.

AnnData handles annotated data matrices for single-cell analysis, storing measurements, metadata, and multi-dimensional annotations. Use it when working with .h5ad files or integrating with the scverse ecosystem.

Arboreto infers gene regulatory networks from gene expression data using scalable ensemble regression algorithms (GRNBoost2, GENIE3). Use when analyzing bulk or single-cell RNA-seq data to identify transcription factor-target relationships and regulatory interactions.