Subcategories
Packages
sentinelhub provides a Python interface to Sentinel Hub's satellite data APIs and geospatial utilities, enabling access to Sentinel, Landsat, MODIS, and other satellite imagery collections through Process, Catalog, Batch, and Statistical APIs.
Install it if you need programmatic access to Sentinel Hub's imagery APIs; skip it if you don't use Sentinel Hub or prefer direct REST calls.
Embeds the Monaco Editor (VS Code's editor) into Qt applications with syntax highlighting, code completion, and Language Server Protocol support.
Bluesky is an experiment control and scientific data collection library that orchestrates experimental procedures, captures rich metadata, and streams live data for inline processing and visualization.
PVAnalytics provides quality control, filtering, feature labeling, and analysis functions for photovoltaic system-level data, organized into modules for data quality checks, feature identification, system characterization, and metrics computation.
Install it if you work with photovoltaic system data and need quality control, feature labeling, or system characterization functions; the modular design lets you use…
PaDELPy wraps the PaDEL-Descriptor Java engine to compute molecular descriptors and fingerprints from chemical structures in SMILES, MDL MolFile, and SDF formats.
Install it if you're already committed to PaDEL's descriptor types; if you're exploring alternatives, consider RDKit or Mordred first, as they have different…
grizz provides composable ingestors and transformers to load and reshape data in polars DataFrames using an object-oriented building-block approach.
However, be aware that the API is unstable in beta—pin your version and review release notes before upgrading, as breaking changes are expected.
SDK for controlling Quantum Machine's Octave hardware using QUA, communicating via gRPC with protobuf serialization.
Sinter performs fast Monte Carlo sampling of quantum error correction circuits, using stim to generate samples and decoders like pymatching to predict logical errors from detector data.
Geode-Explicit is an OpenGeode module for constructing and working with explicit geometric models, built on top of the Geode geometry framework.
However, verify the Proprietary license terms with Geode-solutions first, and confirm that the module's API and capabilities match your use case—public documentation…
AccelForge models, designs, and explores tensor algebra accelerators by integrating hardware component cost modeling with workload mapping and optimization.
However, verify the license terms first, and be prepared for a substantial dependency footprint.
Jina is a framework for building and deploying AI services that communicate via gRPC, HTTP, and WebSockets, with built-in support for scaling, containerization, and cloud deployment.
Not recommended if you prefer minimal dependencies or need cutting-edge feature velocity.
OmniMalloc solves static memory allocation for neural networks by assigning buffer offsets to minimize peak memory usage, with an efficient C++ backend and multiple allocation algorithms including SuperMalloc.
TorchIO reads, preprocesses, augments, and samples 3D medical images for deep learning with PyTorch, offering both standard computer vision transforms and domain-specific medical imaging operations like MRI artifact simulation.
Install it if you're building medical imaging models with PyTorch.
Performs Multiple Imputation by Chained Equations (MICE) using LightGBM to fill missing values in pandas DataFrames and numpy arrays with statistical rigor and speed.
Reads and writes audio files across formats supported by ffmpeg, sox, and mediainfo, with optimized reading speed and metadata extraction for channels, duration, samples, and sampling rate.
Install it if you work with audio files in multiple formats and want to avoid format-specific code.
A collection of machine learning and visualization utilities including API development tools, data simulation, expression building, model fitting helpers, parallelization via joblib, and a lightweight MVC framework for rendering visualizations in multiple formats.
PyGAD is a Python library for building and running genetic algorithms to optimize single-objective and multi-objective problems, with built-in support for training Keras and PyTorch models.
Install it if you need genetic algorithm optimization; skip it if you only do gradient-based machine learning.
swiglpk provides direct Python bindings to the GNU Linear Programming Kit (GLPK) C library, enabling you to formulate and solve linear and mixed-integer optimization problems from Python code.
However, if you want a Pythonic API for formulating optimization problems, use optlang or another high-level wrapper instead; swiglpk is intentionally low-level and…
ggplot is a Python implementation of the grammar of graphics that lets you build layered statistical graphics by combining data, aesthetic mappings, and geometric layers.
Defines and validates the formal data model for bluesky's streaming scientific data ecosystem, providing schemas and Python tools for composing, validating, and transforming experiment documents.
Pyorbital computes orbital parameters from TLE (Two-Line Element) files and performs astronomical calculations for satellite scheduling and pass prediction.
Install it if you need TLE parsing, satellite pass prediction, or orbital position calculations; skip it if your work does not involve satellite scheduling or tracking.
Provides the Python reference implementation of the SOMA specification—a set of base interfaces, types, and utilities for working with standardized single-cell data storage and retrieval across different backend implementations.
TensorFlow Data Validation (TFDV) computes summary statistics, detects anomalies, and generates data schemas for machine learning datasets at scale using Apache Beam and TensorFlow.
audmath provides mathematical functions implemented in pure Python with numpy as its only external dependency.
Compress and decompress VTK datasets using Zstandard compression with multi-threaded read and write operations, integrated with PyVista.
Enables PyTorch to use DirectML for GPU acceleration on DirectX 12-compatible hardware from AMD, Intel, NVIDIA, and Qualcomm.
However, operator coverage is still developing—verify that your specific PyTorch operations are supported via the operator roadmap before committing to production use.
PyRanges provides efficient querying and manipulation of genomic intervals (genes, features, reads) using a pandas-backed interface optimized for genomics workflows.
Install it if you work with genomic intervals or annotations in Python.
Builds minimal perfect hash functions for 64-bit hashes using BBHash, with optional value-table storage for hash-to-value lookups.
Provides feedback latency model parameters for Zurich Instruments quantum control systems (PQSC, SHF, HDAWG), enabling calculation of timing delays in quantum feedback loops.
Provides auto-generated gRPC Python interface files for communicating with Ansys Fluent, enabling programmatic access to Fluent's computational fluid dynamics capabilities.
Automated segmentation of anatomical structures in CT and MR medical images, identifying and labeling 117 classes in CT or 50 classes in MR scans using deep learning.
Provides Pydantic models for reading, validating, and working with Zarr array groups and their metadata in a type-safe way.
Install it if you work with Zarr arrays and want type-safe, validated access to their metadata and structure; skip it if you do not use Zarr or do not need schema…
Encodes molecular structures as MinHash fingerprints (MHFP6) for fast similarity search and nearest-neighbor queries using locality-sensitive hashing.
Not recommended for new projects requiring ongoing support or frequent updates.
PyCUDA provides Pythonic bindings to Nvidia's CUDA API, letting you write GPU-accelerated code in Python with automatic memory management, error handling, and access to CUDA's full driver API.
pySplashsurf provides Python bindings for surface reconstruction from particle data in SPH (smoothed particle hydrodynamics) simulations, available as both a library and command-line tool.
FireWorks stores, executes, and manages calculation workflows, providing a system to define, track, and run computational tasks at scale.
Reports hyperparameter tuning metrics to Google CloudML Engine's hyperparameter tuning service, writing metric data to a local file for the service to consume.
Wraps scikit-learn estimators to return pandas DataFrames instead of numpy arrays, preserving feature names through transformations and enabling feature traceability across pipelines.
Py2neo is a Python client library for Neo4j that provides Bolt and HTTP connectivity, a high-level API, an OGM, and Cypher tooling—though the project is now end-of-life with no further updates planned.
No—not for new projects.
Computes the shortest sea route between two geographic points, returning a GeoJSON LineString with distance and optional port information for maritime visualization.