Packages
SoundCard plays and records audio across Linux, macOS, and Windows using native OS audio libraries (PulseAudio, CoreAudio, WASAPI) without C extensions, exposing a unified Python interface for speaker and microphone access.
Install it if you need speaker/microphone access without C extension overhead.
A Python wrapper for SoundCloud's internal v2 API that enables read-only access to SoundCloud data without requiring an official API key.
However, be aware that it relies on SoundCloud's undocumented internal API, which could change or be blocked at any time without notice—use it for non-critical…
Implements the Soundex phonetic algorithm for indexing names by sound in English, plus a modified version for Indian languages.
Reads and writes audio files in formats like WAV, FLAC, OGG, and MAT through libsndfile, exposing audio data as NumPy arrays.
Converts BeautifulSoup4 parsed HTML or XML objects into Python dictionaries or JSON, preserving element hierarchy, attributes, and text content.
Soupsieve is a CSS selector library designed to work with Beautiful Soup 4 to select, match, and filter HTML and XML elements using modern CSS selectors from CSS level 1 through CSS level 4 specifications.
Install it if you use Beautiful Soup for HTML or XML parsing and want modern CSS selector support.
Distributes Python source code as a package; the fact sheet provides no detail on what transformations or utilities it applies to that source.
No—install only if you have already identified a specific need for this package.
Python wrapper that exposes SoX audio processing capabilities—trimming, effects, format conversion, and concatenation—via a Transformer and Combiner API.
Performs high-quality sample-rate conversion (resampling) for audio signals, supporting both one-shot and streaming modes via a Python wrapper around libsoxr.
Unsupervised Korean natural language processing toolkit that extracts words and nouns from text, tokenizes sentences, and performs part-of-speech analysis without requiring training data.
However, GPLv3 licensing restricts proprietary use, and the package requires a reasonably large, homogeneous corpus to work well—single documents or mixed-domain text…
Spaceforge is a Python framework for building Spacelift plugins using a declarative, hook-based approach that automatically generates plugin manifests.
However, the Alpha status and limited real-world adoption mean you should verify that the framework's hook types and capabilities match your plugin requirements…
Implements CCSDS and ECSS packet standards for space communications, including Space Packets, PUS Telecommand/Telemetry, CFDP, and USLP frame formats with helper utilities for verification and sequencing.
Install it if you are working with CCSDS or ECSS packet standards in any capacity—ground stations, mission control, spacecraft software, or testing.
Provides utilities for working with Hugging Face Spaces, including downloading files, uploading content, and managing repositories on the Hugging Face Hub platform.
Install it if you need to programmatically interact with the Hub.
spaCy is an industrial-strength NLP library providing tokenization, part-of-speech tagging, named entity recognition, dependency parsing, text classification, and neural network-based pipeline components for processing text in 70+ languages.
Aligns tokenizations between spaCy and transformer models by mapping token boundaries between different tokenization schemes, enabling seamless integration of spaCy NLP pipelines with transformer-based models.
Integrates curated transformer models (ALBERT, BERT, CamemBERT, RoBERTa, XLM-RoBERTa) into spaCy pipelines via the curated-transformers library.
Adds language detection to spaCy pipelines at document and sentence level, using langdetect by default but accepting custom detection functions.
Provides legacy registered functions and model architectures for backwards compatibility with older configuration files.
However, its abandoned status since 2023-01-23 means no bug fixes or security patches will be provided.
Provides logging integrations for spaCy model training, supporting Weights & Biases, MLflow, ClearML, PyTorch, and CuPy as alternate loggers decoupled from the core library.
Chinese word segmentation for spaCy with domain-specific models (news, web, medicine, tourism) and optional part-of-speech tagging.
Integrates pretrained transformer models (BERT, RoBERTa, XLNet, GPT-2) into spaCy pipelines via Hugging Face's transformers library, enabling state-of-the-art neural NLP components with automatic tokenization alignment.
Spaghetti analyzes network-based spatial data by building graph-theoretic networks from geographic features and computing network events, topology, and spatial inference on them.
Implements SPAKE2, a password-authenticated key exchange (PAKE) algorithm that allows two parties sharing a weak password to derive a strong shared secret for encrypted communication.
Spandrel loads and runs pre-trained PyTorch models by automatically detecting their architecture from model files and providing a unified interface for inference.
Registers additional PyTorch model architectures with spandrel's model loader, enabling support for architectures that carry restrictive (non-commercial or copyleft) licenses.
Conjugates Spanish verbs by tense, mood, and pronoun through a simple class-based API.
However, verify that the tenses and moods you need are implemented—the package does not support all Spanish moods (e.g., no conditional perfect listed in the docs),…
Spark is an async-first actor framework for Python that lets you build concurrent and distributed systems by creating actors, sending messages with `tell()` (fire-and-forget) or `ask()` (request-reply), and managing them through a `Syndicate` instance.
Spark Expectations is a data quality framework that validates PySpark DataFrames against row-level, aggregate, and query-based rules, quarantining failed records to an error table while passing clean data downstream.
However, verify the license terms first—the metadata does not declare a license identifier—and confirm that PySpark is already in your environment, as it is not…
Spark NLP provides distributed natural language processing on Apache Spark, offering pretrained pipelines and models for tokenization, named entity recognition, sentiment analysis, machine translation, and embeddings across multiple languages.
Install only if you already have Apache Spark 3.0+ and Java 8 or 11 in your environment; it is not suitable for lightweight single-machine NLP work.
Implements an Earley algorithm parser for context-free grammars with Abstract Syntax Tree support and a regex-based scanner.
Not recommended if you need active maintenance or support.
Distributes scikit-learn model training and hyperparameter search across Spark clusters, converting Spark DataFrames to numpy arrays or sparse matrices for parallel processing.
Provides utilities for working with Spark DataFrames that have complex nested schemas, including flattening StructType columns, renaming nested fields, and converting JSON schemas to Spark schemas.
Install only if you are locked into an older Spark version and cannot upgrade.
Converts Pydantic models to PySpark schemas (StructType or JSON format), allowing you to define data structures once and generate compatible Spark schemas automatically.
Sparklines renders compact Unicode bar charts (▃▁▄▁▅█▂▅) in your terminal or Python code to visualize sequences of numbers, supporting mixed positive/negative data and missing values.
Install it if you need quick visual feedback on time-series data in shell scripts, monitoring tools, or Python notebooks without the overhead of a graphing library.
SparkMeasure provides a Python API to collect and analyze Apache Spark performance metrics, enabling interactive troubleshooting and batch job analysis of Spark workloads.
Official Python client for the SparkPost email transmission API, enabling programmatic sending of emails through SparkPost's infrastructure.
Translates rdflib Graph.triples() calls into SPARQL queries and resolves them against a remote SPARQL endpoint, acting as a caching layer for RDF data.
SPARQLWrapper provides a Python interface to query remote SPARQL endpoints, handling query construction and result conversion into manageable formats like JSON, XML, and RDF graphs.
Install it if you need to query remote RDF data sources; the only caveat is to review the W3C license terms for your use case.
Provides multi-dimensional sparse array data structures compatible with NumPy and Numba, enabling efficient storage and computation on arrays with mostly zero or missing values.
Performs fast sparse matrix multiplication with integrated top-n result selection, reducing memory footprint and improving performance over separate multiplication and filtering steps.