Packages
Django app that integrates Firebase Cloud Messaging to send push notifications to mobile devices and browsers via the Firebase HTTP v1 API.
Install it if you need to send push notifications from a Django application.
FDB is a Python Database API 2.0-compliant driver for connecting to and querying Firebird 2.5 relational databases, with limited support for Firebird 3.0.
Provides OAuth 2.0 authentication and utility helpers for FactSet API clients in Python, handling token management, proxy configuration, and SSL certificate validation.
Install it if you are building applications that call FactSet APIs and want to avoid reimplementing OAuth 2.0 token management.
Parses, manipulates, and converts between Device Tree binary (.dtb) and source (.dts) formats, with support for reading, writing, merging, and diffing device tree structures.
Fetches CNN's Fear & Greed Index from their website, parses the current value, and returns it as a named tuple with the index value, category description, and last update timestamp.
Feast is an open-source feature store that manages offline and online feature storage, retrieval, and materialization for machine learning training and real-time inference pipelines.
However, it brings substantial dependencies (30 runtime packages) and requires Python 3.10+.
Feather-format provides a Python interface to store and load pandas DataFrames using the Apache Arrow-based Feather file format for efficient disk serialization.
Feature-engine provides transformers for engineering, selecting, and preprocessing features in machine learning pipelines, following scikit-learn's fit/transform interface.
Enables runtime control of application features through feature flags, allowing conditional activation of functionality based on defined rules and conditions.
Install it if you need runtime feature control; the low friction and clean API make it a straightforward addition to any Python application.
Featuretools automates feature engineering for machine learning by synthesizing new features from multi-table datasets using Deep Feature Synthesis (DFS), eliminating manual feature creation.
Install it if you're prototyping ML pipelines or need to scale feature generation; skip it if your data is already a single flat table or if you prefer manual feature…
Provides tools and APIs for publishing and consuming messages on AMQP brokers, with schema declaration and async consumer services for Fedora's messaging infrastructure.
However, be aware of the GPLv2+ copyleft license (verify dual-licensing implications for proprietary code) and the requirement for a running AMQP broker.
Feedfinder2 detects and returns feed URLs (RSS, Atom, etc.) available on a given website by parsing its HTML.
Generates web feeds in ATOM and RSS formats, with support for extensions including podcast feeds.
Parses Atom and RSS feeds (including RSS 0.9x, RSS 1.0, RSS 2.0, CDF, Atom 0.3, and Atom 1.0) into Python data structures.
Install it if you need to consume RSS or Atom feeds.
Provides the sgmllib SGML parser from Python 2.7 for use with feedparser, enabling parsing of SGML-based feed formats in modern Python versions.
A pure Python implementation of the Fernet symmetric encryption specification, providing token-based authenticated encryption without compiled C dependencies.
No, not for new projects.
Routes HTTP requests through Browser-Use's proxy infrastructure with Chrome TLS fingerprinting, session-based IP persistence, and server-side cookie management.
feu checks package availability, resolves compatible versions across Python environments, and manages package installations with automatic version compatibility validation.
fev is a lightweight benchmarking library for time series forecasting models that provides standardized evaluation workflows, reproducible task definitions, and metric computation without heavy dependencies.
Implements NIST FF3 and FF3-1 format-preserving encryption (FPE) algorithms to encrypt data while preserving its format—digits remain digits, custom alphabets remain within their alphabet.
No—not for new production systems.
Wraps FFmpeg and FFprobe command-line tools to programmatically manipulate video and image files from Python, supporting operations like inserting images and GIFs into videos, converting image sequences to video, and adding text watermarks.
ffmpeg-python provides a Python interface to FFmpeg that translates complex filter graphs into command-line arguments, supporting both simple operations and arbitrarily large directed-acyclic signal graphs.
However, dormancy since 2019-07-06 means no active maintenance and potential compatibility issues with recent FFmpeg releases.
ffmpy is a Python wrapper around FFmpeg that lets you build and execute FFmpeg command lines programmatically without writing shell commands directly.
A gymnasium framework for defining custom reinforcement learning environments where language model agents interact via tool calls and messages to solve structured tasks.
Reads configuration files in INI, TOML, or JSON format through a single unified interface, abstracting away format-specific syntax differences.
However, be aware that maintenance is dormant—last release was 933 days ago—so consider whether you need active support or can tolerate a stable, unchanging library.
Provides Pydantic V2-based abstract base classes and primitive datatypes for building FHIR (Fast Healthcare Interoperability Resources) resource models, with support for JSON, XML, and YAML serialization.
Provides Python classes and validation for all FHIR resource types (R5, R4B, STU3), enabling you to construct, validate, and serialize healthcare data structures according to the FHIR specification.
Install it if you're building or integrating with FHIR-based systems.
A Python client library for connecting to FHIR servers and working with FHIR healthcare data, supporting the SMART on FHIR protocol for OAuth-based authentication and resource queries.
Async/sync FHIR client for Python that provides CRUD operations over FHIR resources via HTTP, supporting search, filtering, and resource manipulation.
fhlmi provides a unified async Python interface to multiple large language models, handling authentication, rate limiting, cost tracking, and tool calling across different LLM providers.
However, verify that all runtime dependencies fit your environment before committing; the dependency footprint is substantial.
Fickling is a decompiler, static analyzer, and bytecode rewriter for Python pickle serializations that detects, analyzes, and can reverse-engineer or create malicious pickle and pickle-based files including PyTorch models.
Install it if you load pickle or PyTorch files from any untrusted source.
Fiddle is a Python-first configuration library that lets you define and manage complex program parameters in readable Python code, particularly suited for machine learning applications.
Install it if configuration-as-code in Python appeals to your workflow; skip it if you prefer external config files or simpler parameter passing.
Implements FIDO2 and WebAuthn protocols for communicating with USB authenticators and verifying cryptographic signatures for passwordless authentication.
Generates lightweight container classes with automatic `__repr__`, comparison methods, and field initialization from a fluent attribute-chain syntax, eliminating boilerplate for simple data holders.
No, not for new projects.
Fifolock provides a low-level building block for creating custom synchronization primitives in asyncio Python, where locks are granted strictly in first-in-first-out order and are not reentrant.
FiftyOne is a Python framework for building, visualizing, and evaluating computer vision datasets and models, with integrated labeling, model evaluation, and data quality tools.
FiftyOne Brain provides AI/ML capabilities for analyzing and manipulating datasets and models, including visual similarity search, text-based querying, sample uniqueness detection, and quality/annotation issue identification.
Install it if you work with computer vision datasets and need systematic quality and similarity analysis.
Provides the database backend for FiftyOne, a computer vision framework for managing and analyzing image and video datasets.
Converts Figma .fig design files into Sketch .sketch documents that can be opened in Sketch applications, handling frame-to-artboard mapping and style translation.
file-magic provides a Python interface to libmagic, enabling detection of file types, MIME types, and encodings from file paths or content.
The main gotcha is the libmagic system dependency, which is typically already present but must be verified in your deployment environment.