Packages
Provides market and exchange trading calendars for pandas, including holiday, early close, and break schedules for over 50 global equity and futures markets.
Install it if you need to work with market hours, holidays, or trading schedules in pandas.
This package is deprecated; it redirects users to ydata-profiling for exploratory data analysis of pandas DataFrames with HTML and JSON export.
Converts XML files into pandas DataFrames, with tools to flatten nested structures and separate relational data from hierarchical XML sources.
Validates pandas DataFrames against user-defined schemas, checking column types, value ranges, patterns, and whitespace to catch data quality issues in tabular data.
Provides type stubs for pandas, enabling static type checkers like mypy and pyright to catch type errors in pandas code before runtime.
Extends pandas' describe() function to provide richer statistical summaries and data profiling for exploratory data analysis on tabular datasets.
However, verify compatibility with your pandas version first—the 2021 release date means it may lag behind current pandas APIs.
Provides over 150 technical analysis indicators and 60 candlestick patterns for financial data, optimized with numba and numpy, and integrated as a pandas DataFrame extension.
However, the Beta status, 334-day maintenance gap, and unclear license terms warrant caution—verify the license for your use case and be prepared for potential…
Pandas TA Classic provides 193 technical indicators and 62 native candlestick patterns as a Pandas DataFrame extension, enabling technical analysis workflows without requiring TA-Lib.
Install it if you need technical analysis indicators in pandas workflows and want to avoid TA-Lib compilation or licensing complexity.
Pandas-TD connects pandas DataFrames to Treasure Data's cloud analytics platform, enabling you to query and load data from Treasure Data into pandas for analysis and write results back to tables.
PandasAI lets you ask questions about your data in natural language and get answers without writing SQL or Python code, using an LLM backend to translate questions into data operations.
However, maintenance is aging (311 days since last release), so verify that the library's feature set and bug-fix cadence meet your stability requirements before…
Integrates LiteLLM with PandasAI to enable natural-language queries against dataframes using any LLM provider that LiteLLM supports.
However, the 0.0.1 version and absence of updates since release suggest early-stage maturity; verify compatibility with your target Python version and litellm setup…
pandasql lets you query pandas DataFrames using SQL syntax instead of pandas methods, bridging SQL familiarity with DataFrame manipulation.
Reads and writes Apache Avro files to and from pandas DataFrames, handling schema inference and type conversion between Avro and numpy/pandas types.
Pandera provides a flexible API for validating dataframe-like objects using declarative schemas with type checking and custom validation rules.
Pandoc Python Library wraps the Pandoc document model to analyze, create, and transform documents programmatically in Python.
Install only if your Pandoc version is stable and you can tolerate no upstream support.
Provides Python utilities for writing pandoc filters that transform document ASTs by reading JSON from stdin, modifying the structure, and writing JSON to stdout.
Panel is a Python framework for building interactive data applications, dashboards, and web apps with widgets, plots, and tables that can be deployed as web services, notebooks, or static exports.
Install it if you need to turn Python data work into interactive web apps or dashboards without learning JavaScript or web frameworks.
Panel Material UI brings Material Design components to Panel dashboards, offering styled buttons, sliders, cards, dialogs, and other widgets with built-in theming and dark mode support.
Install it if you want Material Design styling in Panel without custom CSS work.
Panflute provides a Pythonic interface for writing Pandoc filters, allowing you to programmatically transform documents between markup formats by manipulating their abstract syntax trees.
Upserts pandas DataFrames into PostgreSQL, MySQL, and SQLite databases using primary or unique keys, with optional automatic table and schema creation.
Provides pretrained neural network models for audio tagging and sound event detection on audio files using PANNs (Pretrained Audio Neural Networks).
However, do not expect bug fixes or updates—verify that the pretrained models and PyTorch compatibility meet your production requirements before committing to it for…
PanPhon maps International Phonetic Alphabet (IPA) segments to articulatory phonological feature vectors, enabling programmatic analysis of phonetic and phonological properties of speech sounds.
Pansi provides a clean interface for rendering text and graphics in the terminal using ANSI escape sequences, with modules for text styling, image rendering, and full-screen layout.
However, note that the project is dormant (no releases in 649 days), so do not expect active maintenance, bug fixes, or updates—suitable for stable use cases but…
Converts DataFrames to and from Tableau Hyper Extract format, enabling direct data exchange between Python workflows and Tableau.
Install it if you regularly move data between Python and Tableau; skip it if you don't work with Hyper files.
Evaluates JsonLogic and CertLogic rules—JSON-serializable logic formats designed to share conditional rules between front-end and back-end code—against data objects and returns the computed result.
However, do not use it if you require active maintenance, security updates, or compatibility assurance with future Python versions.
Papermill parameterizes, executes, and analyzes Jupyter Notebooks programmatically, allowing you to inject parameters into notebooks and run them with different inputs via Python API or CLI.
Install it if you need to automate notebook execution with varying inputs or integrate notebooks into larger workflows.
Distributes CPU-intensive processing of collections across multiple cores using Python's multiprocessing module, with a primary map() function for parallel execution.
Paracelsus generates Entity Relationship Diagrams from SQLAlchemy models, outputting them as Mermaid or Dot format for visualization and documentation.
Install it if you want automated, version-controlled schema diagrams; skip it if your schema is trivial or you prefer manual diagram tools.
Paradict serializes and deserializes Python dictionaries to textual or binary formats, with built-in schema validation and support for rich datatypes including dates, times, complex numbers, and nested structures.
However, maintenance is dormant—the last commit was 2024-12-10 and activity is minimal—so consider this for stable, self-contained use cases rather than projects…
Python SDK for interacting with the Paradime data platform API, supporting both bearer token and legacy API key authentication with CLI and programmatic interfaces.
However, the unclear license status is a blocker for commercial or compliance-sensitive use—verify the actual license before committing.
Provides a single function that joins multi-line strings into clean, single-paragraph text by removing indentation and normalizing whitespace, making it easier to write readable long strings in Python code.
However, the dormant maintenance status means you should not expect active support; use it only for straightforward string formatting where you do not need ongoing…
Asynchronous SSH client for running commands on hundreds or thousands of remote servers in parallel with minimal client-side overhead, using native C libraries for performance.
Provides a Python client library for the Parallel REST API with synchronous and asynchronous interfaces, full type hints, and automatic error handling and retries.
Parallelbar wraps Python's multiprocessing Pool methods (map, starmap, imap, imap_unordered) to display live progress bars and handle exceptions and timeouts across worker processes.
Param provides declarative class attributes with runtime validation and a reactive programming API for automatic updates when attribute values change.
Install it if you need runtime parameter validation with class-level configuration, or if you're building interactive systems where automatic synchronization matters.
Provides decorators that automatically convert function parameters to specified types—for example, converting string arguments to Path objects without manual casting in the function body.
Expands POSIX and Bash shell parameter syntax in Python strings without spawning a shell, supporting variable substitution, default values, substring operations, and string replacement.
However, the 1563-day maintenance gap and lack of updates for modern Python versions beyond 3.10 mean you should verify it works with your target environment and have…
Parameterized testing decorator that works with nose, pytest, unittest, and other Python test frameworks to run the same test function across multiple input sets.
However, maintenance is dormant with the last release on 2023-03-27, so compatibility with future Python versions is uncertain.
Provides a `@parametrize` decorator that works with `unittest.TestCase` to run parameterized tests, mimicking pytest's `@pytest.mark.parametrize` syntax without requiring a migration away from unittest.
However, the dormant maintenance status and strict decorator-ordering rules mean it's best suited for teams committed to pytest as their long-term test runner.
Paramiko is a pure-Python SSH protocol library providing both client and server functionality for low-level SSH operations and advanced use cases beyond what higher-level tools offer.
However, if your goal is simply running remote commands or transferring files, use Fabric instead—the maintainers explicitly recommend it for those common cases.