dgreenwald-py-tools 0.2.0

Numerical economics and research utilities.

PyTools

py_tools is a research-oriented Python toolkit for numerical economics, time series, estimation, and dataset loading utilities.

Installation

Install from PyPI:

pip install dgreenwald-py-tools

Or clone the repository and install in editable mode:

pip install -e .

Optional extras:

pip install "dgreenwald-py-tools[ml]"        # scikit-learn, patsy
pip install "dgreenwald-py-tools[datasets]"  # pandas-datareader, python-dotenv
pip install "dgreenwald-py-tools[scraping]"  # requests, beautifulsoup4, lxml
pip install "dgreenwald-py-tools[nlp]"       # nltk
pip install "dgreenwald-py-tools[mpi]"       # mpi4py
pip install "dgreenwald-py-tools[dev]"       # pytest, ruff

If you use dataset loaders, set:

export PY_TOOLS_DATA_DIR=/path/to/data

Or create a .env file in the repository root (or a parent directory):

PY_TOOLS_DATA_DIR=/path/to/data

py_tools.datasets will load .env automatically when python-dotenv is installed (included in the datasets extra).

Quick Start

from py_tools import data, time_series, state_space

# Example: call a utility function from a core module
# (see module docstrings/source for full APIs)

You can also import dataset loaders through:

from py_tools import datasets

available = datasets.list_datasets()
df = datasets.load_dataset("fred", codes=["UNRATE"])

Module Overview

Import path	Contents
py_tools.time_series	Kalman filter, state-space models, VAR/BVAR, HMMs
py_tools.econometrics	NLS/GMM, bootstrap, local projections, high-dimensional FE
py_tools.bayesian	MCMC sampling, prior distributions
py_tools.numerical	Root-finding, Chebyshev approximation
py_tools.datasets	Loaders for ~38 economic data sources
py_tools.data	Data manipulation, aggregation, matching
py_tools.econ	Discrete choice, yield curves, AIM solver
py_tools.plot	Plotting utilities
py_tools.config	Named model specification registry
py_tools.scraping	HTML scraping and text utilities
py_tools.compute	MPI array distribution

Development

pip install -e ".[dev]"
python -m pytest          # run tests
ruff check .              # lint

Contributing

• Keep changes focused and commit one logical change at a time.
• Follow repository coding and workflow guidelines in AGENTS.md.
• Prefer adding deterministic tests in tests/ for new behavior.

License

MIT — see LICENSE.