# yhilpisch/py4fi

Python for Finance (O'Reilly)

Repository: https://github.com/yhilpisch/py4fi
Canonical: https://ross.abutalabs.com/products/py4fi
Language: Jupyter Notebook
License Family: other
Last push: 2023-10-25T10:13:53+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4279, "days_push": 1043, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1917, forks 906 (observed 2026-08-28T04:05:53.997203+00:00)

## What it is
Companion repository of Python code and Jupyter Notebooks for the O'Reilly book 'Python for Finance' by Yves Hilpisch. It provides educational material for analyzing financial data with Python, including NumPy, pandas, and financial analytics examples.

## Use cases
- learn python for finance
- analyze financial data with pandas
- quantitative finance tutorials
- learn algorithmic trading in python
- financial data analysis notebooks
- study numpy and pandas for finance

## When to choose
- you are reading or studying the Python for Finance book
- you want worked Jupyter notebook examples of financial analytics in Python
- you want offline CSV-based financial datasets to avoid unstable third-party APIs

## When to avoid
- you need a maintained production finance library rather than book code
- you rely on live Yahoo/Google Finance data APIs, which the book's code no longer uses
- you need Python 2.7 legacy code, which is unmaintained

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, analytics, trading
- domain: fintech, data-science, education
- platform: python, cross-platform
- tags: finance, jupyter-notebooks, quantitative-finance, book-companion-code, algorithmic-trading

## Member repositories
- yhilpisch/py4fi (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:53.997203+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:10:09.654817+00:00, confidence not recorded.
  - readme: https://github.com/yhilpisch/py4fi (fetched 2026-08-28T04:05:53.997203+00:00, sha 4edddad6dc80)
- Data as of 2026-08-30T08:39:29.467469+00:00.