data-science-types
Type stubs for Python machine learning libraries
Decision gist · record as of 2026-08-14
No. The package is abandoned and has not been maintained since 2021-02-16. While it has no runtime dependencies and installs easily, its incomplete stub coverage combined with lack of maintenance means it will produce false negatives and false positives. Modern versions of the supported libraries have either shipped their own stubs or improved type support, making this package obsolete for current projects.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.6 or later.
- Type coverage is incomplete; many functions lack stubs, so type checkers may report false 'function does not exist' errors.
- Installation is frictionless with no runtime dependencies.
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive), so you can use it freely in commercial and open-source projects without restriction.
last release 2021-02-16 (2005 days) · last repo commit 2021-02-16 · 206 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 180,978 downloads/mo, #10,135 on PyPI
Alternatives
Verify before relying
pip install data-science-types
# Then type checkers will recognize types in the supported libraries
# Example from the package documentation:
arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3]) # OK
arr2: np.ndarray[np.int32] = np.array([3, 7, 39, -3]) # Type error- Current compatibility with recent versions of NumPy, pandas, and Matplotlib given the last release was 2021-02-16
- Whether stub coverage has improved or regressed relative to upstream library API changes since abandonment
- How much of NumPy and pandas functionality is actually covered by the stubs
What it is and what it does
This is a stub-only package that teaches mypy, pytype, and PyCharm how to type-check code using NumPy, pandas, and Matplotlib. It does not add runtime behavior—it only provides type information for static analysis. The stubs are PEP-561 compliant, meaning type checkers will automatically recognize them when the package is installed.
The package is explicitly a work in progress and incomplete. Many functions lack type annotations in the stubs. As a result, you may encounter type checker errors claiming functions do not exist when they actually do. The project was archived and has not been maintained since 2021-02-16, so new library features and API changes are not reflected in the stubs.
Use it for
- Catch type errors in array operations before runtime, such as incorrect dtype assignments or incompatible arithmetic.
- Enable IDE autocompletion and type hints for data structures in editors like PyCharm.
- Validate plotting code with type checking to catch incorrect argument types to functions.
- Add static type safety to data science scripts without waiting for upstream libraries to ship their own stubs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
The package is abandoned and has not been maintained since 2021-02-16. While it has no runtime dependencies and installs easily, its incomplete stub coverage combined with lack of maintenance means it will produce false negatives and false positives. Modern versions of the supported libraries have either shipped their own stubs or improved type support, making this package obsolete for current projects.
Install
data-science-types on PyPI
Before you install
Installation is frictionless with no runtime dependencies. However, the package is abandoned and has not been maintained since 2021-02-16, which means type coverage gaps will persist and newer library versions may lack stub support.
Requires Python 3.6 or later. Type coverage is incomplete; many functions lack stubs, so type checkers may report false 'function does not exist' errors.
License in practice
Licensed under Apache License 2.0 (permissive), so you can use it freely in commercial and open-source projects without restriction.
Quickstart
pip install data-science-types
# Then type checkers will recognize types in the supported libraries
# Example from the package documentation:
arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3]) # OK
arr2: np.ndarray[np.int32] = np.array([3, 7, 39, -3]) # Type errorVerify before relying
- Current compatibility with recent versions of NumPy, pandas, and Matplotlib given the last release was 2021-02-16
- Whether stub coverage has improved or regressed relative to upstream library API changes since abandonment
- How much of NumPy and pandas functionality is actually covered by the stubs
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,005 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 180,978 / month, #10,135 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Typing :: Typed |
Evidence: data_science_types-0.2.23-py3-none-any.whl
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