Python Version Recommendations for Machine Learning Projects (December 2025)

=== Python 3.12 (RECOMMENDED) ===
Release Date: October 2023
Status: BEST CHOICE for ML projects
Stability: Production-ready, very stable
Performance: 60% faster than Python 3.11

ML Library Compatibility:
- TensorFlow 2.16: FULLY SUPPORTED
- PyTorch 2.3: FULLY SUPPORTED
- scikit-learn 1.5: FULLY SUPPORTED
- Pandas 2.2: FULLY SUPPORTED
- NumPy 2.0: FULLY SUPPORTED

Recommendation: Use Python 3.12 for production ML projects. It provides the best balance of performance, stability, and universal ML library compatibility.

=== Python 3.13 ===
Release Date: October 2024
Status: Suitable for research projects
Stability: Stable but limited ML library support
Performance: 5-10% faster than Python 3.12

ML Library Compatibility:
- TensorFlow 2.16: NOT SUPPORTED
- PyTorch 2.3: EXPERIMENTAL SUPPORT
- scikit-learn 1.5: FULLY SUPPORTED
- Pandas 2.2: SUPPORTED (version 2.2.2+)
- NumPy 2.0: FULLY SUPPORTED

Recommendation: Use Python 3.13 only if you need cutting-edge features and can accept limited TensorFlow support.

=== Python 3.14 (LATEST) ===
Release Date: October 2025
Status: NOT RECOMMENDED for ML projects
Stability: Early adoption phase
Performance: Highest (15-25% faster than Python 3.13)

ML Library Compatibility:
- TensorFlow 2.16: NOT SUPPORTED
- PyTorch 2.3: NOT SUPPORTED
- scikit-learn 1.5: NOT SUPPORTED (expected in version 1.6)
- Pandas 2.2: NOT SUPPORTED
- NumPy 2.0: NOT SUPPORTED (expected in version 2.1)

Recommendation: Avoid Python 3.14 for ML projects until library support improves.

=== Python 3.11 ===
Release Date: October 2022
Status: Mature fallback option
Stability: Extremely stable
Performance: Baseline (slower than Python 3.12)

ML Library Compatibility:
- TensorFlow 2.16: FULLY SUPPORTED
- PyTorch 2.3: FULLY SUPPORTED
- scikit-learn 1.5: FULLY SUPPORTED
- Pandas 2.2: FULLY SUPPORTED
- NumPy 1.26+: FULLY SUPPORTED

Recommendation: Use Python 3.11 only if you have compatibility concerns with Python 3.12.