PhySO (Physical Symbolic Optimization) is a Python library for symbolic regression using deep reinforcement learning, with a strong emphasis on physics-informed constraints such as dimensional analysis. It discovers closed-form analytical expressions from data, supporting both single-dataset symbolic regression (SR) and multi-dataset class symbolic regression (Class SR) where the same functional form is shared across realizations with per-dataset free constants. The core architecture consists of a token-based program representation (reverse Polish notation), a custom LSTM-based policy network, a modular prior system (hard/soft length, physical units, nesting limits), and an efficient batch execution engine with optional parallel free-constant optimization. The library provides high-level entry points (`physo.SR()` and `physo.ClassSR()`), configuration presets, and a toolkit for expression manipulation and random generation. It includes extensive benchmarking infrastructure (Feynman and Class benchmarks), demos for real-world problems (e.g., Milky Way streams, US tariffs), and unit tests. External dependencies include PyTorch, NumPy, SymPy, pandas, scikit-learn, and matplotlib. While primarily a software library for symbolic regression, it is deeply research-oriented with benchmark result CSVs and analysis scripts, giving it a hybrid application/research character.