PyYAML

PyYAML has been my go-to for YAML parsing in Python for years. The API is incredibly straightforward - `yaml.safe_load()` and `yaml.dump()` cover 90% of use cases. You can parse YAML files in literally two lines of code, and the library handles complex nested structures, lists, and dictionaries without any fuss. The learning curve is nearly flat if you stick to basic usage.

The biggest gotcha that trips up newcomers is the `yaml.load()` vs `yaml.safe_load()` distinction. Using `yaml.load()` without a Loader argument triggers a deprecation warning, and for good reason - it can execute arbitrary Python code. The error messages could be clearer about this security issue. When YAML syntax is malformed, error messages are decent but sometimes point to the wrong line number in complex files.

Documentation is sparse but functional. The official docs cover basic usage adequately, though you'll often need to look at Stack Overflow for advanced scenarios like custom constructors or handling specific data types. Community support is solid - most questions have been asked and answered. Debugging YAML issues usually means adding print statements to see what's being parsed, as there's no built-in verbose mode.

PyYAML has been the de facto standard for YAML parsing in Python for years, and it shows in its stability and API maturity. The basic `yaml.safe_load()` and `yaml.dump()` cover 95% of use cases cleanly. The C-based LibYAML bindings (`yaml.CSafeLoader`) provide significant performance gains when available, though the fallback to pure Python can catch you off guard in container environments where the C extension isn't compiled.

In production, you'll need to be deliberate about loader choice - always use `safe_load()` unless you have a specific reason not to, as the default loaders can execute arbitrary Python code. Memory usage can spike on large YAML files since it loads the entire structure into memory. There's no streaming parser option, which has burned me on multi-GB config files. Error messages are generally helpful with line numbers, though deeply nested structures can be cryptic.

The library doesn't offer configuration for timeouts or resource limits, so you'll need to wrap it yourself if parsing untrusted input. Documentation is adequate but sparse on performance tuning and advanced customization. Version 6.0+ resolved some longstanding deprecation warnings, though the breaking changes required code updates across our services.

PyYAML gets the job done for basic YAML parsing, but the developer experience feels stuck in the past. The API is straightforward—`yaml.safe_load()` and `yaml.dump()` cover 90% of use cases—but lacks modern Python niceties. Type hints are completely absent in the core library, making IDE autocomplete unreliable and requiring you to check docs constantly. You'll need third-party stubs (types-PyYAML) for any meaningful type checking.

The security model is confusing for newcomers. The default `yaml.load()` allows arbitrary Python object execution, which is a major footgun. You must remember to use `safe_load()` everywhere, and the documentation doesn't emphasize this enough upfront. Error messages when parsing fails are cryptic—you get line numbers but unhelpful context about what's actually wrong.

Custom serialization requires diving into Representer/Constructor classes, which feel overly complex for simple tasks. The C extension speedup is nice but adds installation friction on some platforms. For greenfield projects, consider alternatives like ruamel.yaml or strictyaml that offer better type safety and modern APIs.