Always implement proper validation and type checking to prevent cryptic error messages. When errors do occur, provide clear, actionable guidance for resolution.

Key practices:

  1. Use exact instance checking rather than isinstance() when needed to avoid attribute errors:
    # Prefer this
    if isinstance(path, io.BytesIO):
     target = path
    # Over potentially error-prone checks that could lead to attribute errors
    
  2. Place input validation at appropriate scope levels to catch errors early:
    # Validate inputs early with helpful error messages
    if len(exprs) == 1 and isinstance(exprs[0], Mapping):
     msg = (
         "Cannot pass a dictionary as a single positional argument.\n"
         "If you merely want the *keys*, use:\n"
         "  • df.method(*your_dict.keys())\n"
         "If you need the key–value pairs, use one of:\n"
         "  • unpack as keywords:    df.method(**your_dict)\n"
         "  • build expressions:     df.method(expr.alias(k) for k, expr in your_dict.items())"
     )
     raise TypeError(msg)
    
  3. Consider returning None instead of raising exceptions for non-critical failures to allow more consistent error handling by callers.

  4. Leverage built-in language error mechanisms when appropriate rather than implementing custom error checking that duplicates existing functionality.