pandas-dev/pandas · error · TypeError

You cannot access the property {name}

Error message

You cannot access the property {name}

What it means

Raised by the base PandasDelegate._delegate_property_get fallback. Accessor classes built with @delegate_names declare properties that the host object should delegate; when a concrete accessor has not actually implemented _delegate_property_get for a given name, the base implementation rejects the read with this TypeError. In practice it signals that a delegated property is not supported on the object/dtype in question.

Source

Thrown at pandas/core/accessor.py:67

        """
        Provide method name lookup and completion.

        Notes
        -----
        Only provide 'public' methods.
        """
        rv = set(super().__dir__())
        rv = (rv - self._dir_deletions()) | self._dir_additions()
        return sorted(rv)


class PandasDelegate:
    """
    Abstract base class for delegating methods/properties.
    """

    def _delegate_property_get(self, name: str, *args, **kwargs):
        raise TypeError(f"You cannot access the property {name}")

    def _delegate_property_set(self, name: str, value, *args, **kwargs) -> None:
        raise TypeError(f"The property {name} cannot be set")

    def _delegate_method(self, name: str, *args, **kwargs):
        raise TypeError(f"You cannot call method {name}")

    @classmethod
    def _add_delegate_accessors(
        cls,
        delegate,
        accessors: list[str],
        typ: str,
        overwrite: bool = False,
        accessor_mapping: Callable[[str], str] = lambda x: x,
        raise_on_missing: bool = True,
    ) -> None:
        """

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Confirm the Series/Index dtype actually supports the accessor (e.g. .dt needs datetime-like, .str needs string/object).
2. If you authored the accessor, override _delegate_property_get to implement the property.
3. Switch to the correct accessor or convert the data to a supported dtype before accessing the property.

Example fix

# before
s = pd.Series(['a', 'b'])
s.dt.year  # .dt delegated property not valid for strings
# after
s = pd.to_datetime(s)
s.dt.year

def safe_accessor_attr(s, accessor, attr):
    import pandas.api.types as pt
    ok = (accessor == 'dt' and pt.is_datetime64_any_dtype(s)) or \
         (accessor == 'str' and s.dtype == object) or \
         (accessor == 'cat' and isinstance(s.dtype, pd.CategoricalDtype))
    if not ok:
        raise AttributeError(f'{accessor} not valid for dtype {s.dtype}')
    return getattr(getattr(s, accessor), attr)

def supports_dt(s) -> bool:
    import pandas.api.types as pt
    return pt.is_datetime64_any_dtype(s) or pt.is_timedelta64_dtype(s)

try:
    val = s.dt.year
except TypeError:
    val = pd.to_datetime(s).dt.year

When it happens

Trigger: Accessing a delegated accessor property (e.g. a property wired through delegate_names on a categorical/datetime/string accessor) on an object whose accessor subclass left _delegate_property_get un-overridden for that name; accessing an accessor-only attribute on an incompatible dtype via the delegate machinery.

Common situations: Subclassing a pandas accessor and forgetting to override the delegate get/set hooks; calling an accessor property that is only meaningful for a specific dtype on data of another dtype; version changes that rename or relocate delegated properties.

Related errors

• The property {name} cannot be set
• You cannot call method {name}
• Can only use .cat accessor with a 'category' dtype
• The numba engine only supports using string or numeric colum
• bins argument only works with numeric data.

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/be5cfbc386e6b775. Report an issue: GitHub.