pandas-dev/pandas · error · TypeError

{func_name} requires a Series, Index, ExtensionArray, np.nda

Error message

{func_name} requires a Series, Index, ExtensionArray, np.ndarray or NumpyExtensionArray got {type(values).__name__}.

What it means

Raised by _ensure_arraylike in pandas.core.algorithms. Many internal algorithms (factorize, unique, value_counts, etc.) coerce inputs to array-like before working; if the value is not a Series, Index, ExtensionArray, np.ndarray or NumpyExtensionArray, this TypeError is raised (with a special carve-out for the isin targets path). It guards the algorithm layer against scalars and arbitrary objects.

Source

Thrown at pandas/core/algorithms.py:239

    # error: Incompatible return value type
    # (got "ndarray[tuple[Any, ...], dtype[Any]]",
    # expected "ExtensionArray")
    return values.astype(dtype, copy=False)  # type: ignore[return-value]


def _ensure_arraylike(values, func_name: str) -> ArrayLike:
    """
    ensure that we are arraylike if not already
    """
    if not isinstance(
        values,
        (ABCIndex, ABCSeries, ABCExtensionArray, np.ndarray, ABCNumpyExtensionArray),
    ):
        # GH#52986
        if func_name != "isin-targets":
            # Make an exception for the comps argument in isin.
            raise TypeError(
                f"{func_name} requires a Series, Index, "
                f"ExtensionArray, np.ndarray or NumpyExtensionArray "
                f"got {type(values).__name__}."
            )

        inferred = lib.infer_dtype(values, skipna=False)
        if inferred in ["mixed", "string", "mixed-integer"]:
            # "mixed-integer" to ensure we do not cast ["ss", 42] to str GH#22160
            if isinstance(values, tuple):
                values = list(values)
            values = construct_1d_object_array_from_listlike(values)
        else:
            values = np.asarray(values)
    return values


_hashtables = {
    "complex128": htable.Complex128HashTable,

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Wrap the value in a list/Series/np.ndarray before calling the algorithm.
2. Validate the input is array-like (is_list_like) and raise a clearer error at your boundary.
3. Convert dicts to a Series when a mapping of values is the intent.

Example fix

# before
pd.factorize(5)
# after
pd.factorize([5])

from pandas.api.types import is_list_like
import numpy as np

def to_arraylike(x):
    if not is_list_like(x):
        raise TypeError(f'expected array-like, got {type(x).__name__}')
    return np.asarray(x)

from pandas.api.types import is_list_like
import numpy as np, pandas as pd

def is_arraylike(x) -> bool:
    return isinstance(x, (pd.Series, pd.Index, np.ndarray)) or is_list_like(x)

When it happens

Trigger: Passing a Python scalar or dict directly to an algorithm such as pd.factorize(5), pd.unique({1:2}), or pd.core.algorithms functions with a non-arraylike; calling a public API whose implementation routes the first argument through _ensure_arraylike.

Common situations: Constructing pipelines that forward user input straight into factorize/unique without coercion; data that arrives as a single scalar where a 1-D sequence was expected; dicts being passed where an array-like was intended.

Related errors

• only list-like objects are allowed to be passed to isin(), y
• only list-like objects are allowed to be passed to isin(), y
• pd.api.extensions.take requires a numpy.ndarray, ExtensionAr
• Only np.ndarray, ExtensionArray, and Index objects are allow
• Only list-like objects or None are allowed to be passed to s

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/076ffe84b2c84aea. Report an issue: GitHub.