pola-rs/polars · error · TypeError

cannot set Series of dtype

Error message

cannot set Series of dtype: {self.dtype!r} with list/tuple as value; use a scalar value

What it means

Series.__setitem__ raises this TypeError when the value being assigned is a list or tuple but the Series dtype is not one that supports sequence assignment. Polars only accepts scalar values (or matching structures like lists for list-dtype columns) when setting via a key; for this dtype you must use a scalar.

Solutions

1. Assign a scalar: `series[mask] = 0` instead of a list
2. Wrap the list in the appropriate structure only for List-dtype series (e.g. `series[i] = [1,2,3]` on List(Int64))
3. Use `series.scatter(indices, value_series)` to set multiple positions from another Series

Example fix

// before
s[s > 10] = [0, 0, 0]  # TypeError
// after
s = pl.when(s > 10).then(0).otherwise(s)

if isinstance(value, (list, tuple)) and not isinstance(series.dtype, pl.List):
    raise ValueError("assign a scalar or use when/then instead of a list value")
series[mask] = value

def is_scalar(value: object) -> bool:
    return not isinstance(value, (list, tuple, pl.Series))

try:
    series[mask] = value
except TypeError as e:
    if "list/tuple as value" in str(e):
        series = pl.when(pl.Series([True] * len(series)) & mask).then(value[0]).otherwise(series)
    else:
        raise

When it happens

Trigger: `series[mask_or_indices] = [1, 2, 3]` where series.dtype is e.g. Int32, Date, or another non-list dtype, hitting the branch at py-polars/src/polars/series/series.py:1433.

Common situations: Numpy habits (`arr[mask] = [..]`) carried into polars; assigning multiple replacement values at masked positions; pandas-style vectorized assignment of lists.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

• cannot use " " for indexing
• arr.to_struct() got a str instead of a list. hint: pass
• cannot call `map_groups` when filtering groups with `having`
• cannot call `map_groups` when grouping by an expression
• cannot call `map_groups` when grouping by named expressions

AI-assisted analysis of pola-rs/polars@fe841f959e (2026-09-18). Data as JSON: /api/errors/d25b24eece7925ce. Report an issue: GitHub.

Appendix: source

Thrown at py-polars/src/polars/series/series.py:1433

        [
            10
            99
            99
        ]
        """
        # do the single idx as first branch as those are likely in a tight loop
        if isinstance(key, int) and not isinstance(key, bool):
            self.scatter(key, value)
            return None
        elif isinstance(value, Sequence) and not isinstance(value, str):
            if self.dtype.is_numeric() or self.dtype.is_temporal():
                self.scatter(key, value)  # type: ignore[arg-type]
                return None
            msg = (
                f"cannot set Series of dtype: {self.dtype!r} with list/tuple as value;"
                " use a scalar value"
            )
            raise TypeError(msg)
        if isinstance(key, Series):
            if key.dtype == Boolean:
                self._s = self.set(key, value)._s
            elif key.dtype.is_integer():
                self._s = self.scatter(key, value)._s
            else:
                msg = f"cannot use Series of dtype {key.dtype!r} for indexing; expected boolean or integer dtype"
                raise TypeError(msg)

        # TODO: implement for these types without casting to series
        elif _check_for_numpy(key) and isinstance(key, np.ndarray):
            if key.dtype == np.bool_:
                # boolean numpy mask
                self._s = self.scatter(np.argwhere(key)[:, 0], value)._s
            else:
                s = self._from_pyseries(
                    PySeries.new_u32("", np.array(key, np.uint32), _strict=True)
                )

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