pola-rs/polars · error · TypeError

passing Expr objects to the DataFrame constructor is not…

Error message

passing Expr objects to the DataFrame constructor is not supported

Hint: Try evaluating the expression first using `select`, or if you meant to create an Object column containing expressions, pass a list of Expr objects instead.

What it means

Raised by _expand_dict_values when a dict passed to the DataFrame constructor contains pl.Expr values. Expressions are lazy and unevaluated, so they cannot become column data; Polars raises TypeError with a hint to evaluate the expression first or to explicitly wrap it in a list for an Object column.

Solutions

1. Evaluate the expression first, e.g. df.select(expr) and then build the DataFrame from the result
2. Convert the underlying data to a Series/list before constructing
3. If an Object column of expressions is truly intended, pass a list: {'a': [pl.col('x')]}

Example fix

// before
df = pl.DataFrame({'double_x': pl.col('x') * 2})
// after
df = df.select((pl.col('x') * 2).alias('double_x'))

if isinstance(data, dict):
    exprs = [v for v in data.values() if isinstance(v, pl.Expr)]
    assert not exprs, f'Expr values cannot be column data: {exprs}'

def is_expr_dict(data: dict) -> bool:
    return any(isinstance(v, pl.Expr) for v in data.values())

try:
    df = pl.DataFrame(data)
except TypeError as e:
    if 'passing Expr objects' in str(e):
        raise TypeError('evaluate expressions with select/with_columns first') from e
    raise

When it happens

Trigger: pl.DataFrame({'a': pl.col('x') * 2}) — passing an Expr directly as a dict value to pl.DataFrame.__init__ or dict_to_pydf.

Common situations: Confusing the expression context (df.select / with_columns) with the constructor, translating pandas code where such a value would have been a Series, building column definitions dynamically and accidentally using expressions.

Related errors

• accessing ` ` from the top-level `polars` module was…
• altair>=5.4.0 is required for `.plot`
• `arctan2` expected a `str` or `Expr` got a
• `arctan2` expected a `str` or `Expr` got a
• arr.dot query vector must be one-dimensional

AI-assisted analysis of pola-rs/polars@fe841f959e (2026-09-18). Data as JSON: /api/errors/ffc75539ce5dd2ae. Report an issue: GitHub.

Appendix: source

Thrown at py-polars/src/polars/_utils/construction/dataframe.py:355

def _expand_dict_values(
    data: Mapping[str, ArrayLike | NonNestedLiteral | None],
    *,
    schema_overrides: SchemaDict | None = None,
    strict: bool = True,
    order: Sequence[str] | None = None,
    nan_to_null: bool = False,
) -> dict[str, Series]:
    """Expand any scalar values in dict data (propagate literal as array)."""
    updated_data = {}
    if data:
        if any(isinstance(val, pl.Expr) for val in data.values()):
            msg = (
                "passing Expr objects to the DataFrame constructor is not supported"
                "\n\nHint: Try evaluating the expression first using `select`,"
                " or if you meant to create an Object column containing expressions,"
                " pass a list of Expr objects instead."
            )
            raise TypeError(msg)

        dtypes = schema_overrides or {}
        data = _expand_dict_data(data, dtypes, strict=strict)
        array_len = max((arrlen(val) or 0) for val in data.values())
        if array_len > 0:
            for name, val in data.items():
                dtype = dtypes.get(name)
                if isinstance(val, dict) and dtype != Struct:
                    vdf = pl.DataFrame(val, strict=strict)
                    if (
                        vdf.height == 1
                        and array_len > 1
                        and all(not d.is_nested() for d in vdf.schema.values())
                    ):
                        s_vals = {
                            nm: vdf[nm].extend_constant(v, n=(array_len - 1))
                            for nm, v in val.items()
                        }

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