pola-rs/polars · error · ModuleUpgradeRequiredError

pandas>=1.5.0 is required for…

Error message

pandas>=1.5.0 is required for `to_pandas("use_pyarrow_extension_array=True")`, found Pandas {pd.__version__}

What it means

to_pandas(use_pyarrow_extension_array=True) relies on pandas' ArrowDtype-backed extension arrays, introduced in pandas 1.5.0. If the installed pandas is older, polars raises ModuleUpgradeRequiredError with the detected version.

Solutions

Upgrade pandas: pip install -U 'pandas>=1.5.0'
Call to_pandas() without use_pyarrow_extension_array
Convert via numpy only: pd.Series(srs.to_numpy())

Example fix

// before
srs.to_pandas(use_pyarrow_extension_array=True)
// after
# either upgrade pandas>=1.5.0, or:
srs.to_pandas()

Defensive patterns

Strategy: validation

Validate before calling

from polars.dependencies import pandas as pd
from polars.utils.various import parse_version
assert parse_version(pd.__version__) >= (1, 5), 'pandas>=1.5.0 required'

Try / catch

try:
    pdf = srs.to_pandas(use_pyarrow_extension_array=True)
except pl.exceptions.ModuleUpgradeRequiredError:
    pdf = srs.to_pandas()

Prevention

Pin pandas>=1.5.0 in requirements when using arrow extension arrays
Check pd.__version__ at startup in legacy environments
Use plain to_pandas() if arrow-backed dtypes are not needed

When it happens

Trigger: srs.to_pandas(use_pyarrow_extension_array=True) with pandas < 1.5.0 installed (e.g. pandas 1.3/1.4 in pinned environments).

Common situations: Legacy environments (older conda pins, air-gapped installs) where pandas cannot be upgraded; CI images with stale pandas.

Understand the failure class

Background: "not installed", "pip install", "required for": how missing-dependency errors surface across open-source libraries — this error's family across 34 libraries.

Related errors

AI-assisted analysis of pola-rs/polars@fe841f959e (2026-09-18). Data as JSON: /api/errors/ad61452bb56bc078. Report an issue: GitHub.

Appendix: source

Thrown at py-polars/src/polars/series/series.py:5296

        Name: b, dtype: float64

        Pass `use_pyarrow_extension_array=True` to get a pandas Series backed by a
        PyArrow extension array. This will preserve null values.

        >>> s.to_pandas(use_pyarrow_extension_array=True)
        0       1
        1       2
        2    <NA>
        Name: b, dtype: int64[pyarrow]
        """
        if self.dtype == Object:
            # Can't convert via PyArrow, so do it via NumPy
            return pd.Series(self.to_numpy(), dtype=object, name=self.name)

        if use_pyarrow_extension_array:
            if parse_version(pd.__version__) < (1, 5):
                msg = f'pandas>=1.5.0 is required for `to_pandas("use_pyarrow_extension_array=True")`, found Pandas {pd.__version__}'
                raise ModuleUpgradeRequiredError(msg)
            if not _PYARROW_AVAILABLE or parse_version(pa.__version__) < (8, 0):
                raise ModuleUpgradeRequiredError(
                    f'pyarrow>=8.0.0 is required for `to_pandas("use_pyarrow_extension_array=True")`'
                    f", found pyarrow {pa.__version__!r}"
                    if _PYARROW_AVAILABLE
                    else ""
                )

        if isinstance(self.dtype, (Categorical, Enum)):
            return (
                self.to_frame()
                .to_pandas(
                    use_pyarrow_extension_array=use_pyarrow_extension_array, **kwargs
                )
                .iloc[:, 0]
            )
        else:
            pa_arr = self.to_arrow()

View on GitHub (pinned to fe841f959e)