Construction of arrays with `object` dtype very slow when Pandas `future.infer_string` is enabled

What happened?

Constructing a Variable (or Dataset/DataArray) from an object-dtype numpy array of strings takes ~0.1ms per 10M elements under pandas' default settings, but ~500–620ms with pd.options.future.infer_string = True — the setting that becomes the default in pandas 3.0.

Why:

as_compatible_data routes every object array through _possibly_convert_objects,

https://github.com/pydata/xarray/blob/adc8005a209919bf71296c7f3f6fc4c736a3f443/xarray/core/variable.py#L316-L317

which round-trips the data through pd.Series to convert datetime/timedelta objects to datetime64/timedelta64.

Under legacy (Pandas 2.x) inference, that probe was effectively free — pandas' datetime scan bails on the first non-datetime element. Under string (Pandas 3.x) inference, the same constructor's contract now includes instantiating the string dtype object, so pandas performs a full scan and materializes an Arrow string array of the entire input — which _possibly_convert_objects immediately converts back to numpy and discards:

as_compatible_data
└─ _possibly_convert_objects
   └─ pd.Series(values)                      # probe only
      └─ maybe_infer_to_datetimelike
         └─ ArrowStringArrayNumpySemantics._from_sequence   # full Arrow build
   └─ np.asarray(series)                     # Arrow array discarded

Minimal Complete Verifiable Example

import time

import numpy as np
import pandas as pd
import xarray as xr

rng = np.random.default_rng(0)
labels = np.array([f"label_{i:04d}" for i in range(500)], dtype=object)
arr = labels[rng.integers(0, len(labels), 10_000_000)]

for infer_string in (False, True):
    pd.options.future.infer_string = infer_string
    best = float("inf")
    for _ in range(5):
        t0 = time.perf_counter()
        xr.Variable(("x",), arr)
        best = min(best, time.perf_counter() - t0)
    print(f"infer_string={infer_string}: {best * 1000:.1f}ms")

Output:

infer_string=False:   0.1ms
infer_string=True:  606.3ms

MVCE confirmation

• Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• Complete example — the example is self-contained, including all data and the text of any traceback.
• Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• New issue — a search of GitHub Issues suggests this is not a duplicate.
• Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Environment

Start in xarray/core/variable.py at as_compatible_data and _possibly_convert_objects, especially the linked lines around the pd.Series probe. Reproduce the slowdown with the provided MVCE under both pandas inference settings, then verify that the datetime/timedelta conversion behavior remains correct without materializing a discarded Arrow string array; done means the object-string construction no longer incurs the reported slowdown.