Fix NA semantics for numpy.StringDType #10967
Description
What happened?
StringDType() with na_object=np.nan and na_object=None is only partially or not supported.
What did you expect to happen?
Similar behavior to python object strings
Minimal Complete Verifiable Example
# /// script
# requires-python = ">=3.11"
# dependencies = [
# "xarray[complete]@git+https://github.com/pydata/xarray.git@main",
# ]
# ///
#
# This script automatically imports the development branch of xarray to check for issues.
# Please delete this header if you have _not_ tested this script with `uv run`!
import xarray as xr
xr.show_versions()
# your reproducer code ...
"""
Check how xarray handles NumPy StringDType with different na_object values.
It compares:
- object-typed DataArray / Dataset (baseline)
- StringDType-typed DataArray / Dataset (under test)
For each case it runs:
- .isnull()
- .count()
- .dropna(dim="x")
- .where(~isnull, other="filled")
and records success / failure (with the error message, including na_object).
"""
from __future__ import annotations
import numpy as np
import xarray as xr
from numpy.dtypes import StringDType
from textwrap import shorten
NA_OBJECTS = [np.nan, None]
OPS = ["isnull", "count", "dropna", "where"]
def _short(obj, width=40):
return shorten(repr(obj), width=width, placeholder="…")
def run_op(kind: str, da_or_var: xr.DataArray):
"""
Run the four operations on a DataArray (or Dataset variable) and
return a dict: {op_name: (ok: bool, detail: str)}.
"""
results: dict[str, tuple[bool, str]] = {}
# isnull
try:
res = da_or_var.isnull()
results["isnull"] = (True, f"mask={_short(res.values)}")
except Exception as e: # noqa: BLE001
results["isnull"] = (False, f"{type(e).__name__}: {_short(e)}")
# count
try:
res = da_or_var.count()
results["count"] = (True, f"value={_short(res.values)}")
except Exception as e: # noqa: BLE001
results["count"] = (False, f"{type(e).__name__}: {_short(e)}")
# dropna
try:
res = da_or_var.dropna(dim="x")
results["dropna"] = (True, f"values={_short(res.values)}")
except Exception as e: # noqa: BLE001
results["dropna"] = (False, f"{type(e).__name__}: {_short(e)}")
# where(~isnull, "filled")
try:
mask = da_or_var.isnull()
res = da_or_var.where(~mask, other="filled")
results["where"] = (True, f"values={_short(res.values)}")
except Exception as e: # noqa: BLE001
results["where"] = (False, f"{type(e).__name__}: {_short(e)}")
return results
def print_case_header(title: str, arr: np.ndarray, dtype, na_obj):
print(f"\n=== {title} ===")
print(f" storage dtype: {dtype!r} (type={type(dtype)})")
print(f" na_object: {na_obj!r}")
print(f" values: {arr!r}")
def print_results_table(results: dict[str, tuple[bool, str]]):
# Simple ASCII table: op | OK/FAIL | detail
col_op = max(len("op"), max(len(op) for op in OPS))
col_ok = len("status")
col_detail = 60
header = f" {'op'.ljust(col_op)} {'status'.ljust(col_ok)} detail"
sep = " " + "-" * col_op + " " + "-" * col_ok + " " + "-" * col_detail
print(header)
print(sep)
for op in OPS:
ok, detail = results[op]
status = "OK" if ok else "FAIL"
print(f" {op.ljust(col_op)} {status.ljust(col_ok)} {_short(detail, col_detail)}")
def main():
print("NumPy version:", np.__version__)
print("xarray version:", xr.__version__)
print("============================================================")
summary_rows = []
# 1) Baseline: object dtype (no custom na_object)
arr_obj = np.array(["a", np.nan, "c"], dtype=object)
# DataArray baseline
da_obj = xr.DataArray(arr_obj, dims="x", name="str_obj")
print_case_header("DataArray – object dtype (baseline)", da_obj.values, da_obj.dtype, na_obj=None)
res_da_obj = run_op("DataArray-object", da_obj)
print_results_table(res_da_obj)
summary_rows.append(
("DataArray", "object", "-", sum(r[0] for r in res_da_obj.values()), sum(not r[0] for r in res_da_obj.values()))
)
# Dataset baseline
ds_obj = xr.Dataset({"str": ("x", arr_obj)})
var_obj = ds_obj["str"]
print_case_header("Dataset['str'] – object dtype (baseline)", var_obj.values, var_obj.dtype, na_obj=None)
res_ds_obj = run_op("Dataset-object", var_obj)
print_results_table(res_ds_obj)
summary_rows.append(
("Dataset['str']", "object", "-", sum(r[0] for r in res_ds_obj.values()), sum(not r[0] for r in res_ds_obj.values()))
)
# 2) StringDType with different na_object values
for na_obj in NA_OBJECTS:
dt = StringDType(na_object=na_obj)
arr_str = np.array(["a", na_obj, "c"], dtype=dt)
# DataArray with StringDType
da_str = xr.DataArray(arr_str, dims="x", name=f"str_StringDType_{type(na_obj).__name__}")
print_case_header(f"DataArray – StringDType(na_object={na_obj!r})", da_str.values, da_str.dtype, na_obj)
res_da_str = run_op("DataArray-StringDType", da_str)
print_results_table(res_da_str)
summary_rows.append(
(
"DataArray",
"StringDType",
repr(na_obj),
sum(r[0] for r in res_da_str.values()),
sum(not r[0] for r in res_da_str.values()),
)
)
# Dataset with StringDType
ds_str = xr.Dataset({"str": ("x", arr_str)})
var_str = ds_str["str"]
print_case_header(
f"Dataset['str'] – StringDType(na_object={na_obj!r})",
var_str.values,
var_str.dtype,
na_obj,
)
res_ds_str = run_op("Dataset-StringDType", var_str)
print_results_table(res_ds_str)
summary_rows.append(
(
"Dataset['str']",
"StringDType",
repr(na_obj),
sum(r[0] for r in res_ds_str.values()),
sum(not r[0] for r in res_ds_str.values()),
)
)
# Summary matrix
print("\n==================== SUMMARY ====================")
col_scope = max(len("scope"), max(len(r[0]) for r in summary_rows))
col_storage = max(len("storage"), max(len(r[1]) for r in summary_rows))
col_naobj = max(len("na_object"), max(len(r[2]) for r in summary_rows))
header = (
f"{'scope'.ljust(col_scope)} "
f"{'storage'.ljust(col_storage)} "
f"{'na_object'.ljust(col_naobj)} "
f"{'#OK':>3} "
f"{'#FAIL':>5}"
)
sep = "-" * len(header)
print(header)
print(sep)
for scope, storage, na_obj_repr, ok_count, fail_count in summary_rows:
print(
f"{scope.ljust(col_scope)} "
f"{storage.ljust(col_storage)} "
f"{na_obj_repr.ljust(col_naobj)} "
f"{ok_count:>3} "
f"{fail_count:>5}"
)
if __name__ == "__main__":
main()Steps to reproduce
No response
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.
Relevant log output
NumPy version: 2.3.5
xarray version: 2025.11.0
============================================================
=== DataArray – object dtype (baseline) ===
storage dtype: dtype('O') (type=<class 'numpy.dtypes.ObjectDType'>)
na_object: None
values: array(['a', nan, 'c'], dtype=object)
op status detail
------ ------ ------------------------------------------------------------
isnull OK 'mask=array([False, True, False])'
count OK 'value=array(2)'
dropna OK "values=array(['a', 'c'], dtype=object)"
where OK "values=array(['a', 'filled', 'c'],…"
=== Dataset['str'] – object dtype (baseline) ===
storage dtype: dtype('O') (type=<class 'numpy.dtypes.ObjectDType'>)
na_object: None
values: array(['a', nan, 'c'], dtype=object)
op status detail
------ ------ ------------------------------------------------------------
isnull OK 'mask=array([False, True, False])'
count OK 'value=array(2)'
dropna OK "values=array(['a', 'c'], dtype=object)"
where OK "values=array(['a', 'filled', 'c'],…"
=== DataArray – StringDType(na_object=nan) ===
storage dtype: StringDType(na_object=nan) (type=<class 'numpy.dtypes.StringDType'>)
na_object: nan
values: array(['a', nan, 'c'], dtype=StringDType(na_object=nan))
op status detail
------ ------ ------------------------------------------------------------
isnull FAIL 'TypeError: TypeError("dtype argument must be a…'
count FAIL 'TypeError: TypeError("dtype argument must be a…'
dropna FAIL 'TypeError: TypeError("dtype argument must be a…'
where FAIL 'TypeError: TypeError("dtype argument must be a…'
=== Dataset['str'] – StringDType(na_object=nan) ===
storage dtype: StringDType(na_object=nan) (type=<class 'numpy.dtypes.StringDType'>)
na_object: nan
values: array(['a', nan, 'c'], dtype=StringDType(na_object=nan))
op status detail
------ ------ ------------------------------------------------------------
isnull FAIL 'TypeError: TypeError("dtype argument must be a…'
count FAIL 'TypeError: TypeError("dtype argument must be a…'
dropna FAIL 'TypeError: TypeError("dtype argument must be a…'
where FAIL 'TypeError: TypeError("dtype argument must be a…'
=== DataArray – StringDType(na_object=None) ===
storage dtype: StringDType(na_object=None) (type=<class 'numpy.dtypes.StringDType'>)
na_object: None
values: array(['a', None, 'c'], dtype=StringDType(na_object=None))
op status detail
------ ------ ------------------------------------------------------------
isnull FAIL 'TypeError: TypeError("dtype argument must be a…'
count FAIL 'TypeError: TypeError("dtype argument must be a…'
dropna FAIL 'TypeError: TypeError("dtype argument must be a…'
where FAIL 'TypeError: TypeError("dtype argument must be a…'
=== Dataset['str'] – StringDType(na_object=None) ===
storage dtype: StringDType(na_object=None) (type=<class 'numpy.dtypes.StringDType'>)
na_object: None
values: array(['a', None, 'c'], dtype=StringDType(na_object=None))
op status detail
------ ------ ------------------------------------------------------------
isnull FAIL 'TypeError: TypeError("dtype argument must be a…'
count FAIL 'TypeError: TypeError("dtype argument must be a…'
dropna FAIL 'TypeError: TypeError("dtype argument must be a…'
where FAIL 'TypeError: TypeError("dtype argument must be a…'
==================== SUMMARY ====================
scope storage na_object #OK #FAIL
--------------------------------------------------
DataArray object - 4 0
Dataset['str'] object - 4 0
DataArray StringDType nan 0 4
Dataset['str'] StringDType nan 0 4
DataArray StringDType None 0 4
Dataset['str'] StringDType None 0 4Anything else we need to know?
No response
Environment
NumPy version: 2.3.5
xarray version: 2025.11.0