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here is the log from the commit of package python-pandas for openSUSE:Factory
checked in at 2025-07-15 16:42:43
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-pandas (Old)
and /work/SRC/openSUSE:Factory/.python-pandas.new.7373 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-pandas"
Tue Jul 15 16:42:43 2025 rev:77 rq:1292272 version:2.3.1
Changes:
--------
--- /work/SRC/openSUSE:Factory/python-pandas/python-pandas.changes
2025-03-21 20:21:43.777030991 +0100
+++ /work/SRC/openSUSE:Factory/.python-pandas.new.7373/python-pandas.changes
2025-07-15 16:43:24.334866474 +0200
@@ -1,0 +2,91 @@
+Fri Jul 11 19:29:45 UTC 2025 - Ben Greiner <c...@bnavigator.de>
+
+- Update to 2.3.1
+ * This release includes some improvements and fixes to the future
+ string data type (preview feature for the upcoming pandas 3.0)
+ ## Improvements and fixes for the StringDtype
+ * Comparisons between different string dtypes
+ * Index set operations ignore empty RangeIndex and object dtype
+ Index
+ ## Bug fixes
+ * Bug in DataFrameGroupBy.min(), DataFrameGroupBy.max(),
+ Resampler.min(), Resampler.max() where all NA values of string
+ dtype would return float instead of string dtype (GH 60810)
+ * Bug in DataFrame.join() incorrectly downcasting object-dtype
+ indexes (GH 61771)
+ * Bug in DataFrame.sum() with axis=1, DataFrameGroupBy.sum() or
+ SeriesGroupBy.sum() with skipna=True, and Resampler.sum() with
+ all NA values of StringDtype resulted in 0 instead of the empty
+ string "" (GH 60229)
+ * Fixed bug in DataFrame.explode() and Series.explode() where
+ methods would fail with dtype="str" (GH 61623)
+ * Fixed bug in unpickling objects pickled in pandas versions
+ pre-2.3.0 that used StringDtype (GH 61763)
+- Release 2.3.0
+ ## Enhancements
+ * The semantics for the copy keyword in __array__ methods (i.e.
+ called when using np.array() or np.asarray() on pandas objects)
+ has been updated to work correctly with NumPy >= 2 (GH 57739)
+ * Series.str.decode() result now has StringDtype when
+ future.infer_string is True (GH 60709)
+ * to_hdf() and to_hdf() now round-trip with StringDtype (GH
+ 60663)
+ * Improved repr of NumpyExtensionArray to account for NEP51 (GH
+ 61085)
+ * The Series.str.decode() has gained the argument dtype to
+ control the dtype of the result (GH 60940)
+ * The cumsum(), cummin(), and cummax() reductions are now
+ implemented for StringDtype columns (GH 60633)
+ * The sum() reduction is now implemented for StringDtype columns
+ (GH 59853)
+ ## Deprecations
+ * Deprecated allowing non-bool values for na in str.contains(),
+ str.startswith(), and str.endswith() for dtypes that do not
+ already disallow these (GH 59615)
+ * Deprecated the "pyarrow_numpy" storage option for StringDtype
+ (GH 60152)
+ * The deprecation of setting the argument include_groups to True
+ in DataFrameGroupBy.apply() has been promoted from a
+ DeprecationWarning to FutureWarning; only False will be allowed
+ (GH 7155)
+ ## Bug fixes
+ ### Numeric
+ * Bug in Series.mode() and DataFrame.mode() with dropna=False
+ where not all dtypes would sort in the presence of NA values
+ (GH 60702)
+ * Bug in Series.round() where a TypeError would always raise with
+ object dtype (GH 61206)
+ ### Strings
+ * Bug in Series.__pos__() and DataFrame.__pos__() where an
+ Exception was not raised for StringDtype with storage="pyarrow"
+ (GH 60710)
+ * Bug in Series.rank() for StringDtype with storage="pyarrow"
+ that incorrectly returned integer results with method="average"
+ and raised an error if it would truncate results (GH 59768)
+ * Bug in Series.replace() with StringDtype when replacing with a
+ non-string value was not upcasting to object dtype (GH 60282)
+ * Bug in Series.str.center() with StringDtype with
+ storage="pyarrow" not matching the python behavior in corner
+ cases with an odd number of fill characters (GH 54792)
+ * Bug in Series.str.replace() when n < 0 for StringDtype with
+ storage="pyarrow" (GH 59628)
+ * Bug in Series.str.slice() with negative step with ArrowDtype
+ and StringDtype with storage="pyarrow" giving incorrect results
+ (GH 59710)
+ ### Indexing
+ Bug in Index.get_indexer() round-tripping through string dtype
+ when infer_string is enabled (GH 55834)
+ ### I/O
+ * Bug in DataFrame.to_excel() which stored decimals as strings
+ instead of numbers (GH 49598)
+ ### Other
+ * Fixed usage of inspect when the optional dependencies pyarrow
+ or jinja2 are not installed (GH 60196)
+- Drop patches:
+ * timedelta.patch
+ * pandas-pr60545-arrow-exception.patch
+ * pandas-pr60584-60586-mpl-vert.patch
+- Refresh dropna.patch to pandas-pr61132-dropna.patch
+ * gh#pandas-dev/pandas#61132
+
+-------------------------------------------------------------------
Old:
----
dropna.patch
pandas-2.2.3.tar.gz
pandas-pr60545-arrow-exception.patch
pandas-pr60584-60586-mpl-vert.patch
timedelta.patch
New:
----
pandas-2.3.1.tar.gz
pandas-pr61132-dropna.patch
----------(Old B)----------
Old: * pandas-pr60584-60586-mpl-vert.patch
- Refresh dropna.patch to pandas-pr61132-dropna.patch
* gh#pandas-dev/pandas#61132
Old: * timedelta.patch
* pandas-pr60545-arrow-exception.patch
* pandas-pr60584-60586-mpl-vert.patch
Old: * pandas-pr60545-arrow-exception.patch
* pandas-pr60584-60586-mpl-vert.patch
- Refresh dropna.patch to pandas-pr61132-dropna.patch
Old:- Drop patches:
* timedelta.patch
* pandas-pr60545-arrow-exception.patch
----------(Old E)----------
----------(New B)----------
New: * pandas-pr60584-60586-mpl-vert.patch
- Refresh dropna.patch to pandas-pr61132-dropna.patch
* gh#pandas-dev/pandas#61132
----------(New E)----------
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-pandas.spec ++++++
--- /var/tmp/diff_new_pack.VQ58Kt/_old 2025-07-15 16:43:27.918999766 +0200
+++ /var/tmp/diff_new_pack.VQ58Kt/_new 2025-07-15 16:43:27.935000362 +0200
@@ -37,7 +37,7 @@
%if "%{flavor}" != "test-py313"
%define skip_python313 1
%endif
-# Skip empty buildsets, last one is for sle15_python_module_pythons
+# Skip empty buildsets on tumbleweed or flavors other than python311 on leap
with sle15_python_module_pythons
%if "%{shrink:%{pythons}}" == "" || ("%pythons" == "python311" &&
0%{?skip_python311})
ExclusiveArch: donotbuild
%define python_module()
%flavor-not-enabled-in-buildset-for-suse-%{?suse_version}
@@ -64,7 +64,7 @@
%endif
Name: python-pandas%{psuffix}
# Set version through _service
-Version: 2.2.3
+Version: 2.3.1
Release: 0
Summary: Python data structures for data analysis, time series, and
statistics
License: BSD-3-Clause
@@ -72,14 +72,8 @@
# SourceRepository: https://github.com/pandas-dev/pandas
# Must be created by cloning through `osc service runall`:
gh#pandas-dev/pandas#54903, gh#pandas-dev/pandas#54907
Source0: pandas-%{version}.tar.gz
-# PATCH-FIX-UPSTREAM pandas-pr60545-arrow-exception.patch
gh#pandas-dev/pandas#60545
-Patch0:
https://github.com/pandas-dev/pandas/pull/60545.patch#/pandas-pr60545-arrow-exception.patch
-# PATCH-FIX-UPSTREAM pandas-pr60584-60586-mpl-vert.patch
gh#pandas-dev/pandas#60584 gh#pandas-dev/pandas#60586
-Patch1:
https://github.com/pandas-dev/pandas/pull/60586.patch#/pandas-pr60584-60586-mpl-vert.patch
-# PATCH-FIX-UPSTREAM https://github.com/pandas-dev/pandas/pull/61132 BUG:
.mode(dropna=False) doesn't work with nullable integers
-Patch2: dropna.patch
-# PATCH-FIX-UPSTREAM https://github.com/pandas-dev/pandas/pull/60416 TST:
Avoid hashing np.timedelta64 without unit
-Patch3: timedelta.patch
+# PATCH-FIX-UPSTREAM pandas-pr61132-dropna.patch gh#pandas-dev/pandas#61132
BUG: .mode(dropna=False) doesn't work with nullable integers
+Patch1: pandas-pr61132-dropna.patch
%if !%{with test}
BuildRequires: %{python_module Cython >= 3.0.5}
BuildRequires: %{python_module devel >= 3.9}
@@ -91,7 +85,7 @@
BuildRequires: fdupes
BuildRequires: gcc%{?gccver}-c++
BuildRequires: git-core
-BuildRequires: meson >= 1.2.1
+BuildRequires: (meson >= 1.2.1 with meson < 2)
%endif
BuildRequires: python-rpm-macros
Requires: python-python-dateutil >= 2.8.2
@@ -518,21 +512,15 @@
# expects a dirty git revision from git repo
SKIP_TESTS+=" or test_git_version"
# https://github.com/pandas-dev/pandas/pull/57391, proposed change is not
necessarily the right one
-%if "%{flavor}" == "test-py312"
+%if "%{flavor}" == "test-py312" || "%{flavor}" == "test-py313"
SKIP_TESTS+=" or (test_scalar_unary and numexpr-pandas)"
%endif
-%if "%{flavor}" == "test-py313"
-SKIP_TESTS+=" or (test_scalar_unary and numexpr-pandas)"
-%endif
-# https://github.com/pandas-dev/pandas/pull/55901, not gonna merge this huge
patch to fix one test failing with new timezone, will be included in new release
+# https://github.com/pandas-dev/pandas/pull/55901, not gonna merge this huge
patch to fix one test failing with new timezone, will be included in 3.0
SKIP_TESTS+=" or test_array_inference[data7-expected7]"
-# numpy 2.1 issues?
-SKIP_TESTS+=" or test_frame_setitem_dask_array_into_new_col"
# too new xarray, gh#pandas-dev/pandas#60109 backport too much
SKIP_TESTS+=" or (TestDataFrameToXArray and test_to_xarray_index_types)"
-# too new pyarrow, gh#pandas-dev/pandas#60755 backport too much
-SKIP_TESTS+=" or (TestParquetPyArrow and test_roundtrip_decimal)"
-
+# xpass strict: our xarray seems to handle this fine
+SKIP_TESTS+=" or (TestSeriesToXArray and test_to_xarray_index_types)"
%ifarch %{ix86} %{arm32}
# https://github.com/pandas-dev/pandas/issues/31856
SKIP_TESTS+=" or test_maybe_promote_int_with_int"
++++++ _service ++++++
--- /var/tmp/diff_new_pack.VQ58Kt/_old 2025-07-15 16:43:28.247011965 +0200
+++ /var/tmp/diff_new_pack.VQ58Kt/_new 2025-07-15 16:43:28.279013156 +0200
@@ -2,7 +2,7 @@
<service name="tar_scm" mode="disabled">
<param name="url">https://github.com/pandas-dev/pandas.git</param>
<param name="scm">git</param>
- <param name="revision">v2.2.3</param>
+ <param name="revision">v2.3.1</param>
<param name="versionformat">@PARENT_TAG@</param>
<param name="versionrewrite-pattern">v(.*)</param>
<param name="filename">pandas</param>
++++++ pandas-2.2.3.tar.gz -> pandas-2.3.1.tar.gz ++++++
/work/SRC/openSUSE:Factory/python-pandas/pandas-2.2.3.tar.gz
/work/SRC/openSUSE:Factory/.python-pandas.new.7373/pandas-2.3.1.tar.gz differ:
char 12, line 1
++++++ pandas-pr61132-dropna.patch ++++++
>From 1e899afbd9ca20f4ce9d6f93e1f62c072be0ed23 Mon Sep 17 00:00:00 2001
From: Gen Sato <52241300+haloge...@users.noreply.github.com>
Date: Tue, 18 Mar 2025 01:33:40 +0900
Subject: [PATCH] BUG: .mode(dropna=False) doesn't work with nullable integers
(#61132)
* Fix dropna bug when mode
* Fix test cases
* Fix data type incompatible
---
doc/source/whatsnew/v3.0.0.rst | 1 +
pandas/_libs/hashtable_func_helper.pxi.in | 2 +-
pandas/core/algorithms.py | 12 +++---
pandas/core/arrays/base.py | 5 ++-
pandas/core/arrays/categorical.py | 2 +-
pandas/core/arrays/datetimelike.py | 2 +-
pandas/core/arrays/masked.py | 8 +---
pandas/core/series.py | 2 +-
pandas/tests/series/test_reductions.py | 23 +++++++++++
pandas/tests/test_algos.py | 47 +++++++++++++++--------
10 files changed, 71 insertions(+), 33 deletions(-)
Index: pandas-2.3.1/pandas/_libs/hashtable_func_helper.pxi.in
===================================================================
--- pandas-2.3.1.orig/pandas/_libs/hashtable_func_helper.pxi.in
+++ pandas-2.3.1/pandas/_libs/hashtable_func_helper.pxi.in
@@ -443,7 +443,7 @@ def mode(ndarray[htfunc_t] values, bint
if na_counter > 0:
res_mask = np.zeros(j+1, dtype=np.bool_)
- res_mask[j] = True
+ res_mask[j] = (na_counter == max_count)
return modes[:j + 1], res_mask
Index: pandas-2.3.1/pandas/core/algorithms.py
===================================================================
--- pandas-2.3.1.orig/pandas/core/algorithms.py
+++ pandas-2.3.1/pandas/core/algorithms.py
@@ -1022,7 +1022,7 @@ def duplicated(
def mode(
values: ArrayLike, dropna: bool = True, mask: npt.NDArray[np.bool_] | None
= None
-) -> ArrayLike:
+) -> tuple[np.ndarray, npt.NDArray[np.bool_]] | ExtensionArray:
"""
Returns the mode(s) of an array.
@@ -1035,7 +1035,7 @@ def mode(
Returns
-------
- np.ndarray or ExtensionArray
+ Union[Tuple[np.ndarray, npt.NDArray[np.bool_]], ExtensionArray]
"""
values = _ensure_arraylike(values, func_name="mode")
original = values
@@ -1049,8 +1049,10 @@ def mode(
values = _ensure_data(values)
npresult, res_mask = htable.mode(values, dropna=dropna, mask=mask)
- if res_mask is not None:
- return npresult, res_mask # type: ignore[return-value]
+ if res_mask is None:
+ res_mask = np.zeros(npresult.shape, dtype=np.bool_)
+ else:
+ return npresult, res_mask
try:
npresult = safe_sort(npresult)
@@ -1061,7 +1063,7 @@ def mode(
)
result = _reconstruct_data(npresult, original.dtype, original)
- return result
+ return result, res_mask
def rank(
Index: pandas-2.3.1/pandas/core/arrays/base.py
===================================================================
--- pandas-2.3.1.orig/pandas/core/arrays/base.py
+++ pandas-2.3.1/pandas/core/arrays/base.py
@@ -2270,8 +2270,9 @@ class ExtensionArray:
Sorted, if possible.
"""
# error: Incompatible return value type (got "Union[ExtensionArray,
- # ndarray[Any, Any]]", expected "Self")
- return mode(self, dropna=dropna) # type: ignore[return-value]
+ # Tuple[np.ndarray, npt.NDArray[np.bool_]]", expected "Self")
+ result, _ = mode(self, dropna=dropna)
+ return result # type: ignore[return-value]
def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
if any(
Index: pandas-2.3.1/pandas/core/arrays/categorical.py
===================================================================
--- pandas-2.3.1.orig/pandas/core/arrays/categorical.py
+++ pandas-2.3.1/pandas/core/arrays/categorical.py
@@ -2459,7 +2459,7 @@ class Categorical(NDArrayBackedExtension
if dropna:
mask = self.isna()
- res_codes = algorithms.mode(codes, mask=mask)
+ res_codes, _ = algorithms.mode(codes, mask=mask)
res_codes = cast(np.ndarray, res_codes)
assert res_codes.dtype == codes.dtype
res = self._from_backing_data(res_codes)
Index: pandas-2.3.1/pandas/core/arrays/datetimelike.py
===================================================================
--- pandas-2.3.1.orig/pandas/core/arrays/datetimelike.py
+++ pandas-2.3.1/pandas/core/arrays/datetimelike.py
@@ -1669,7 +1669,7 @@ class DatetimeLikeArrayMixin( # type: i
if dropna:
mask = self.isna()
- i8modes = algorithms.mode(self.view("i8"), mask=mask)
+ i8modes, _ = algorithms.mode(self.view("i8"), mask=mask)
npmodes = i8modes.view(self._ndarray.dtype)
npmodes = cast(np.ndarray, npmodes)
return self._from_backing_data(npmodes)
Index: pandas-2.3.1/pandas/core/arrays/masked.py
===================================================================
--- pandas-2.3.1.orig/pandas/core/arrays/masked.py
+++ pandas-2.3.1/pandas/core/arrays/masked.py
@@ -1124,12 +1124,8 @@ class BaseMaskedArray(OpsMixin, Extensio
return Series(arr, index=index, name="count", copy=False)
def _mode(self, dropna: bool = True) -> Self:
- if dropna:
- result = mode(self._data, dropna=dropna, mask=self._mask)
- res_mask = np.zeros(result.shape, dtype=np.bool_)
- else:
- result, res_mask = mode(self._data, dropna=dropna, mask=self._mask)
- result = type(self)(result, res_mask) # type: ignore[arg-type]
+ result, res_mask = mode(self._data, dropna=dropna, mask=self._mask)
+ result = type(self)(result, res_mask)
return result[result.argsort()]
@doc(ExtensionArray.equals)
Index: pandas-2.3.1/pandas/core/series.py
===================================================================
--- pandas-2.3.1.orig/pandas/core/series.py
+++ pandas-2.3.1/pandas/core/series.py
@@ -2337,7 +2337,7 @@ class Series(base.IndexOpsMixin, NDFrame
# TODO: Add option for bins like value_counts()
values = self._values
if isinstance(values, np.ndarray):
- res_values = algorithms.mode(values, dropna=dropna)
+ res_values, _ = algorithms.mode(values, dropna=dropna)
else:
res_values = values._mode(dropna=dropna)
Index: pandas-2.3.1/pandas/tests/series/test_reductions.py
===================================================================
--- pandas-2.3.1.orig/pandas/tests/series/test_reductions.py
+++ pandas-2.3.1/pandas/tests/series/test_reductions.py
@@ -51,6 +51,29 @@ def test_mode_nullable_dtype(any_numeric
tm.assert_series_equal(result, expected)
+def test_mode_nullable_dtype_edge_case(any_numeric_ea_dtype):
+ # GH##58926
+ ser = Series([1, 2, 3, 1], dtype=any_numeric_ea_dtype)
+ result = ser.mode(dropna=False)
+ expected = Series([1], dtype=any_numeric_ea_dtype)
+ tm.assert_series_equal(result, expected)
+
+ ser2 = Series([1, 1, 2, 3, pd.NA], dtype=any_numeric_ea_dtype)
+ result = ser2.mode(dropna=False)
+ expected = Series([1], dtype=any_numeric_ea_dtype)
+ tm.assert_series_equal(result, expected)
+
+ ser3 = Series([1, pd.NA, pd.NA], dtype=any_numeric_ea_dtype)
+ result = ser3.mode(dropna=False)
+ expected = Series([pd.NA], dtype=any_numeric_ea_dtype)
+ tm.assert_series_equal(result, expected)
+
+ ser4 = Series([1, 1, pd.NA, pd.NA], dtype=any_numeric_ea_dtype)
+ result = ser4.mode(dropna=False)
+ expected = Series([1, pd.NA], dtype=any_numeric_ea_dtype)
+ tm.assert_series_equal(result, expected)
+
+
def test_mode_infer_string():
# GH#56183
pytest.importorskip("pyarrow")
Index: pandas-2.3.1/pandas/tests/test_algos.py
===================================================================
--- pandas-2.3.1.orig/pandas/tests/test_algos.py
+++ pandas-2.3.1/pandas/tests/test_algos.py
@@ -1855,7 +1855,8 @@ class TestRank:
class TestMode:
def test_no_mode(self):
exp = Series([], dtype=np.float64, index=Index([], dtype=int))
- tm.assert_numpy_array_equal(algos.mode(np.array([])), exp.values)
+ result, _ = algos.mode(np.array([]))
+ tm.assert_numpy_array_equal(result, exp.values)
@pytest.mark.parametrize("dt", np.typecodes["AllInteger"] +
np.typecodes["Float"])
def test_mode_single(self, dt):
@@ -1868,20 +1869,24 @@ class TestMode:
ser = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
ser = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_mode_obj_int(self):
exp = Series([1], dtype=int)
- tm.assert_numpy_array_equal(algos.mode(exp.values), exp.values)
+ result, _ = algos.mode(exp.values)
+ tm.assert_numpy_array_equal(result, exp.values)
exp = Series(["a", "b", "c"], dtype=object)
- tm.assert_numpy_array_equal(algos.mode(exp.values), exp.values)
+ result, _ = algos.mode(exp.values)
+ tm.assert_numpy_array_equal(result, exp.values)
@pytest.mark.parametrize("dt", np.typecodes["AllInteger"] +
np.typecodes["Float"])
def test_number_mode(self, dt):
@@ -1893,12 +1898,14 @@ class TestMode:
ser = Series(data_single, dtype=dt)
exp = Series(exp_single, dtype=dt)
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
ser = Series(data_multi, dtype=dt)
exp = Series(exp_multi, dtype=dt)
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_strobj_mode(self):
@@ -1907,7 +1914,8 @@ class TestMode:
ser = Series(data, dtype="c")
exp = Series(exp, dtype="c")
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
@pytest.mark.parametrize("dt", [str, object])
@@ -1920,7 +1928,8 @@ class TestMode:
if using_infer_string and dt is str:
tm.assert_extension_array_equal(algos.mode(ser.values), exp.values)
else:
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_datelike_mode(self):
@@ -1954,18 +1963,21 @@ class TestMode:
def test_mixed_dtype(self):
exp = Series(["foo"], dtype=object)
ser = Series([1, "foo", "foo"])
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_uint64_overflow(self):
exp = Series([2**63], dtype=np.uint64)
ser = Series([1, 2**63, 2**63], dtype=np.uint64)
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
exp = Series([1, 2**63], dtype=np.uint64)
ser = Series([1, 2**63], dtype=np.uint64)
- tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
+ result, _ = algos.mode(ser.values)
+ tm.assert_numpy_array_equal(result, exp.values)
tm.assert_series_equal(ser.mode(), exp)
def test_categorical(self):
@@ -1987,15 +1999,18 @@ class TestMode:
def test_index(self):
idx = Index([1, 2, 3])
exp = Series([1, 2, 3], dtype=np.int64)
- tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
+ result, _ = algos.mode(idx)
+ tm.assert_numpy_array_equal(result, exp.values)
idx = Index([1, "a", "a"])
exp = Series(["a"], dtype=object)
- tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
+ result, _ = algos.mode(idx)
+ tm.assert_numpy_array_equal(result, exp.values)
idx = Index([1, 1, 2, 3, 3])
exp = Series([1, 3], dtype=np.int64)
- tm.assert_numpy_array_equal(algos.mode(idx), exp.values)
+ result, _ = algos.mode(idx)
+ tm.assert_numpy_array_equal(result, exp.values)
idx = Index(
["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"],
