DEPS: drop np 1.21

ci/deps/actions-39-minimum_versions.yaml

@@ -22,7 +22,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil=2.8.2
-  - numpy=1.21.6
+  - numpy=1.22.4
   - pytz=2020.1
 
   # optional dependencies

doc/source/getting_started/install.rst

@@ -203,7 +203,7 @@ pandas requires the following dependencies.
 ================================================================ ==========================
 Package                                                          Minimum supported version
 ================================================================ ==========================
-`NumPy <https://numpy.org>`__                                    1.21.6
+`NumPy <https://numpy.org>`__                                    1.22.4
 `python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__ 2.8.2
 `pytz <https://pypi.org/project/pytz/>`__                        2020.1
 ================================================================ ==========================

doc/source/whatsnew/v2.1.0.rst

@@ -217,7 +217,7 @@ If installed, we now require:
 +-----------------+-----------------+----------+---------+
 | Package         | Minimum Version | Required | Changed |
 +=================+=================+==========+=========+
-| numpy           | 1.21.6          |    X     |    X    |
+| numpy           | 1.22.4          |    X     |    X    |
 +-----------------+-----------------+----------+---------+
 | mypy (dev)      | 1.4.1           |          |    X    |
 +-----------------+-----------------+----------+---------+

pandas/compat/numpy/__init__.py

@@ -6,14 +6,11 @@
 # numpy versioning
 _np_version = np.__version__
 _nlv = Version(_np_version)
-np_version_under1p22 = _nlv < Version("1.22")
 np_version_gte1p24 = _nlv >= Version("1.24")
 np_version_gte1p24p3 = _nlv >= Version("1.24.3")
 np_version_gte1p25 = _nlv >= Version("1.25")
 is_numpy_dev = _nlv.dev is not None
-_min_numpy_ver = "1.21.6"
-
-np_percentile_argname = "interpolation" if np_version_under1p22 else "method"
+_min_numpy_ver = "1.22.4"
 
 
 if _nlv < Version(_min_numpy_ver):

pandas/core/array_algos/quantile.py

@@ -4,8 +4,6 @@
 
 import numpy as np
 
-from pandas.compat.numpy import np_percentile_argname
-
 from pandas.core.dtypes.missing import (
     isna,
     na_value_for_dtype,
@@ -150,7 +148,7 @@ def _nanpercentile_1d(
         # error: No overload variant of "percentile" matches argument
         # types "ndarray[Any, Any]", "ndarray[Any, dtype[floating[_64Bit]]]"
         # , "Dict[str, str]"  [call-overload]
-        **{np_percentile_argname: interpolation},  # type: ignore[call-overload]
+        method=interpolation,  # type: ignore[call-overload]
     )
 
 
@@ -224,5 +222,5 @@ def _nanpercentile(
             # error: No overload variant of "percentile" matches argument types
             # "ndarray[Any, Any]", "ndarray[Any, dtype[floating[_64Bit]]]",
             # "int", "Dict[str, str]"  [call-overload]
-            **{np_percentile_argname: interpolation},  # type: ignore[call-overload]
+            method=interpolation,  # type: ignore[call-overload]
         )

pandas/core/frame.py

@@ -54,10 +54,7 @@
 from pandas.compat import PYPY
 from pandas.compat._constants import REF_COUNT
 from pandas.compat._optional import import_optional_dependency
-from pandas.compat.numpy import (
-    function as nv,
-    np_percentile_argname,
-)
+from pandas.compat.numpy import function as nv
 from pandas.errors import (
     ChainedAssignmentError,
     InvalidIndexError,
@@ -11699,9 +11696,7 @@ def quantile(
                     dtype = self.dtype
                 return self._constructor([], index=q, columns=data.columns, dtype=dtype)
 
-            q_idx = np.quantile(  # type: ignore[call-overload]
-                np.arange(len(data)), q, **{np_percentile_argname: interpolation}
-            )
+            q_idx = np.quantile(np.arange(len(data)), q, method=interpolation)
 
             by = data.columns
             if len(by) > 1:

pandas/tests/frame/methods/test_quantile.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat.numpy import np_percentile_argname
-
 import pandas as pd
 from pandas import (
     DataFrame,
@@ -262,7 +260,7 @@ def test_quantile_interpolation(self):
             np.array([[1, 2, 3], [2, 3, 4]]),
             0.5,
             axis=0,
-            **{np_percentile_argname: "nearest"},
+            method="nearest",
         )
         expected = Series(exp, index=[1, 2, 3], name=0.5, dtype="int64")
         tm.assert_series_equal(result, expected)
@@ -276,7 +274,7 @@ def test_quantile_interpolation(self):
             np.array([[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]),
             0.5,
             axis=0,
-            **{np_percentile_argname: "nearest"},
+            method="nearest",
         )
         expected = Series(exp, index=[1, 2, 3], name=0.5, dtype="float64")
         tm.assert_series_equal(result, expected)

pyproject.toml

@@ -10,7 +10,7 @@ requires = [
     # we don't want to force users to compile with 1.25 though
     # (Ideally, in the future, though, oldest-supported-numpy can be dropped when our min numpy is 1.25.x)
     "oldest-supported-numpy>=2022.8.16; python_version<'3.12'",
-    "numpy>=1.21.6; python_version>='3.12'",
+    "numpy>=1.22.4; python_version>='3.12'",
     "versioneer[toml]"
 ]
 
@@ -29,7 +29,7 @@ authors = [
 license = {file = 'LICENSE'}
 requires-python = '>=3.9'
 dependencies = [
-  "numpy>=1.21.6; python_version<'3.11'",
+  "numpy>=1.22.4; python_version<'3.11'",
   "numpy>=1.23.2; python_version>='3.11'",
   "python-dateutil>=2.8.2",
   "pytz>=2020.1",