DEPS: drop np 1.20

ci/deps/actions-38-minimum_versions.yaml

@@ -19,7 +19,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil=2.8.2
-  - numpy=1.20.3
+  - numpy=1.21.6
   - pytz=2020.1
 
   # optional dependencies

doc/source/getting_started/install.rst

@@ -260,7 +260,7 @@ pandas requires the following dependencies.
 ================================================================ ==========================
 Package                                                          Minimum supported version
 ================================================================ ==========================
-`NumPy <https://numpy.org>`__                                    1.20.3
+`NumPy <https://numpy.org>`__                                    1.21.6
 `python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__ 2.8.2
 `pytz <https://pypi.org/project/pytz/>`__                        2020.1
 ================================================================ ==========================

pandas/compat/__init__.py

@@ -22,10 +22,7 @@
     PYPY,
 )
 import pandas.compat.compressors
-from pandas.compat.numpy import (
-    is_numpy_dev,
-    np_version_under1p21,
-)
+from pandas.compat.numpy import is_numpy_dev
 from pandas.compat.pyarrow import (
     pa_version_under7p0,
     pa_version_under8p0,
@@ -159,7 +156,6 @@ def get_lzma_file() -> type[pandas.compat.compressors.LZMAFile]:
 
 __all__ = [
     "is_numpy_dev",
-    "np_version_under1p21",
     "pa_version_under7p0",
     "pa_version_under8p0",
     "pa_version_under9p0",

pandas/compat/numpy/__init__.py

@@ -6,17 +6,12 @@
 # numpy versioning
 _np_version = np.__version__
 _nlv = Version(_np_version)
-np_version_under1p21 = _nlv < Version("1.21")
 np_version_under1p22 = _nlv < Version("1.22")
-np_version_gte1p22 = _nlv >= Version("1.22")
 np_version_gte1p24 = _nlv >= Version("1.24")
 is_numpy_dev = _nlv.dev is not None
-_min_numpy_ver = "1.20.3"
+_min_numpy_ver = "1.21.6"
 
-if is_numpy_dev or not np_version_under1p22:
-    np_percentile_argname = "method"
-else:
-    np_percentile_argname = "interpolation"
+np_percentile_argname = "interpolation" if np_version_under1p22 else "method"
 
 
 if _nlv < Version(_min_numpy_ver):

pandas/core/array_algos/putmask.py

@@ -11,7 +11,6 @@
 import numpy as np
 
 from pandas._libs import lib
-from pandas.compat import np_version_under1p21
 
 from pandas.core.dtypes.cast import infer_dtype_from
 from pandas.core.dtypes.common import is_list_like
@@ -73,9 +72,6 @@ def putmask_without_repeat(
     mask : np.ndarray[bool]
     new : Any
     """
-    if np_version_under1p21:
-        new = setitem_datetimelike_compat(values, mask.sum(), new)
-
     if getattr(new, "ndim", 0) >= 1:
         new = new.astype(values.dtype, copy=False)
 

pandas/tests/extension/base/casting.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import np_version_under1p21
 import pandas.util._test_decorators as td
 
 import pandas as pd
@@ -31,11 +30,9 @@ def test_astype_object_frame(self, all_data):
         assert isinstance(result._mgr.arrays[0], np.ndarray)
         assert result._mgr.arrays[0].dtype == np.dtype(object)
 
-        # earlier numpy raises TypeError on e.g. np.dtype(np.int64) == "Int64"
-        if not np_version_under1p21:
-            # check that we can compare the dtypes
-            comp = result.dtypes == df.dtypes
-            assert not comp.any()
+        # check that we can compare the dtypes
+        comp = result.dtypes == df.dtypes
+        assert not comp.any()
 
     def test_tolist(self, data):
         result = pd.Series(data).tolist()

pandas/tests/frame/methods/test_quantile.py

@@ -1,10 +1,7 @@
 import numpy as np
 import pytest
 
-from pandas.compat.numpy import (
-    np_percentile_argname,
-    np_version_under1p21,
-)
+from pandas.compat.numpy import np_percentile_argname
 
 import pandas as pd
 from pandas import (
@@ -848,11 +845,6 @@ def test_quantile_ea(self, request, obj, index):
         qs = [0.5, 0, 1]
         result = self.compute_quantile(obj, qs)
 
-        if np_version_under1p21 and index.dtype == "timedelta64[ns]":
-            msg = "failed on Numpy 1.20.3; TypeError: data type 'Int64' not understood"
-            mark = pytest.mark.xfail(reason=msg, raises=TypeError)
-            request.node.add_marker(mark)
-
         exp_dtype = index.dtype
         if index.dtype == "Int64":
             # match non-nullable casting behavior
@@ -914,11 +906,6 @@ def test_quantile_ea_scalar(self, request, obj, index):
         qs = 0.5
         result = self.compute_quantile(obj, qs)
 
-        if np_version_under1p21 and index.dtype == "timedelta64[ns]":
-            msg = "failed on Numpy 1.20.3; TypeError: data type 'Int64' not understood"
-            mark = pytest.mark.xfail(reason=msg, raises=TypeError)
-            request.node.add_marker(mark)
-
         exp_dtype = index.dtype
         if index.dtype == "Int64":
             exp_dtype = "Float64"