DEPS: drop np 1.20

.github/workflows/sdist.yml

@@ -81,11 +81,11 @@ jobs:
       run: |
         case "${{matrix.python-version}}" in
         3.8)
-          pip install numpy==1.20.3 ;;
+          pip install numpy==1.21.6 ;;
         3.9)
-          pip install numpy==1.20.3 ;;
+          pip install numpy==1.21.6 ;;
         3.10)
-          pip install numpy==1.21.2 ;;
+          pip install numpy==1.21.6 ;;
         3.11)
           pip install numpy==1.23.2 ;;
         esac

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
 ================================================================ ==========================

doc/source/whatsnew/v2.1.0.rst

@@ -131,6 +131,8 @@ If installed, we now require:
 +-----------------+-----------------+----------+---------+
 | Package         | Minimum Version | Required | Changed |
 +=================+=================+==========+=========+
+| numpy           | 1.21.6          |    X     |    X    |
++-----------------+-----------------+----------+---------+
 | mypy (dev)      | 1.2             |          |    X    |
 +-----------------+-----------------+----------+---------+
 | beautifulsoup4  | 4.11.1          |          |    X    |

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/extension/base/dtype.py

@@ -62,14 +62,7 @@ def test_check_dtype(self, data):
             {"A": pd.Series(data, dtype=dtype), "B": data, "C": "foo", "D": 1}
         )
         result = df.dtypes == str(dtype)
-
-        try:
-            new_numpy_behavior = np.dtype("int64") != "Int64"
-        except TypeError:
-            # numpy<=1.20.3 this comparison could raise or in some cases
-            #  come back True
-            new_numpy_behavior = True
-        assert new_numpy_behavior
+        assert np.dtype("int64") != "Int64"
 
         expected = pd.Series([True, True, False, False], index=list("ABCD"))
 

pandas/tests/extension/test_arrow.py

@@ -600,17 +600,6 @@ def test_in_numeric_groupby(self, data_for_grouping):
 
 
 class TestBaseDtype(base.BaseDtypeTests):
-    def test_check_dtype(self, data, request):
-        pa_dtype = data.dtype.pyarrow_dtype
-        if pa.types.is_decimal(pa_dtype) and pa_version_under8p0:
-            request.node.add_marker(
-                pytest.mark.xfail(
-                    raises=ValueError,
-                    reason="decimal string repr affects numpy comparison",
-                )
-            )
-        super().test_check_dtype(data)
-
     def test_construct_from_string_own_name(self, dtype, request):
         pa_dtype = dtype.pyarrow_dtype
         if pa.types.is_decimal(pa_dtype):

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"

pyproject.toml

@@ -23,8 +23,7 @@ authors = [
 license = {file = 'LICENSE'}
 requires-python = '>=3.8'
 dependencies = [
-  "numpy>=1.20.3; python_version<'3.10'",
-  "numpy>=1.21.0; python_version>='3.10'",
+  "numpy>=1.21.6; python_version<'3.11'",
   "numpy>=1.23.2; python_version>='3.11'",
   "python-dateutil>=2.8.2",
   "pytz>=2020.1",