Merge branch 'master' into feature/generalized_window_operations

Author: Matt Roeschke

doc/source/whatsnew/v1.0.0.rst

@@ -184,6 +184,7 @@ Indexing
 - Bug in :meth:`DataFrame.explode` would duplicate frame in the presence of duplicates in the index (:issue:`28010`)
 - Bug in reindexing a :meth:`PeriodIndex` with another type of index that contained a `Period` (:issue:`28323`) (:issue:`28337`)
 - Fix assignment of column via `.loc` with numpy non-ns datetime type (:issue:`27395`)
+- Bug in :meth:`Float64Index.astype` where ``np.inf`` was not handled properly when casting to an integer dtype (:issue:`28475`)
 
 Missing
 ^^^^^^^
@@ -240,7 +241,7 @@ Sparse
 ExtensionArray
 ^^^^^^^^^^^^^^
 
--
+- Bug in :class:`arrays.PandasArray` when setting a scalar string (:issue:`28118`, :issue:`28150`).
 -
 
 

pandas/_libs/testing.pyx

@@ -143,11 +143,9 @@ cpdef assert_almost_equal(a, b,
                 from pandas.util.testing import assert_attr_equal
                 assert_attr_equal('dtype', a, b, obj=obj)
 
-            try:
-                if array_equivalent(a, b, strict_nan=True):
-                    return True
-            except:
-                pass
+            if array_equivalent(a, b, strict_nan=True):
+                return True
+
         else:
             na, nb = len(a), len(b)
 

pandas/core/arrays/numpy_.py

@@ -235,15 +235,8 @@ def __setitem__(self, key, value):
         if not lib.is_scalar(value):
             value = np.asarray(value)
 
-        values = self._ndarray
-        t = np.result_type(value, values)
-        if t != self._ndarray.dtype:
-            values = values.astype(t, casting="safe")
-            values[key] = value
-            self._dtype = PandasDtype(t)
-            self._ndarray = values
-        else:
-            self._ndarray[key] = value
+        value = np.asarray(value, dtype=self._ndarray.dtype)
+        self._ndarray[key] = value
 
     def __len__(self) -> int:
         return len(self._ndarray)

pandas/core/dtypes/missing.py

@@ -445,7 +445,7 @@ def array_equivalent(left, right, strict_nan=False):
                 if not isinstance(right_value, float) or not np.isnan(right_value):
                     return False
             else:
-                if left_value != right_value:
+                if np.any(left_value != right_value):
                     return False
         return True
 

pandas/core/indexes/base.py

@@ -452,7 +452,7 @@ def __new__(
         elif hasattr(data, "__array__"):
             return Index(np.asarray(data), dtype=dtype, copy=copy, name=name, **kwargs)
         elif data is None or is_scalar(data):
-            cls._scalar_data_error(data)
+            raise cls._scalar_data_error(data)
         else:
             if tupleize_cols and is_list_like(data):
                 # GH21470: convert iterable to list before determining if empty
@@ -4020,7 +4020,9 @@ def _try_convert_to_int_index(cls, data, copy, name, dtype):
 
     @classmethod
     def _scalar_data_error(cls, data):
-        raise TypeError(
+        # We return the TypeError so that we can raise it from the constructor
+        #  in order to keep mypy happy
+        return TypeError(
             "{0}(...) must be called with a collection of some "
             "kind, {1} was passed".format(cls.__name__, repr(data))
         )
@@ -4048,7 +4050,7 @@ def _coerce_to_ndarray(cls, data):
 
         if not isinstance(data, (np.ndarray, Index)):
             if data is None or is_scalar(data):
-                cls._scalar_data_error(data)
+                raise cls._scalar_data_error(data)
 
             # other iterable of some kind
             if not isinstance(data, (ABCSeries, list, tuple)):

pandas/core/indexes/category.py

@@ -194,7 +194,7 @@ def __new__(
             # if data is None, then categories must be provided
             if is_scalar(data):
                 if data is not None or categories is None:
-                    cls._scalar_data_error(data)
+                    raise cls._scalar_data_error(data)
                 data = []
 
         data = cls._create_categorical(data, dtype=dtype)

pandas/core/indexes/numeric.py

@@ -5,6 +5,7 @@
 from pandas._libs import index as libindex
 from pandas.util._decorators import Appender, cache_readonly
 
+from pandas.core.dtypes.cast import astype_nansafe
 from pandas.core.dtypes.common import (
     is_bool,
     is_bool_dtype,
@@ -367,12 +368,11 @@ def astype(self, dtype, copy=True):
                 "values are required for conversion"
             ).format(dtype=dtype)
             raise TypeError(msg)
-        elif (
-            is_integer_dtype(dtype) and not is_extension_array_dtype(dtype)
-        ) and self.hasnans:
+        elif is_integer_dtype(dtype) and not is_extension_array_dtype(dtype):
             # TODO(jreback); this can change once we have an EA Index type
             # GH 13149
-            raise ValueError("Cannot convert NA to integer")
+            arr = astype_nansafe(self.values, dtype=dtype)
+            return Int64Index(arr)
         return super().astype(dtype, copy=copy)
 
     @Appender(_index_shared_docs["_convert_scalar_indexer"])

pandas/tests/arithmetic/test_datetime64.py

@@ -78,17 +78,6 @@ def assert_invalid_comparison(left, right, box):
         right >= left
 
 
-def assert_all(obj):
-    """
-    Test helper to call call obj.all() the appropriate number of times on
-    a Series or DataFrame.
-    """
-    if isinstance(obj, pd.DataFrame):
-        assert obj.all().all()
-    else:
-        assert obj.all()
-
-
 # ------------------------------------------------------------------
 # Comparisons
 
@@ -578,17 +567,17 @@ def test_comparison_tzawareness_compat(self, op, box_df_fail):
             op(dz, np.array(list(dr), dtype=object))
 
         # The aware==aware and naive==naive comparisons should *not* raise
-        assert_all(dr == dr)
-        assert_all(dr == list(dr))
-        assert_all(list(dr) == dr)
-        assert_all(np.array(list(dr), dtype=object) == dr)
-        assert_all(dr == np.array(list(dr), dtype=object))
-
-        assert_all(dz == dz)
-        assert_all(dz == list(dz))
-        assert_all(list(dz) == dz)
-        assert_all(np.array(list(dz), dtype=object) == dz)
-        assert_all(dz == np.array(list(dz), dtype=object))
+        assert np.all(dr == dr)
+        assert np.all(dr == list(dr))
+        assert np.all(list(dr) == dr)
+        assert np.all(np.array(list(dr), dtype=object) == dr)
+        assert np.all(dr == np.array(list(dr), dtype=object))
+
+        assert np.all(dz == dz)
+        assert np.all(dz == list(dz))
+        assert np.all(list(dz) == dz)
+        assert np.all(np.array(list(dz), dtype=object) == dz)
+        assert np.all(dz == np.array(list(dz), dtype=object))
 
     @pytest.mark.parametrize(
         "op",
@@ -606,12 +595,12 @@ def test_comparison_tzawareness_compat_scalars(self, op, box_with_array):
         ts = pd.Timestamp("2000-03-14 01:59")
         ts_tz = pd.Timestamp("2000-03-14 01:59", tz="Europe/Amsterdam")
 
-        assert_all(dr > ts)
+        assert np.all(dr > ts)
         msg = "Cannot compare tz-naive and tz-aware"
         with pytest.raises(TypeError, match=msg):
             op(dr, ts_tz)
 
-        assert_all(dz > ts_tz)
+        assert np.all(dz > ts_tz)
         with pytest.raises(TypeError, match=msg):
             op(dz, ts)
 

pandas/tests/arrays/test_numpy.py

@@ -211,3 +211,18 @@ def test_basic_binop():
     result = x + x
     expected = PandasArray(np.array([2, 4, 6]))
     tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", [None, object])
+def test_setitem_object_typecode(dtype):
+    arr = PandasArray(np.array(["a", "b", "c"], dtype=dtype))
+    arr[0] = "t"
+    expected = PandasArray(np.array(["t", "b", "c"], dtype=dtype))
+    tm.assert_extension_array_equal(arr, expected)
+
+
+def test_setitem_no_coercion():
+    # https://github.com/pandas-dev/pandas/issues/28150
+    arr = PandasArray(np.array([1, 2, 3]))
+    with pytest.raises(ValueError, match="int"):
+        arr[0] = "a"

pandas/tests/dtypes/test_missing.py

@@ -360,6 +360,20 @@ def test_array_equivalent_str():
         )
 
 
+def test_array_equivalent_nested():
+    # reached in groupby aggregations, make sure we use np.any when checking
+    #  if the comparison is truthy
+    left = np.array([np.array([50, 70, 90]), np.array([20, 30, 40])], dtype=object)
+    right = np.array([np.array([50, 70, 90]), np.array([20, 30, 40])], dtype=object)
+
+    assert array_equivalent(left, right, strict_nan=True)
+    assert not array_equivalent(left, right[::-1], strict_nan=True)
+
+    left = np.array([np.array([50, 50, 50]), np.array([40, 40, 40])], dtype=object)
+    right = np.array([50, 40])
+    assert not array_equivalent(left, right, strict_nan=True)
+
+
 @pytest.mark.parametrize(
     "dtype, na_value",
     [

pandas/tests/frame/test_indexing.py

@@ -2160,23 +2160,6 @@ def test_iat(self, float_frame):
                 expected = float_frame.at[row, col]
                 assert result == expected
 
-    def test_nested_exception(self):
-        # Ignore the strange way of triggering the problem
-        # (which may get fixed), it's just a way to trigger
-        # the issue or reraising an outer exception without
-        # a named argument
-        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}).set_index(
-            ["a", "b"]
-        )
-        index = list(df.index)
-        index[0] = ["a", "b"]
-        df.index = index
-
-        try:
-            repr(df)
-        except Exception as e:
-            assert type(e) != UnboundLocalError
-
     @pytest.mark.parametrize(
         "method,expected_values",
         [

pandas/tests/indexes/interval/test_astype.py

@@ -143,7 +143,7 @@ def test_subtype_integer(self, subtype):
         tm.assert_index_equal(result, expected)
 
         # raises with NA
-        msg = "Cannot convert NA to integer"
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
         with pytest.raises(ValueError, match=msg):
             index.insert(0, np.nan).astype(dtype)
 

pandas/tests/indexes/test_numeric.py

@@ -242,10 +242,17 @@ def test_astype(self):
         # GH 13149
         for dtype in ["int16", "int32", "int64"]:
             i = Float64Index([0, 1.1, np.NAN])
-            msg = "Cannot convert NA to integer"
+            msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
             with pytest.raises(ValueError, match=msg):
                 i.astype(dtype)
 
+    def test_cannot_cast_inf_to_int(self):
+        idx = pd.Float64Index([1, 2, np.inf])
+
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(ValueError, match=msg):
+            idx.astype(int)
+
     def test_type_coercion_fail(self, any_int_dtype):
         # see gh-15832
         msg = "Trying to coerce float values to integers"

pandas/tests/indexing/common.py

@@ -210,22 +210,23 @@ def _print(result, error=None):
             try:
                 rs = getattr(obj, method1).__getitem__(_axify(obj, k1, a))
 
-                try:
-                    xp = self.get_result(obj, method2, k2, a)
-                except Exception:
-                    result = "no comp"
-                    _print(result)
-                    return
+                with catch_warnings(record=True):
+                    filterwarnings("ignore", "\\n.ix", FutureWarning)
+                    try:
+                        xp = self.get_result(obj, method2, k2, a)
+                    except (KeyError, IndexError):
+                        # TODO: why is this allowed?
+                        result = "no comp"
+                        _print(result)
+                        return
 
                 detail = None
 
                 try:
                     if is_scalar(rs) and is_scalar(xp):
                         assert rs == xp
-                    elif xp.ndim == 1:
-                        tm.assert_series_equal(rs, xp)
-                    elif xp.ndim == 2:
-                        tm.assert_frame_equal(rs, xp)
+                    else:
+                        tm.assert_equal(rs, xp)
                     result = "ok"
                 except AssertionError as e:
                     detail = str(e)
@@ -242,7 +243,7 @@ def _print(result, error=None):
 
             except AssertionError:
                 raise
-            except Exception as detail:
+            except (IndexError, TypeError, KeyError) as detail:
 
                 # if we are in fails, the ok, otherwise raise it
                 if fails is not None:

pandas/tests/io/json/conftest.py

@@ -0,0 +1,9 @@
+import pytest
+
+
+@pytest.fixture(params=["split", "records", "index", "columns", "values"])
+def orient(request):
+    """
+    Fixture for orients excluding the table format.
+    """
+    return request.param