Merge remote-tracking branch 'upstream/master' into CI-unify-boilerplate

Author: MomIsBestFriend

doc/source/user_guide/text.rst

@@ -87,8 +87,9 @@ l. For ``StringDtype``, :ref:`string accessor methods<api.series.str>`
 
    .. ipython:: python
 
-      s.astype(object).str.count("a")
-      s.astype(object).dropna().str.count("a")
+      s2 = pd.Series(["a", None, "b"], dtype="object")
+      s2.str.count("a")
+      s2.dropna().str.count("a")
 
    When NA values are present, the output dtype is float64. Similarly for
    methods returning boolean values.

doc/source/whatsnew/v1.0.0.rst

@@ -1004,7 +1004,7 @@ Reshaping
 Sparse
 ^^^^^^
 - Bug in :class:`SparseDataFrame` arithmetic operations incorrectly casting inputs to float (:issue:`28107`)
--
+- Bug in ``DataFrame.sparse`` returning a ``Series`` when there was a column named ``sparse`` rather than the accessor (:issue:`30758`)
 -
 
 ExtensionArray

pandas/_testing.py

@@ -1287,7 +1287,7 @@ def assert_frame_equal(
     >>> assert_frame_equal(df1, df2)
     Traceback (most recent call last):
     ...
-    AssertionError: Attributes of DataFrame.iloc[:, 1] are different
+    AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different
 
     Attribute "dtype" are different
     [left]:  int64
@@ -1366,7 +1366,7 @@ def assert_frame_equal(
                 check_names=check_names,
                 check_datetimelike_compat=check_datetimelike_compat,
                 check_categorical=check_categorical,
-                obj=f"{obj}.iloc[:, {i}]",
+                obj=f'{obj}.iloc[:, {i}] (column name="{col}")',
             )
 
 

pandas/core/arrays/datetimelike.py

@@ -11,6 +11,7 @@
 from pandas._libs.tslibs.timedeltas import Timedelta, delta_to_nanoseconds
 from pandas._libs.tslibs.timestamps import RoundTo, round_nsint64
 from pandas._typing import DatetimeLikeScalar
+from pandas.compat import set_function_name
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError, NullFrequencyError, PerformanceWarning
 from pandas.util._decorators import Appender, Substitution
@@ -37,19 +38,94 @@
 from pandas.core.dtypes.inference import is_array_like
 from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna
 
-from pandas.core import missing, nanops
+from pandas.core import missing, nanops, ops
 from pandas.core.algorithms import checked_add_with_arr, take, unique1d, value_counts
 import pandas.core.common as com
 from pandas.core.indexers import check_bool_array_indexer
 from pandas.core.ops.common import unpack_zerodim_and_defer
-from pandas.core.ops.invalid import make_invalid_op
+from pandas.core.ops.invalid import invalid_comparison, make_invalid_op
 
 from pandas.tseries import frequencies
 from pandas.tseries.offsets import DateOffset, Tick
 
 from .base import ExtensionArray, ExtensionOpsMixin
 
 
+def _datetimelike_array_cmp(cls, op):
+    """
+    Wrap comparison operations to convert Timestamp/Timedelta/Period-like to
+    boxed scalars/arrays.
+    """
+    opname = f"__{op.__name__}__"
+    nat_result = opname == "__ne__"
+
+    @unpack_zerodim_and_defer(opname)
+    def wrapper(self, other):
+
+        if isinstance(other, str):
+            try:
+                # GH#18435 strings get a pass from tzawareness compat
+                other = self._scalar_from_string(other)
+            except ValueError:
+                # failed to parse as Timestamp/Timedelta/Period
+                return invalid_comparison(self, other, op)
+
+        if isinstance(other, self._recognized_scalars) or other is NaT:
+            other = self._scalar_type(other)
+            self._check_compatible_with(other)
+
+            other_i8 = self._unbox_scalar(other)
+
+            result = op(self.view("i8"), other_i8)
+            if isna(other):
+                result.fill(nat_result)
+
+        elif not is_list_like(other):
+            return invalid_comparison(self, other, op)
+
+        elif len(other) != len(self):
+            raise ValueError("Lengths must match")
+
+        else:
+            if isinstance(other, list):
+                # TODO: could use pd.Index to do inference?
+                other = np.array(other)
+
+            if not isinstance(other, (np.ndarray, type(self))):
+                return invalid_comparison(self, other, op)
+
+            if is_object_dtype(other):
+                # We have to use comp_method_OBJECT_ARRAY instead of numpy
+                #  comparison otherwise it would fail to raise when
+                #  comparing tz-aware and tz-naive
+                with np.errstate(all="ignore"):
+                    result = ops.comp_method_OBJECT_ARRAY(
+                        op, self.astype(object), other
+                    )
+                o_mask = isna(other)
+
+            elif not type(self)._is_recognized_dtype(other.dtype):
+                return invalid_comparison(self, other, op)
+
+            else:
+                # For PeriodDType this casting is unnecessary
+                other = type(self)._from_sequence(other)
+                self._check_compatible_with(other)
+
+                result = op(self.view("i8"), other.view("i8"))
+                o_mask = other._isnan
+
+            if o_mask.any():
+                result[o_mask] = nat_result
+
+        if self._hasnans:
+            result[self._isnan] = nat_result
+
+        return result
+
+    return set_function_name(wrapper, opname, cls)
+
+
 class AttributesMixin:
     _data: np.ndarray
 
@@ -934,6 +1010,7 @@ def _is_unique(self):
 
     # ------------------------------------------------------------------
     # Arithmetic Methods
+    _create_comparison_method = classmethod(_datetimelike_array_cmp)
 
     # pow is invalid for all three subclasses; TimedeltaArray will override
     #  the multiplication and division ops
@@ -1485,6 +1562,8 @@ def mean(self, skipna=True):
         return self._box_func(result)
 
 
+DatetimeLikeArrayMixin._add_comparison_ops()
+
 # -------------------------------------------------------------------
 # Shared Constructor Helpers
 

pandas/core/arrays/datetimes.py

@@ -18,7 +18,6 @@
     timezones,
     tzconversion,
 )
-import pandas.compat as compat
 from pandas.errors import PerformanceWarning
 
 from pandas.core.dtypes.common import (
@@ -32,7 +31,6 @@
     is_dtype_equal,
     is_extension_array_dtype,
     is_float_dtype,
-    is_list_like,
     is_object_dtype,
     is_period_dtype,
     is_string_dtype,
@@ -43,13 +41,10 @@
 from pandas.core.dtypes.generic import ABCIndexClass, ABCPandasArray, ABCSeries
 from pandas.core.dtypes.missing import isna
 
-from pandas.core import ops
 from pandas.core.algorithms import checked_add_with_arr
 from pandas.core.arrays import datetimelike as dtl
 from pandas.core.arrays._ranges import generate_regular_range
 import pandas.core.common as com
-from pandas.core.ops.common import unpack_zerodim_and_defer
-from pandas.core.ops.invalid import invalid_comparison
 
 from pandas.tseries.frequencies import get_period_alias, to_offset
 from pandas.tseries.offsets import Day, Tick
@@ -131,81 +126,6 @@ def f(self):
     return property(f)
 
 
-def _dt_array_cmp(cls, op):
-    """
-    Wrap comparison operations to convert datetime-like to datetime64
-    """
-    opname = f"__{op.__name__}__"
-    nat_result = opname == "__ne__"
-
-    @unpack_zerodim_and_defer(opname)
-    def wrapper(self, other):
-
-        if isinstance(other, str):
-            try:
-                # GH#18435 strings get a pass from tzawareness compat
-                other = self._scalar_from_string(other)
-            except ValueError:
-                # string that cannot be parsed to Timestamp
-                return invalid_comparison(self, other, op)
-
-        if isinstance(other, self._recognized_scalars) or other is NaT:
-            other = self._scalar_type(other)
-            self._assert_tzawareness_compat(other)
-
-            other_i8 = other.value
-
-            result = op(self.view("i8"), other_i8)
-            if isna(other):
-                result.fill(nat_result)
-
-        elif not is_list_like(other):
-            return invalid_comparison(self, other, op)
-
-        elif len(other) != len(self):
-            raise ValueError("Lengths must match")
-
-        else:
-            if isinstance(other, list):
-                other = np.array(other)
-
-            if not isinstance(other, (np.ndarray, cls)):
-                # Following Timestamp convention, __eq__ is all-False
-                # and __ne__ is all True, others raise TypeError.
-                return invalid_comparison(self, other, op)
-
-            if is_object_dtype(other):
-                # We have to use comp_method_OBJECT_ARRAY instead of numpy
-                #  comparison otherwise it would fail to raise when
-                #  comparing tz-aware and tz-naive
-                with np.errstate(all="ignore"):
-                    result = ops.comp_method_OBJECT_ARRAY(
-                        op, self.astype(object), other
-                    )
-                o_mask = isna(other)
-
-            elif not cls._is_recognized_dtype(other.dtype):
-                # e.g. is_timedelta64_dtype(other)
-                return invalid_comparison(self, other, op)
-
-            else:
-                self._assert_tzawareness_compat(other)
-                other = type(self)._from_sequence(other)
-
-                result = op(self.view("i8"), other.view("i8"))
-                o_mask = other._isnan
-
-            if o_mask.any():
-                result[o_mask] = nat_result
-
-        if self._hasnans:
-            result[self._isnan] = nat_result
-
-        return result
-
-    return compat.set_function_name(wrapper, opname, cls)
-
-
 class DatetimeArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps, dtl.DatelikeOps):
     """
     Pandas ExtensionArray for tz-naive or tz-aware datetime data.
@@ -324,7 +244,7 @@ def __init__(self, values, dtype=_NS_DTYPE, freq=None, copy=False):
                     raise TypeError(msg)
             elif values.tz:
                 dtype = values.dtype
-            # freq = validate_values_freq(values, freq)
+
             if freq is None:
                 freq = values.freq
             values = values._data
@@ -714,8 +634,6 @@ def _format_native_types(self, na_rep="NaT", date_format=None, **kwargs):
     # -----------------------------------------------------------------
     # Comparison Methods
 
-    _create_comparison_method = classmethod(_dt_array_cmp)
-
     def _has_same_tz(self, other):
         zzone = self._timezone
 
@@ -1767,9 +1685,6 @@ def to_julian_date(self):
         )
 
 
-DatetimeArray._add_comparison_ops()
-
-
 # -------------------------------------------------------------------
 # Constructor Helpers