Merge remote-tracking branch 'upstream/master'

Author: Matt Roeschke

doc/redirects.csv

@@ -1577,3 +1577,6 @@ generated/pandas.unique,../reference/api/pandas.unique
 generated/pandas.util.hash_array,../reference/api/pandas.util.hash_array
 generated/pandas.util.hash_pandas_object,../reference/api/pandas.util.hash_pandas_object
 generated/pandas.wide_to_long,../reference/api/pandas.wide_to_long
+
+# Cached searches
+reference/api/pandas.DataFrame.from_csv,pandas.read_csv

doc/source/whatsnew/v1.0.0.rst

@@ -125,9 +125,10 @@ Deprecations
 Removed SparseSeries and SparseDataFrame
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-``SparseSeries`` and ``SparseDataFrame`` have been removed (:issue:`28425`).
-We recommend using a ``Series`` or ``DataFrame`` with sparse values instead.
-See :ref:`sparse.migration` for help with migrating existing code.
+``SparseSeries``, ``SparseDataFrame`` and the ``DataFrame.to_sparse`` method
+have been removed (:issue:`28425`). We recommend using a ``Series`` or
+``DataFrame`` with sparse values instead. See :ref:`sparse.migration` for help
+with migrating existing code.
 
 Removal of prior version deprecations/changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -199,7 +200,7 @@ Timezones
 Numeric
 ^^^^^^^
 - Bug in :meth:`DataFrame.quantile` with zero-column :class:`DataFrame` incorrectly raising (:issue:`23925`)
--
+- :class:`DataFrame` inequality comparisons with object-dtype and ``complex`` entries failing to raise ``TypeError`` like their :class:`Series` counterparts (:issue:`28079`)
 -
 
 Conversion

pandas/__init__.py

@@ -114,7 +114,7 @@
     DataFrame,
 )
 
-from pandas.core.sparse.api import SparseArray, SparseDtype
+from pandas.core.arrays.sparse import SparseArray, SparseDtype
 
 from pandas.tseries.api import infer_freq
 from pandas.tseries import offsets

pandas/_libs/tslibs/conversion.pyx

@@ -519,7 +519,7 @@ cdef _TSObject convert_str_to_tsobject(object ts, object tz, object unit,
             try:
                 ts = parse_datetime_string(ts, dayfirst=dayfirst,
                                            yearfirst=yearfirst)
-            except Exception:
+            except (ValueError, OverflowError):
                 raise ValueError("could not convert string to Timestamp")
 
     return convert_to_tsobject(ts, tz, unit, dayfirst, yearfirst)

pandas/_libs/tslibs/nattype.pyx

@@ -551,7 +551,7 @@ class NaTType(_NaT):
         """)
     round = _make_nat_func('round',  # noqa:E128
         """
-        Round the Timestamp to the specified resolution
+        Round the Timestamp to the specified resolution.
 
         Parameters
         ----------
@@ -589,7 +589,7 @@ default 'raise'
         """)
     floor = _make_nat_func('floor',  # noqa:E128
         """
-        return a new Timestamp floored to this resolution
+        return a new Timestamp floored to this resolution.
 
         Parameters
         ----------
@@ -623,7 +623,7 @@ default 'raise'
         """)
     ceil = _make_nat_func('ceil',  # noqa:E128
         """
-        return a new Timestamp ceiled to this resolution
+        return a new Timestamp ceiled to this resolution.
 
         Parameters
         ----------
@@ -735,7 +735,7 @@ default 'raise'
         """)
     replace = _make_nat_func('replace',  # noqa:E128
         """
-        implements datetime.replace, handles nanoseconds
+        implements datetime.replace, handles nanoseconds.
 
         Parameters
         ----------

pandas/_libs/tslibs/parsing.pyx

@@ -309,9 +309,9 @@ cdef parse_datetime_string_with_reso(date_string, freq=None, dayfirst=False,
         parsed, reso = dateutil_parse(date_string, _DEFAULT_DATETIME,
                                       dayfirst=dayfirst, yearfirst=yearfirst,
                                       ignoretz=False, tzinfos=None)
-    except Exception as e:
+    except (ValueError, OverflowError) as err:
         # TODO: allow raise of errors within instead
-        raise DateParseError(e)
+        raise DateParseError(err)
     if parsed is None:
         raise DateParseError("Could not parse {dstr}".format(dstr=date_string))
     return parsed, parsed, reso

pandas/_libs/tslibs/timedeltas.pyx

@@ -1155,7 +1155,7 @@ cdef class _Timedelta(timedelta):
         """
         Format Timedelta as ISO 8601 Duration like
         ``P[n]Y[n]M[n]DT[n]H[n]M[n]S``, where the ``[n]`` s are replaced by the
-        values. See https://en.wikipedia.org/wiki/ISO_8601#Durations
+        values. See https://en.wikipedia.org/wiki/ISO_8601#Durations.
 
         .. versionadded:: 0.20.0
 
@@ -1319,7 +1319,7 @@ class Timedelta(_Timedelta):
 
     def round(self, freq):
         """
-        Round the Timedelta to the specified resolution
+        Round the Timedelta to the specified resolution.
 
         Parameters
         ----------
@@ -1337,7 +1337,7 @@ class Timedelta(_Timedelta):
 
     def floor(self, freq):
         """
-        return a new Timedelta floored to this resolution
+        return a new Timedelta floored to this resolution.
 
         Parameters
         ----------
@@ -1347,7 +1347,7 @@ class Timedelta(_Timedelta):
 
     def ceil(self, freq):
         """
-        return a new Timedelta ceiled to this resolution
+        return a new Timedelta ceiled to this resolution.
 
         Parameters
         ----------

pandas/_libs/tslibs/timestamps.pyx

@@ -441,7 +441,7 @@ class Timestamp(_Timestamp):
 
     def round(self, freq, ambiguous='raise', nonexistent='raise'):
         """
-        Round the Timestamp to the specified resolution
+        Round the Timestamp to the specified resolution.
 
         Parameters
         ----------
@@ -483,7 +483,7 @@ default 'raise'
 
     def floor(self, freq, ambiguous='raise', nonexistent='raise'):
         """
-        return a new Timestamp floored to this resolution
+        return a new Timestamp floored to this resolution.
 
         Parameters
         ----------
@@ -519,7 +519,7 @@ default 'raise'
 
     def ceil(self, freq, ambiguous='raise', nonexistent='raise'):
         """
-        return a new Timestamp ceiled to this resolution
+        return a new Timestamp ceiled to this resolution.
 
         Parameters
         ----------
@@ -556,7 +556,7 @@ default 'raise'
     @property
     def tz(self):
         """
-        Alias for tzinfo
+        Alias for tzinfo.
         """
         return self.tzinfo
 
@@ -754,7 +754,7 @@ default 'raise'
     def resolution(self):
         """
         Return resolution describing the smallest difference between two
-        times that can be represented by Timestamp object_state
+        times that can be represented by Timestamp object_state.
         """
         # GH#21336, GH#21365
         return Timedelta(nanoseconds=1)
@@ -893,7 +893,7 @@ default 'raise'
                 hour=None, minute=None, second=None, microsecond=None,
                 nanosecond=None, tzinfo=object, fold=0):
         """
-        implements datetime.replace, handles nanoseconds
+        implements datetime.replace, handles nanoseconds.
 
         Parameters
         ----------

pandas/core/apply.py

@@ -342,7 +342,7 @@ def wrap_results(self):
         results = self.results
 
         # see if we can infer the results
-        if len(results) > 0 and is_sequence(results[0]):
+        if len(results) > 0 and 0 in results and is_sequence(results[0]):
 
             return self.wrap_results_for_axis()
 

pandas/core/arrays/datetimes.py

@@ -1918,6 +1918,9 @@ def sequence_to_dt64ns(
     tz = validate_tz_from_dtype(dtype, tz)
 
     if isinstance(data, ABCIndexClass):
+        if data.nlevels > 1:
+            # Without this check, data._data below is None
+            raise TypeError("Cannot create a DatetimeArray from a MultiIndex.")
         data = data._data
 
     # By this point we are assured to have either a numpy array or Index