Merge remote-tracking branch 'upstream/master' into combine-exception

Author: TomAugspurger

Diff for: ci/travis-27.yaml

@@ -44,7 +44,7 @@ dependencies:
   # universal
   - pytest
   - pytest-xdist
-  - moto
+  - moto==1.3.4
   - hypothesis>=3.58.0
   - pip:
     - backports.lzma

Diff for: doc/source/whatsnew/v0.24.0.txt

@@ -532,6 +532,35 @@ Current Behavior:
     ...
     OverflowError: Trying to coerce negative values to unsigned integers
 
+.. _whatsnew_0240.api.crosstab_dtypes
+
+Crosstab Preserves Dtypes
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:func:`crosstab` will preserve now dtypes in some cases that previously would
+cast from integer dtype to floating dtype (:issue:`22019`)
+
+Previous Behavior:
+
+.. code-block:: ipython
+
+    In [3]: df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],
+       ...:                    'c': [1, 1, np.nan, 1, 1]})
+    In [4]: pd.crosstab(df.a, df.b, normalize='columns')
+    Out[4]:
+    b    3    4
+    a
+    1  0.5  0.0
+    2  0.5  1.0
+
+Current Behavior:
+
+.. code-block:: ipython
+
+    In [3]: df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4],
+       ...:                    'c': [1, 1, np.nan, 1, 1]})
+    In [4]: pd.crosstab(df.a, df.b, normalize='columns')
+
 Datetimelike API Changes
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -666,7 +695,7 @@ Timedelta
 - Bug in :class:`Index` with numeric dtype when multiplying or dividing an array with dtype ``timedelta64`` (:issue:`22390`)
 - Bug in :class:`TimedeltaIndex` incorrectly allowing indexing with ``Timestamp`` object (:issue:`20464`)
 - Fixed bug where subtracting :class:`Timedelta` from an object-dtyped array would raise ``TypeError`` (:issue:`21980`)
--
+- Fixed bug in adding a :class:`DataFrame` with all-`timedelta64[ns]` dtypes to a :class:`DataFrame` with all-integer dtypes returning incorrect results instead of raising ``TypeError`` (:issue:`22696`)
 -
 
 Timezones
@@ -834,4 +863,3 @@ Other
 - :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
 - :meth:`~pandas.io.formats.style.Styler.bar` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` and setting clipping range with ``vmin`` and ``vmax`` (:issue:`21548` and :issue:`21526`). ``NaN`` values are also handled properly.
 - Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
--

Diff for: pandas/core/arrays/interval.py

@@ -108,12 +108,7 @@ class IntervalArray(IntervalMixin, ExtensionArray):
     _na_value = _fill_value = np.nan
 
     def __new__(cls, data, closed=None, dtype=None, copy=False,
-                fastpath=False, verify_integrity=True):
-
-        if fastpath:
-            return cls._simple_new(data.left, data.right, closed,
-                                   copy=copy, dtype=dtype,
-                                   verify_integrity=False)
+                verify_integrity=True):
 
         if isinstance(data, ABCSeries) and is_interval_dtype(data):
             data = data.values

Diff for: pandas/core/arrays/period.py

@@ -264,7 +264,7 @@ def asfreq(self, freq=None, how='E'):
         if self.hasnans:
             new_data[self._isnan] = iNaT
 
-        return self._simple_new(new_data, self.name, freq=freq)
+        return self._shallow_copy(new_data, freq=freq)
 
     # ------------------------------------------------------------------
     # Arithmetic Methods

Diff for: pandas/core/computation/pytables.py

@@ -411,7 +411,7 @@ def visit_Subscript(self, node, **kwargs):
         slobj = self.visit(node.slice)
         try:
             value = value.value
-        except:
+        except AttributeError:
             pass
 
         try:

Diff for: pandas/core/dtypes/common.py

@@ -467,7 +467,7 @@ def is_timedelta64_dtype(arr_or_dtype):
         return False
     try:
         tipo = _get_dtype_type(arr_or_dtype)
-    except:
+    except (TypeError, ValueError, SyntaxError):
         return False
     return issubclass(tipo, np.timedelta64)
 

Diff for: pandas/core/dtypes/dtypes.py

@@ -358,11 +358,11 @@ def construct_from_string(cls, string):
         try:
             if string == 'category':
                 return cls()
-        except:
+            else:
+                raise TypeError("cannot construct a CategoricalDtype")
+        except AttributeError:
             pass
 
-        raise TypeError("cannot construct a CategoricalDtype")
-
     @staticmethod
     def validate_ordered(ordered):
         """
@@ -519,7 +519,7 @@ def __new__(cls, unit=None, tz=None):
                 if m is not None:
                     unit = m.groupdict()['unit']
                     tz = m.groupdict()['tz']
-            except:
+            except TypeError:
                 raise ValueError("could not construct DatetimeTZDtype")
 
         elif isinstance(unit, compat.string_types):

Diff for: pandas/core/frame.py

@@ -3260,7 +3260,7 @@ def _ensure_valid_index(self, value):
         if not len(self.index) and is_list_like(value):
             try:
                 value = Series(value)
-            except:
+            except (ValueError, NotImplementedError, TypeError):
                 raise ValueError('Cannot set a frame with no defined index '
                                  'and a value that cannot be converted to a '
                                  'Series')
@@ -3629,7 +3629,8 @@ def align(self, other, join='outer', axis=None, level=None, copy=True,
                                             fill_axis=fill_axis,
                                             broadcast_axis=broadcast_axis)
 
-    @Appender(_shared_docs['reindex'] % _shared_doc_kwargs)
+    @Substitution(**_shared_doc_kwargs)
+    @Appender(NDFrame.reindex.__doc__)
     @rewrite_axis_style_signature('labels', [('method', None),
                                              ('copy', True),
                                              ('level', None),
@@ -4479,7 +4480,8 @@ def f(vals):
     # ----------------------------------------------------------------------
     # Sorting
 
-    @Appender(_shared_docs['sort_values'] % _shared_doc_kwargs)
+    @Substitution(**_shared_doc_kwargs)
+    @Appender(NDFrame.sort_values.__doc__)
     def sort_values(self, by, axis=0, ascending=True, inplace=False,
                     kind='quicksort', na_position='last'):
         inplace = validate_bool_kwarg(inplace, 'inplace')
@@ -4521,7 +4523,8 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False,
         else:
             return self._constructor(new_data).__finalize__(self)
 
-    @Appender(_shared_docs['sort_index'] % _shared_doc_kwargs)
+    @Substitution(**_shared_doc_kwargs)
+    @Appender(NDFrame.sort_index.__doc__)
     def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
                    kind='quicksort', na_position='last', sort_remaining=True,
                    by=None):
@@ -4886,7 +4889,7 @@ def _arith_op(left, right):
             left, right = ops.fill_binop(left, right, fill_value)
             return func(left, right)
 
-        if this._is_mixed_type or other._is_mixed_type:
+        if ops.should_series_dispatch(this, other, func):
             # iterate over columns
             return ops.dispatch_to_series(this, other, _arith_op)
         else:
@@ -4896,7 +4899,6 @@ def _arith_op(left, right):
                                      copy=False)
 
     def _combine_match_index(self, other, func, level=None):
-        assert isinstance(other, Series)
         left, right = self.align(other, join='outer', axis=0, level=level,
                                  copy=False)
         assert left.index.equals(right.index)
@@ -4916,11 +4918,7 @@ def _combine_match_columns(self, other, func, level=None, try_cast=True):
         left, right = self.align(other, join='outer', axis=1, level=level,
                                  copy=False)
         assert left.columns.equals(right.index)
-
-        new_data = left._data.eval(func=func, other=right,
-                                   axes=[left.columns, self.index],
-                                   try_cast=try_cast)
-        return self._constructor(new_data)
+        return ops.dispatch_to_series(left, right, func, axis="columns")
 
     def _combine_const(self, other, func, errors='raise', try_cast=True):
         if lib.is_scalar(other) or np.ndim(other) == 0:
@@ -7747,7 +7745,7 @@ def convert(v):
                 values = np.array([convert(v) for v in values])
             else:
                 values = convert(values)
-        except:
+        except (ValueError, TypeError):
             values = convert(values)
 
     else: