Merge remote-tracking branch 'upstream/master' into test_pytables_refactor

Author: tolaa001

pandas/core/dtypes/cast.py

@@ -350,14 +350,21 @@ def maybe_promote(dtype, fill_value=np.nan):
 
     # returns tuple of (dtype, fill_value)
     if issubclass(dtype.type, np.datetime64):
-        fill_value = tslibs.Timestamp(fill_value).to_datetime64()
+        try:
+            fill_value = tslibs.Timestamp(fill_value).to_datetime64()
+        except (TypeError, ValueError):
+            dtype = np.dtype(np.object_)
     elif issubclass(dtype.type, np.timedelta64):
-        fv = tslibs.Timedelta(fill_value)
-        if fv is NaT:
-            # NaT has no `to_timedelta6` method
-            fill_value = np.timedelta64("NaT", "ns")
+        try:
+            fv = tslibs.Timedelta(fill_value)
+        except ValueError:
+            dtype = np.dtype(np.object_)
         else:
-            fill_value = fv.to_timedelta64()
+            if fv is NaT:
+                # NaT has no `to_timedelta64` method
+                fill_value = np.timedelta64("NaT", "ns")
+            else:
+                fill_value = fv.to_timedelta64()
     elif is_datetime64tz_dtype(dtype):
         if isna(fill_value):
             fill_value = NaT

pandas/core/frame.py

@@ -5279,24 +5279,17 @@ def _arith_op(left, right):
             new_data = dispatch_fill_zeros(func, this.values, other.values, res_values)
         return this._construct_result(new_data)
 
-    def _combine_match_index(self, other, func, level=None):
-        left, right = self.align(other, join="outer", axis=0, level=level, copy=False)
-        # at this point we have `left.index.equals(right.index)`
+    def _combine_match_index(self, other, func):
+        # at this point we have `self.index.equals(other.index)`
 
-        if left._is_mixed_type or right._is_mixed_type:
+        if self._is_mixed_type or other._is_mixed_type:
             # operate column-wise; avoid costly object-casting in `.values`
-            new_data = ops.dispatch_to_series(left, right, func)
+            new_data = ops.dispatch_to_series(self, other, func)
         else:
             # fastpath --> operate directly on values
             with np.errstate(all="ignore"):
-                new_data = func(left.values.T, right.values).T
-        return left._construct_result(new_data)
-
-    def _combine_match_columns(self, other: Series, func, level=None):
-        left, right = self.align(other, join="outer", axis=1, level=level, copy=False)
-        # at this point we have `left.columns.equals(right.index)`
-        new_data = ops.dispatch_to_series(left, right, func, axis="columns")
-        return left._construct_result(new_data)
+                new_data = func(self.values.T, other.values).T
+        return new_data
 
     def _construct_result(self, result) -> "DataFrame":
         """

pandas/core/indexes/timedeltas.py

@@ -717,17 +717,17 @@ def timedelta_range(
 
     Parameters
     ----------
-    start : string or timedelta-like, default None
+    start : str or timedelta-like, default None
         Left bound for generating timedeltas
-    end : string or timedelta-like, default None
+    end : str or timedelta-like, default None
         Right bound for generating timedeltas
-    periods : integer, default None
+    periods : int, default None
         Number of periods to generate
-    freq : string or DateOffset, default 'D'
+    freq : str or DateOffset, default 'D'
         Frequency strings can have multiples, e.g. '5H'
-    name : string, default None
+    name : str, default None
         Name of the resulting TimedeltaIndex
-    closed : string, default None
+    closed : str, default None
         Make the interval closed with respect to the given frequency to
         the 'left', 'right', or both sides (None)
 

pandas/core/ops/__init__.py

@@ -384,7 +384,7 @@ def column_op(a, b):
             return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
 
     elif isinstance(right, ABCSeries) and axis == "columns":
-        # We only get here if called via left._combine_match_columns,
+        # We only get here if called via _combine_frame_series,
         # in which case we specifically want to operate row-by-row
         assert right.index.equals(left.columns)
 
@@ -613,15 +613,18 @@ def _combine_series_frame(self, other, func, fill_value=None, axis=None, level=N
             "fill_value {fill} not supported.".format(fill=fill_value)
         )
 
-    if axis is not None:
-        axis = self._get_axis_number(axis)
-        if axis == 0:
-            return self._combine_match_index(other, func, level=level)
-        else:
-            return self._combine_match_columns(other, func, level=level)
+    if axis is None:
+        # default axis is columns
+        axis = 1
+
+    axis = self._get_axis_number(axis)
+    left, right = self.align(other, join="outer", axis=axis, level=level, copy=False)
+    if axis == 0:
+        new_data = left._combine_match_index(right, func)
+    else:
+        new_data = dispatch_to_series(left, right, func, axis="columns")
 
-    # default axis is columns
-    return self._combine_match_columns(other, func, level=level)
+    return left._construct_result(new_data)
 
 
 def _align_method_FRAME(left, right, axis):

pandas/io/formats/style.py

@@ -642,16 +642,16 @@ def apply(self, func, axis=0, subset=None, **kwargs):
             ``func`` should take a Series or DataFrame (depending
             on ``axis``), and return an object with the same shape.
             Must return a DataFrame with identical index and
-            column labels when ``axis=None``
+            column labels when ``axis=None``.
         axis : {0 or 'index', 1 or 'columns', None}, default 0
-            apply to each column (``axis=0`` or ``'index'``), to each row
+            Apply to each column (``axis=0`` or ``'index'``), to each row
             (``axis=1`` or ``'columns'``), or to the entire DataFrame at once
             with ``axis=None``.
         subset : IndexSlice
-            a valid indexer to limit ``data`` to *before* applying the
-            function. Consider using a pandas.IndexSlice
+            A valid indexer to limit ``data`` to *before* applying the
+            function. Consider using a pandas.IndexSlice.
         **kwargs : dict
-            pass along to ``func``
+            Pass along to ``func``.
 
         Returns
         -------
@@ -698,12 +698,12 @@ def applymap(self, func, subset=None, **kwargs):
         Parameters
         ----------
         func : function
-            ``func`` should take a scalar and return a scalar
+            ``func`` should take a scalar and return a scalar.
         subset : IndexSlice
-            a valid indexer to limit ``data`` to *before* applying the
-            function. Consider using a pandas.IndexSlice
+            A valid indexer to limit ``data`` to *before* applying the
+            function. Consider using a pandas.IndexSlice.
         **kwargs : dict
-            pass along to ``func``
+            Pass along to ``func``.
 
         Returns
         -------
@@ -729,16 +729,16 @@ def where(self, cond, value, other=None, subset=None, **kwargs):
         Parameters
         ----------
         cond : callable
-            ``cond`` should take a scalar and return a boolean
+            ``cond`` should take a scalar and return a boolean.
         value : str
-            applied when ``cond`` returns true
+            Applied when ``cond`` returns true.
         other : str
-            applied when ``cond`` returns false
+            Applied when ``cond`` returns false.
         subset : IndexSlice
-            a valid indexer to limit ``data`` to *before* applying the
-            function. Consider using a pandas.IndexSlice
+            A valid indexer to limit ``data`` to *before* applying the
+            function. Consider using a pandas.IndexSlice.
         **kwargs : dict
-            pass along to ``cond``
+            Pass along to ``cond``.
 
         Returns
         -------
@@ -819,7 +819,7 @@ def use(self, styles):
         Parameters
         ----------
         styles : list
-            list of style functions
+            List of style functions.
 
         Returns
         -------
@@ -969,19 +969,19 @@ def background_gradient(
         Parameters
         ----------
         cmap : str or colormap
-            matplotlib colormap
+            Matplotlib colormap.
         low : float
-            compress the range by the low.
+            Compress the range by the low.
         high : float
-            compress the range by the high.
+            Compress the range by the high.
         axis : {0 or 'index', 1 or 'columns', None}, default 0
-            apply to each column (``axis=0`` or ``'index'``), to each row
+            Apply to each column (``axis=0`` or ``'index'``), to each row
             (``axis=1`` or ``'columns'``), or to the entire DataFrame at once
             with ``axis=None``.
         subset : IndexSlice
-            a valid slice for ``data`` to limit the style application to.
+            A valid slice for ``data`` to limit the style application to.
         text_color_threshold : float or int
-            luminance threshold for determining text color. Facilitates text
+            Luminance threshold for determining text color. Facilitates text
             visibility across varying background colors. From 0 to 1.
             0 = all text is dark colored, 1 = all text is light colored.
 
@@ -1084,9 +1084,9 @@ def set_properties(self, subset=None, **kwargs):
         Parameters
         ----------
         subset : IndexSlice
-            a valid slice for ``data`` to limit the style application to
+            A valid slice for ``data`` to limit the style application to.
         **kwargs : dict
-            property: value pairs to be set for each cell
+            A dictionary of property, value pairs to be set for each cell.
 
         Returns
         -------
@@ -1180,7 +1180,7 @@ def bar(
         subset : IndexSlice, optional
             A valid slice for `data` to limit the style application to.
         axis : {0 or 'index', 1 or 'columns', None}, default 0
-            apply to each column (``axis=0`` or ``'index'``), to each row
+            Apply to each column (``axis=0`` or ``'index'``), to each row
             (``axis=1`` or ``'columns'``), or to the entire DataFrame at once
             with ``axis=None``.
         color : str or 2-tuple/list
@@ -1256,10 +1256,10 @@ def highlight_max(self, subset=None, color="yellow", axis=0):
         Parameters
         ----------
         subset : IndexSlice, default None
-            a valid slice for ``data`` to limit the style application to.
+            A valid slice for ``data`` to limit the style application to.
         color : str, default 'yellow'
         axis : {0 or 'index', 1 or 'columns', None}, default 0
-            apply to each column (``axis=0`` or ``'index'``), to each row
+            Apply to each column (``axis=0`` or ``'index'``), to each row
             (``axis=1`` or ``'columns'``), or to the entire DataFrame at once
             with ``axis=None``.
 
@@ -1276,10 +1276,10 @@ def highlight_min(self, subset=None, color="yellow", axis=0):
         Parameters
         ----------
         subset : IndexSlice, default None
-            a valid slice for ``data`` to limit the style application to.
+            A valid slice for ``data`` to limit the style application to.
         color : str, default 'yellow'
         axis : {0 or 'index', 1 or 'columns', None}, default 0
-            apply to each column (``axis=0`` or ``'index'``), to each row
+            Apply to each column (``axis=0`` or ``'index'``), to each row
             (``axis=1`` or ``'columns'``), or to the entire DataFrame at once
             with ``axis=None``.
 
@@ -1328,9 +1328,9 @@ def from_custom_template(cls, searchpath, name):
         Parameters
         ----------
         searchpath : str or list
-            Path or paths of directories containing the templates
+            Path or paths of directories containing the templates.
         name : str
-            Name of your custom template to use for rendering
+            Name of your custom template to use for rendering.
 
         Returns
         -------

pandas/io/json/_table_schema.py

@@ -199,7 +199,7 @@ def build_table_schema(data, index=True, primary_key=None, version=True):
     index : bool, default True
         Whether to include ``data.index`` in the schema.
     primary_key : bool or None, default True
-        column names to designate as the primary key.
+        Column names to designate as the primary key.
         The default `None` will set `'primaryKey'` to the index
         level or levels if the index is unique.
     version : bool, default True

pandas/tests/dtypes/cast/test_promote.py

@@ -272,10 +272,6 @@ def test_maybe_promote_any_with_bool(any_numpy_dtype_reduced, box):
         pytest.xfail("falsely upcasts to object")
     if boxed and dtype not in (str, object) and box_dtype is None:
         pytest.xfail("falsely upcasts to object")
-    if not boxed and dtype.kind == "M":
-        pytest.xfail("raises error")
-    if not boxed and dtype.kind == "m":
-        pytest.xfail("raises error")
 
     # filling anything but bool with bool casts to object
     expected_dtype = np.dtype(object) if dtype != bool else dtype
@@ -348,8 +344,6 @@ def test_maybe_promote_any_with_datetime64(
             or (box_dtype is None and is_datetime64_dtype(type(fill_value)))
         ):
             pytest.xfail("mix of lack of upcasting, resp. wrong missing value")
-        if not boxed and is_timedelta64_dtype(dtype):
-            pytest.xfail("raises error")
 
     # special case for box_dtype
     box_dtype = np.dtype(datetime64_dtype) if box_dtype == "dt_dtype" else box_dtype
@@ -490,9 +484,7 @@ def test_maybe_promote_any_numpy_dtype_with_datetimetz(
     fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture)
     boxed, box_dtype = box  # read from parametrized fixture
 
-    if dtype.kind == "m" and not boxed:
-        pytest.xfail("raises error")
-    elif dtype.kind == "M" and not boxed:
+    if dtype.kind == "M" and not boxed:
         pytest.xfail("Comes back as M8 instead of object")
 
     fill_value = pd.Series([fill_value], dtype=fill_dtype)[0]
@@ -549,8 +541,6 @@ def test_maybe_promote_any_with_timedelta64(
     else:
         if boxed and box_dtype is None and is_timedelta64_dtype(type(fill_value)):
             pytest.xfail("does not upcast correctly")
-        if not boxed and is_datetime64_dtype(dtype):
-            pytest.xfail("raises error")
 
     # special case for box_dtype
     box_dtype = np.dtype(timedelta64_dtype) if box_dtype == "td_dtype" else box_dtype
@@ -622,9 +612,6 @@ def test_maybe_promote_any_with_string(any_numpy_dtype_reduced, string_dtype, bo
     fill_dtype = np.dtype(string_dtype)
     boxed, box_dtype = box  # read from parametrized fixture
 
-    if is_datetime_or_timedelta_dtype(dtype) and box_dtype != object:
-        pytest.xfail("does not upcast or raises")
-
     # create array of given dtype
     fill_value = "abc"
 
@@ -678,9 +665,6 @@ def test_maybe_promote_any_with_object(any_numpy_dtype_reduced, object_dtype, bo
     dtype = np.dtype(any_numpy_dtype_reduced)
     boxed, box_dtype = box  # read from parametrized fixture
 
-    if not boxed and is_datetime_or_timedelta_dtype(dtype):
-        pytest.xfail("raises error")
-
     # create array of object dtype from a scalar value (i.e. passing
     # dtypes.common.is_scalar), which can however not be cast to int/float etc.
     fill_value = pd.DateOffset(1)

pandas/tests/io/excel/conftest.py

@@ -7,6 +7,9 @@
 
 @pytest.fixture
 def frame(float_frame):
+    """
+    Returns the first ten items in fixture "float_frame".
+    """
     return float_frame[:10]
 
 

pandas/tests/io/test_gcs.py

@@ -108,9 +108,7 @@ def mock_get_filepath_or_buffer(*args, **kwargs):
     assert_frame_equal(df1, df2)
 
 
-@pytest.mark.skipif(
-    td.safe_import("gcsfs"), reason="Only check when gcsfs not installed"
-)
+@td.skip_if_installed("gcsfs")
 def test_gcs_not_present_exception():
     with pytest.raises(ImportError) as e:
         read_csv("gs://test/test.csv")

requirements-dev.txt

@@ -17,7 +17,7 @@ numpydoc>=0.9.0
 nbconvert>=5.4.1
 nbsphinx
 pandoc
-dask-core
+dask
 toolz>=0.7.3
 fsspec>=0.5.1
 partd>=0.3.10

scripts/generate_pip_deps_from_conda.py

@@ -48,6 +48,9 @@ def conda_package_to_pip(package):
 
         break
 
+    if package in RENAME:
+        return RENAME[package]
+
     return package