update stacklevel for doc decorator

Author: HH-MWB

ci/code_checks.sh

@@ -150,7 +150,13 @@ if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
     # Check for imports from pandas._testing instead of `import pandas._testing as tm`
     invgrep -R --include="*.py*" -E "from pandas._testing import" pandas/tests
     RET=$(($RET + $?)) ; echo $MSG "DONE"
-    invgrep -R --include="*.py*" -E "from pandas.util import testing as tm" pandas/tests
+    invgrep -R --include="*.py*" -E "from pandas import _testing as tm" pandas/tests
+    RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+    # No direct imports from conftest
+    invgrep -R --include="*.py*" -E "conftest import" pandas/tests
+    RET=$(($RET + $?)) ; echo $MSG "DONE"
+    invgrep -R --include="*.py*" -E "import conftest" pandas/tests
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
     MSG='Check for use of exec' ; echo $MSG

doc/source/getting_started/index.rst

@@ -398,7 +398,7 @@ data set, a sliding window of the data or grouped by categories. The latter is a
                 <div class="card-body">
 
 Change the structure of your data table in multiple ways. You can :func:`~pandas.melt` your data table from wide to long/tidy form or :func:`~pandas.pivot`
-from long to wide format. With aggregations built-in, a pivot table is created with a sinlge command.
+from long to wide format. With aggregations built-in, a pivot table is created with a single command.
 
 .. image:: ../_static/schemas/07_melt.svg
    :align: center

doc/source/user_guide/cookbook.rst

@@ -1333,33 +1333,6 @@ Values can be set to NaT using np.nan, similar to datetime
    y[1] = np.nan
    y
 
-Aliasing axis names
--------------------
-
-To globally provide aliases for axis names, one can define these 2 functions:
-
-.. ipython:: python
-
-   def set_axis_alias(cls, axis, alias):
-       if axis not in cls._AXIS_NUMBERS:
-           raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
-       cls._AXIS_ALIASES[alias] = axis
-
-.. ipython:: python
-
-   def clear_axis_alias(cls, axis, alias):
-       if axis not in cls._AXIS_NUMBERS:
-           raise Exception("invalid axis [%s] for alias [%s]" % (axis, alias))
-       cls._AXIS_ALIASES.pop(alias, None)
-
-.. ipython:: python
-
-   set_axis_alias(pd.DataFrame, 'columns', 'myaxis2')
-   df2 = pd.DataFrame(np.random.randn(3, 2), columns=['c1', 'c2'],
-                      index=['i1', 'i2', 'i3'])
-   df2.sum(axis='myaxis2')
-   clear_axis_alias(pd.DataFrame, 'columns', 'myaxis2')
-
 Creating example data
 ---------------------
 

doc/source/whatsnew/v1.1.0.rst

@@ -175,14 +175,16 @@ Other API changes
 - :meth:`Groupby.groups` now returns an abbreviated representation when called on large dataframes (:issue:`1135`)
 - ``loc`` lookups with an object-dtype :class:`Index` and an integer key will now raise ``KeyError`` instead of ``TypeError`` when key is missing (:issue:`31905`)
 - Using a :func:`pandas.api.indexers.BaseIndexer` with ``skew``, ``cov``, ``corr`` will now raise a ``NotImplementedError`` (:issue:`32865`)
-- Using a :func:`pandas.api.indexers.BaseIndexer` with ``count``, ``min``, ``max`` will now return correct results for any monotonic :func:`pandas.api.indexers.BaseIndexer` descendant (:issue:`32865`)
+- Using a :func:`pandas.api.indexers.BaseIndexer` with ``count``, ``min``, ``max``, ``median`` will now return correct results for any monotonic :func:`pandas.api.indexers.BaseIndexer` descendant (:issue:`32865`)
 - Added a :func:`pandas.api.indexers.FixedForwardWindowIndexer` class to support forward-looking windows during ``rolling`` operations.
 -
 
 Backwards incompatible API changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 - :meth:`DataFrame.swaplevels` now raises a  ``TypeError`` if the axis is not a :class:`MultiIndex`.
-  Previously a ``AttributeError`` was raised (:issue:`31126`)
+  Previously an ``AttributeError`` was raised (:issue:`31126`)
+- :meth:`DataFrame.xs` now raises a  ``TypeError`` if a ``level`` keyword is supplied and the axis is not a :class:`MultiIndex`.
+  Previously an ``AttributeError`` was raised (:issue:`33610`)
 - :meth:`DataFrameGroupby.mean` and :meth:`SeriesGroupby.mean` (and similarly for :meth:`~DataFrameGroupby.median`, :meth:`~DataFrameGroupby.std` and :meth:`~DataFrameGroupby.var`)
   now raise a  ``TypeError`` if a not-accepted keyword argument is passed into it.
   Previously a ``UnsupportedFunctionCall`` was raised (``AssertionError`` if ``min_count`` passed into :meth:`~DataFrameGroupby.median`) (:issue:`31485`)
@@ -527,6 +529,7 @@ Indexing
 - Bug in :meth:`DataFrame.iloc` when slicing a single column-:class:`DataFrame`` with ``ExtensionDtype`` (e.g. ``df.iloc[:, :1]``) returning an invalid result (:issue:`32957`)
 - Bug in :meth:`DatetimeIndex.insert` and :meth:`TimedeltaIndex.insert` causing index ``freq`` to be lost when setting an element into an empty :class:`Series` (:issue:33573`)
 - Bug in :meth:`Series.__setitem__` with an :class:`IntervalIndex` and a list-like key of integers (:issue:`33473`)
+- Bug in :meth:`Series.__getitem__` allowing missing labels with ``np.ndarray``, :class:`Index`, :class:`Series` indexers but not ``list``, these now all raise ``KeyError`` (:issue:`33646`)
 
 Missing
 ^^^^^^^
@@ -580,6 +583,8 @@ I/O
 - Bug in :func:`pandas.io.json.json_normalize` where location specified by `record_path` doesn't point to an array. (:issue:`26284`)
 - :func:`pandas.read_hdf` has a more explicit error message when loading an
   unsupported HDF file (:issue:`9539`)
+- Bug in :meth:`~DataFrame.to_parquet` was not raising ``PermissionError`` when writing to a private s3 bucket with invalid creds. (:issue:`27679`)
+- Bug in :meth:`~DataFrame.to_csv` was silently failing when writing to an invalid s3 bucket. (:issue:`32486`)
 
 Plotting
 ^^^^^^^^

pandas/_libs/internals.pyx

@@ -141,10 +141,10 @@ cdef class BlockPlacement:
 
         return BlockPlacement(val)
 
-    def delete(self, loc) -> "BlockPlacement":
+    def delete(self, loc) -> BlockPlacement:
         return BlockPlacement(np.delete(self.as_array, loc, axis=0))
 
-    def append(self, others) -> "BlockPlacement":
+    def append(self, others) -> BlockPlacement:
         if not len(others):
             return self
 
@@ -185,7 +185,7 @@ cdef class BlockPlacement:
             val = newarr
             return BlockPlacement(val)
 
-    def add(self, other) -> "BlockPlacement":
+    def add(self, other) -> BlockPlacement:
         # We can get here with int or ndarray
         return self.iadd(other)
 

pandas/_libs/window/aggregations.pyx

@@ -843,7 +843,8 @@ def roll_kurt_variable(ndarray[float64_t] values, ndarray[int64_t] start,
 
 
 def roll_median_c(ndarray[float64_t] values, ndarray[int64_t] start,
-                  ndarray[int64_t] end, int64_t minp, int64_t win):
+                  ndarray[int64_t] end, int64_t minp, int64_t win=0):
+    # GH 32865. win argument kept for compatibility
     cdef:
         float64_t val, res, prev
         bint err = False
@@ -858,7 +859,7 @@ def roll_median_c(ndarray[float64_t] values, ndarray[int64_t] start,
     # actual skiplist ops outweigh any window computation costs
     output = np.empty(N, dtype=float)
 
-    if win == 0 or (end - start).max() == 0:
+    if (end - start).max() == 0:
         output[:] = NaN
         return output
     win = (end - start).max()

pandas/_testing.py

@@ -22,7 +22,7 @@
 )
 
 import pandas._libs.testing as _testing
-from pandas._typing import FilePathOrBuffer, FrameOrSeries
+from pandas._typing import Dtype, FilePathOrBuffer, FrameOrSeries
 from pandas.compat import _get_lzma_file, _import_lzma
 
 from pandas.core.dtypes.common import (
@@ -73,6 +73,37 @@
 _K = 4
 _RAISE_NETWORK_ERROR_DEFAULT = False
 
+UNSIGNED_INT_DTYPES: List[Dtype] = ["uint8", "uint16", "uint32", "uint64"]
+UNSIGNED_EA_INT_DTYPES: List[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
+SIGNED_INT_DTYPES: List[Dtype] = [int, "int8", "int16", "int32", "int64"]
+SIGNED_EA_INT_DTYPES: List[Dtype] = ["Int8", "Int16", "Int32", "Int64"]
+ALL_INT_DTYPES = UNSIGNED_INT_DTYPES + SIGNED_INT_DTYPES
+ALL_EA_INT_DTYPES = UNSIGNED_EA_INT_DTYPES + SIGNED_EA_INT_DTYPES
+
+FLOAT_DTYPES: List[Dtype] = [float, "float32", "float64"]
+COMPLEX_DTYPES: List[Dtype] = [complex, "complex64", "complex128"]
+STRING_DTYPES: List[Dtype] = [str, "str", "U"]
+
+DATETIME64_DTYPES: List[Dtype] = ["datetime64[ns]", "M8[ns]"]
+TIMEDELTA64_DTYPES: List[Dtype] = ["timedelta64[ns]", "m8[ns]"]
+
+BOOL_DTYPES = [bool, "bool"]
+BYTES_DTYPES = [bytes, "bytes"]
+OBJECT_DTYPES = [object, "object"]
+
+ALL_REAL_DTYPES = FLOAT_DTYPES + ALL_INT_DTYPES
+ALL_NUMPY_DTYPES = (
+    ALL_REAL_DTYPES
+    + COMPLEX_DTYPES
+    + STRING_DTYPES
+    + DATETIME64_DTYPES
+    + TIMEDELTA64_DTYPES
+    + BOOL_DTYPES
+    + OBJECT_DTYPES
+    + BYTES_DTYPES
+)
+
+
 # set testing_mode
 _testing_mode_warnings = (DeprecationWarning, ResourceWarning)
 

pandas/conftest.py

@@ -23,7 +23,6 @@
 from decimal import Decimal
 import operator
 import os
-from typing import List
 
 from dateutil.tz import tzlocal, tzutc
 import hypothesis
@@ -32,7 +31,6 @@
 import pytest
 from pytz import FixedOffset, utc
 
-from pandas._typing import Dtype
 import pandas.util._test_decorators as td
 
 import pandas as pd
@@ -864,39 +862,7 @@ def utc_fixture(request):
 # ----------------------------------------------------------------
 # Dtypes
 # ----------------------------------------------------------------
-
-UNSIGNED_INT_DTYPES = ["uint8", "uint16", "uint32", "uint64"]
-UNSIGNED_EA_INT_DTYPES = ["UInt8", "UInt16", "UInt32", "UInt64"]
-SIGNED_INT_DTYPES: List[Dtype] = [int, "int8", "int16", "int32", "int64"]
-SIGNED_EA_INT_DTYPES = ["Int8", "Int16", "Int32", "Int64"]
-ALL_INT_DTYPES = UNSIGNED_INT_DTYPES + SIGNED_INT_DTYPES
-ALL_EA_INT_DTYPES = UNSIGNED_EA_INT_DTYPES + SIGNED_EA_INT_DTYPES
-
-FLOAT_DTYPES: List[Dtype] = [float, "float32", "float64"]
-COMPLEX_DTYPES: List[Dtype] = [complex, "complex64", "complex128"]
-STRING_DTYPES: List[Dtype] = [str, "str", "U"]
-
-DATETIME64_DTYPES = ["datetime64[ns]", "M8[ns]"]
-TIMEDELTA64_DTYPES = ["timedelta64[ns]", "m8[ns]"]
-
-BOOL_DTYPES = [bool, "bool"]
-BYTES_DTYPES = [bytes, "bytes"]
-OBJECT_DTYPES = [object, "object"]
-
-ALL_REAL_DTYPES = FLOAT_DTYPES + ALL_INT_DTYPES
-ALL_NUMPY_DTYPES = (
-    ALL_REAL_DTYPES
-    + COMPLEX_DTYPES
-    + STRING_DTYPES
-    + DATETIME64_DTYPES
-    + TIMEDELTA64_DTYPES
-    + BOOL_DTYPES
-    + OBJECT_DTYPES
-    + BYTES_DTYPES
-)
-
-
-@pytest.fixture(params=STRING_DTYPES)
+@pytest.fixture(params=tm.STRING_DTYPES)
 def string_dtype(request):
     """
     Parametrized fixture for string dtypes.
@@ -908,7 +874,7 @@ def string_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=BYTES_DTYPES)
+@pytest.fixture(params=tm.BYTES_DTYPES)
 def bytes_dtype(request):
     """
     Parametrized fixture for bytes dtypes.
@@ -919,7 +885,7 @@ def bytes_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=OBJECT_DTYPES)
+@pytest.fixture(params=tm.OBJECT_DTYPES)
 def object_dtype(request):
     """
     Parametrized fixture for object dtypes.
@@ -930,7 +896,7 @@ def object_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=DATETIME64_DTYPES)
+@pytest.fixture(params=tm.DATETIME64_DTYPES)
 def datetime64_dtype(request):
     """
     Parametrized fixture for datetime64 dtypes.
@@ -941,7 +907,7 @@ def datetime64_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=TIMEDELTA64_DTYPES)
+@pytest.fixture(params=tm.TIMEDELTA64_DTYPES)
 def timedelta64_dtype(request):
     """
     Parametrized fixture for timedelta64 dtypes.
@@ -952,7 +918,7 @@ def timedelta64_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=FLOAT_DTYPES)
+@pytest.fixture(params=tm.FLOAT_DTYPES)
 def float_dtype(request):
     """
     Parameterized fixture for float dtypes.
@@ -964,7 +930,7 @@ def float_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=COMPLEX_DTYPES)
+@pytest.fixture(params=tm.COMPLEX_DTYPES)
 def complex_dtype(request):
     """
     Parameterized fixture for complex dtypes.
@@ -976,7 +942,7 @@ def complex_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=SIGNED_INT_DTYPES)
+@pytest.fixture(params=tm.SIGNED_INT_DTYPES)
 def sint_dtype(request):
     """
     Parameterized fixture for signed integer dtypes.
@@ -990,7 +956,7 @@ def sint_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=UNSIGNED_INT_DTYPES)
+@pytest.fixture(params=tm.UNSIGNED_INT_DTYPES)
 def uint_dtype(request):
     """
     Parameterized fixture for unsigned integer dtypes.
@@ -1003,7 +969,7 @@ def uint_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=ALL_INT_DTYPES)
+@pytest.fixture(params=tm.ALL_INT_DTYPES)
 def any_int_dtype(request):
     """
     Parameterized fixture for any integer dtype.
@@ -1021,7 +987,7 @@ def any_int_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=ALL_EA_INT_DTYPES)
+@pytest.fixture(params=tm.ALL_EA_INT_DTYPES)
 def any_nullable_int_dtype(request):
     """
     Parameterized fixture for any nullable integer dtype.
@@ -1038,7 +1004,7 @@ def any_nullable_int_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=ALL_REAL_DTYPES)
+@pytest.fixture(params=tm.ALL_REAL_DTYPES)
 def any_real_dtype(request):
     """
     Parameterized fixture for any (purely) real numeric dtype.
@@ -1059,7 +1025,7 @@ def any_real_dtype(request):
     return request.param
 
 
-@pytest.fixture(params=ALL_NUMPY_DTYPES)
+@pytest.fixture(params=tm.ALL_NUMPY_DTYPES)
 def any_numpy_dtype(request):
     """
     Parameterized fixture for all numpy dtypes.

pandas/core/array_algos/transforms.py

@@ -10,6 +10,10 @@
 def shift(values: np.ndarray, periods: int, axis: int, fill_value) -> np.ndarray:
     new_values = values
 
+    if periods == 0:
+        # TODO: should we copy here?
+        return new_values
+
     # make sure array sent to np.roll is c_contiguous
     f_ordered = values.flags.f_contiguous
     if f_ordered: