Merge branch 'master' into 16737-unexpected-results-creating-an-empty-series

Author: SaturnFromTitan

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/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

@@ -175,7 +175,7 @@ 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.
 -
 
@@ -584,6 +584,8 @@ 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/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/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:

pandas/core/arrays/categorical.py

@@ -27,7 +27,6 @@
 from pandas.core.dtypes.common import (
     ensure_int64,
     ensure_object,
-    ensure_platform_int,
     is_categorical_dtype,
     is_datetime64_dtype,
     is_dict_like,
@@ -51,6 +50,7 @@
 from pandas.core.accessor import PandasDelegate, delegate_names
 import pandas.core.algorithms as algorithms
 from pandas.core.algorithms import _get_data_algo, factorize, take, take_1d, unique1d
+from pandas.core.array_algos.transforms import shift
 from pandas.core.arrays.base import ExtensionArray, _extension_array_shared_docs
 from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs
 import pandas.core.common as com
@@ -199,17 +199,6 @@ def contains(cat, key, container):
         return any(loc_ in container for loc_ in loc)
 
 
-_codes_doc = """
-The category codes of this categorical.
-
-Level codes are an array if integer which are the positions of the real
-values in the categories array.
-
-There is not setter, use the other categorical methods and the normal item
-setter to change values in the categorical.
-"""
-
-
 class Categorical(ExtensionArray, PandasObject):
     """
     Represent a categorical variable in classic R / S-plus fashion.
@@ -652,27 +641,26 @@ def from_codes(cls, codes, categories=None, ordered=None, dtype=None):
 
         return cls(codes, dtype=dtype, fastpath=True)
 
-    def _get_codes(self):
+    @property
+    def codes(self) -> np.ndarray:
         """
-        Get the codes.
+        The category codes of this categorical.
+
+        Codes are an array of integers which are the positions of the actual
+        values in the categories array.
+
+        There is no setter, use the other categorical methods and the normal item
+        setter to change values in the categorical.
 
         Returns
         -------
-        codes : integer array view
-            A non writable view of the `codes` array.
+        ndarray[int]
+            A non-writable view of the `codes` array.
         """
         v = self._codes.view()
         v.flags.writeable = False
         return v
 
-    def _set_codes(self, codes):
-        """
-        Not settable by the user directly
-        """
-        raise ValueError("cannot set Categorical codes directly")
-
-    codes = property(fget=_get_codes, fset=_set_codes, doc=_codes_doc)
-
     def _set_categories(self, categories, fastpath=False):
         """
         Sets new categories inplace
@@ -1241,23 +1229,41 @@ def shift(self, periods, fill_value=None):
         codes = self.codes
         if codes.ndim > 1:
             raise NotImplementedError("Categorical with ndim > 1.")
-        if np.prod(codes.shape) and (periods != 0):
-            codes = np.roll(codes, ensure_platform_int(periods), axis=0)
-            if isna(fill_value):
-                fill_value = -1
-            elif fill_value in self.categories:
-                fill_value = self.categories.get_loc(fill_value)
-            else:
-                raise ValueError(
-                    f"'fill_value={fill_value}' is not present "
-                    "in this Categorical's categories"
-                )
-            if periods > 0:
-                codes[:periods] = fill_value
-            else:
-                codes[periods:] = fill_value
 
-        return self.from_codes(codes, dtype=self.dtype)
+        fill_value = self._validate_fill_value(fill_value)
+
+        codes = shift(codes.copy(), periods, axis=0, fill_value=fill_value)
+
+        return self._constructor(codes, dtype=self.dtype, fastpath=True)
+
+    def _validate_fill_value(self, fill_value):
+        """
+        Convert a user-facing fill_value to  a representation to use with our
+        underlying ndarray, raising ValueError if this is not possible.
+
+        Parameters
+        ----------
+        fill_value : object
+
+        Returns
+        -------
+        fill_value : int
+
+        Raises
+        ------
+        ValueError
+        """
+
+        if isna(fill_value):
+            fill_value = -1
+        elif fill_value in self.categories:
+            fill_value = self.categories.get_loc(fill_value)
+        else:
+            raise ValueError(
+                f"'fill_value={fill_value}' is not present "
+                "in this Categorical's categories"
+            )
+        return fill_value
 
     def __array__(self, dtype=None) -> np.ndarray:
         """
@@ -1835,24 +1841,12 @@ def take(self, indexer, allow_fill: bool = False, fill_value=None):
         """
         indexer = np.asarray(indexer, dtype=np.intp)
 
-        dtype = self.dtype
-
-        if isna(fill_value):
-            fill_value = -1
-        elif allow_fill:
+        if allow_fill:
             # convert user-provided `fill_value` to codes
-            if fill_value in self.categories:
-                fill_value = self.categories.get_loc(fill_value)
-            else:
-                msg = (
-                    f"'fill_value' ('{fill_value}') is not in this "
-                    "Categorical's categories."
-                )
-                raise TypeError(msg)
+            fill_value = self._validate_fill_value(fill_value)
 
         codes = take(self._codes, indexer, allow_fill=allow_fill, fill_value=fill_value)
-        result = type(self).from_codes(codes, dtype=dtype)
-        return result
+        return self._constructor(codes, dtype=self.dtype, fastpath=True)
 
     def take_nd(self, indexer, allow_fill: bool = False, fill_value=None):
         # GH#27745 deprecate alias that other EAs dont have