Merge https://github.com/pandas-dev/pandas

Author: OliEfr

pandas/core/algorithms.py

@@ -1661,6 +1661,40 @@ def take(arr, indices, axis: int = 0, allow_fill: bool = False, fill_value=None)
     return result
 
 
+def _take_preprocess_indexer_and_fill_value(
+    arr, indexer, axis, out, fill_value, allow_fill
+):
+    mask_info = None
+
+    if indexer is None:
+        indexer = np.arange(arr.shape[axis], dtype=np.int64)
+        dtype, fill_value = arr.dtype, arr.dtype.type()
+    else:
+        indexer = ensure_int64(indexer, copy=False)
+        if not allow_fill:
+            dtype, fill_value = arr.dtype, arr.dtype.type()
+            mask_info = None, False
+        else:
+            # check for promotion based on types only (do this first because
+            # it's faster than computing a mask)
+            dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+            if dtype != arr.dtype and (out is None or out.dtype != dtype):
+                # check if promotion is actually required based on indexer
+                mask = indexer == -1
+                needs_masking = mask.any()
+                mask_info = mask, needs_masking
+                if needs_masking:
+                    if out is not None and out.dtype != dtype:
+                        raise TypeError("Incompatible type for fill_value")
+                else:
+                    # if not, then depromote, set fill_value to dummy
+                    # (it won't be used but we don't want the cython code
+                    # to crash when trying to cast it to dtype)
+                    dtype, fill_value = arr.dtype, arr.dtype.type()
+
+    return indexer, dtype, fill_value, mask_info
+
+
 def take_nd(
     arr,
     indexer,
@@ -1700,8 +1734,6 @@ def take_nd(
     subarray : array-like
         May be the same type as the input, or cast to an ndarray.
     """
-    mask_info = None
-
     if fill_value is lib.no_default:
         fill_value = na_value_for_dtype(arr.dtype, compat=False)
 
@@ -1712,31 +1744,9 @@ def take_nd(
     arr = extract_array(arr)
     arr = np.asarray(arr)
 
-    if indexer is None:
-        indexer = np.arange(arr.shape[axis], dtype=np.int64)
-        dtype, fill_value = arr.dtype, arr.dtype.type()
-    else:
-        indexer = ensure_int64(indexer, copy=False)
-        if not allow_fill:
-            dtype, fill_value = arr.dtype, arr.dtype.type()
-            mask_info = None, False
-        else:
-            # check for promotion based on types only (do this first because
-            # it's faster than computing a mask)
-            dtype, fill_value = maybe_promote(arr.dtype, fill_value)
-            if dtype != arr.dtype and (out is None or out.dtype != dtype):
-                # check if promotion is actually required based on indexer
-                mask = indexer == -1
-                needs_masking = mask.any()
-                mask_info = mask, needs_masking
-                if needs_masking:
-                    if out is not None and out.dtype != dtype:
-                        raise TypeError("Incompatible type for fill_value")
-                else:
-                    # if not, then depromote, set fill_value to dummy
-                    # (it won't be used but we don't want the cython code
-                    # to crash when trying to cast it to dtype)
-                    dtype, fill_value = arr.dtype, arr.dtype.type()
+    indexer, dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value(
+        arr, indexer, axis, out, fill_value, allow_fill
+    )
 
     flip_order = False
     if arr.ndim == 2 and arr.flags.f_contiguous:

pandas/core/dtypes/cast.py

@@ -475,7 +475,8 @@ def maybe_upcast_putmask(result: np.ndarray, mask: np.ndarray) -> np.ndarray:
         # upcast (possibly), otherwise we DON't want to upcast (e.g. if we
         # have values, say integers, in the success portion then it's ok to not
         # upcast)
-        new_dtype, _ = maybe_promote(result.dtype, np.nan)
+        new_dtype = ensure_dtype_can_hold_na(result.dtype)
+
         if new_dtype != result.dtype:
             result = result.astype(new_dtype, copy=True)
 
@@ -484,7 +485,21 @@ def maybe_upcast_putmask(result: np.ndarray, mask: np.ndarray) -> np.ndarray:
     return result
 
 
-def maybe_promote(dtype, fill_value=np.nan):
+def ensure_dtype_can_hold_na(dtype: DtypeObj) -> DtypeObj:
+    """
+    If we have a dtype that cannot hold NA values, find the best match that can.
+    """
+    if isinstance(dtype, ExtensionDtype):
+        # TODO: ExtensionDtype.can_hold_na?
+        return dtype
+    elif dtype.kind == "b":
+        return np.dtype(object)
+    elif dtype.kind in ["i", "u"]:
+        return np.dtype(np.float64)
+    return dtype
+
+
+def maybe_promote(dtype: DtypeObj, fill_value=np.nan):
     """
     Find the minimal dtype that can hold both the given dtype and fill_value.
 
@@ -565,7 +580,7 @@ def maybe_promote(dtype, fill_value=np.nan):
                     fill_value = np.timedelta64("NaT", "ns")
                 else:
                     fill_value = fv.to_timedelta64()
-    elif is_datetime64tz_dtype(dtype):
+    elif isinstance(dtype, DatetimeTZDtype):
         if isna(fill_value):
             fill_value = NaT
         elif not isinstance(fill_value, datetime):

pandas/core/frame.py

@@ -4821,7 +4821,10 @@ def set_index(
                 names.extend(col.names)
             elif isinstance(col, (Index, Series)):
                 # if Index then not MultiIndex (treated above)
-                arrays.append(col)
+
+                # error: Argument 1 to "append" of "list" has incompatible
+                #  type "Union[Index, Series]"; expected "Index"  [arg-type]
+                arrays.append(col)  # type:ignore[arg-type]
                 names.append(col.name)
             elif isinstance(col, (list, np.ndarray)):
                 arrays.append(col)

pandas/core/indexes/base.py

@@ -5766,7 +5766,7 @@ def insert(self, loc: int, item):
         idx = np.concatenate((arr[:loc], item, arr[loc:]))
         return Index(idx, name=self.name)
 
-    def drop(self, labels, errors: str_t = "raise"):
+    def drop(self: _IndexT, labels, errors: str_t = "raise") -> _IndexT:
         """
         Make new Index with passed list of labels deleted.