TYP: core.reshape

pandas/core/reshape/merge.py

@@ -1004,7 +1004,7 @@ def _maybe_add_join_keys(
                     result_dtype = find_common_type([lvals.dtype, rvals.dtype])
 
                 if result._is_label_reference(name):
-                    result[name] = Series(
+                    result[name] = result._constructor_sliced(
                         key_col, dtype=result_dtype, index=result.index
                     )
                 elif result._is_level_reference(name):

pandas/core/reshape/pivot.py

@@ -263,7 +263,7 @@ def _add_margins(
     rows,
     cols,
     aggfunc,
-    observed=None,
+    observed: bool,
     margins_name: Hashable = "All",
     fill_value=None,
 ):
@@ -292,7 +292,7 @@ def _add_margins(
     if not values and isinstance(table, ABCSeries):
         # If there are no values and the table is a series, then there is only
         # one column in the data. Compute grand margin and return it.
-        return table._append(Series({key: grand_margin[margins_name]}))
+        return table._append(table._constructor({key: grand_margin[margins_name]}))
 
     elif values:
         marginal_result_set = _generate_marginal_results(
@@ -364,8 +364,16 @@ def _compute_grand_margin(
 
 
 def _generate_marginal_results(
-    table, data, values, rows, cols, aggfunc, observed, margins_name: Hashable = "All"
+    table,
+    data: DataFrame,
+    values,
+    rows,
+    cols,
+    aggfunc,
+    observed: bool,
+    margins_name: Hashable = "All",
 ):
+    margin_keys: list | Index
     if len(cols) > 0:
         # need to "interleave" the margins
         table_pieces = []
@@ -433,23 +441,24 @@ def _all_key(key):
         new_order = [len(cols)] + list(range(len(cols)))
         row_margin.index = row_margin.index.reorder_levels(new_order)
     else:
-        row_margin = Series(np.nan, index=result.columns)
+        row_margin = data._constructor_sliced(np.nan, index=result.columns)
 
     return result, margin_keys, row_margin
 
 
 def _generate_marginal_results_without_values(
     table: DataFrame,
-    data,
+    data: DataFrame,
     rows,
     cols,
     aggfunc,
-    observed,
+    observed: bool,
     margins_name: Hashable = "All",
 ):
+    margin_keys: list | Index
     if len(cols) > 0:
         # need to "interleave" the margins
-        margin_keys: list | Index = []
+        margin_keys = []
 
         def _all_key():
             if len(cols) == 1:
@@ -535,7 +544,9 @@ def pivot(
                     data.index.get_level_values(i) for i in range(data.index.nlevels)
                 ]
             else:
-                index_list = [Series(data.index, name=data.index.name)]
+                index_list = [
+                    data._constructor_sliced(data.index, name=data.index.name)
+                ]
         else:
             index_list = [data[idx] for idx in com.convert_to_list_like(index)]
 

pandas/core/reshape/reshape.py

@@ -46,7 +46,10 @@
 )
 
 if TYPE_CHECKING:
-    from pandas._typing import npt
+    from pandas._typing import (
+        Level,
+        npt,
+    )
 
     from pandas.core.arrays import ExtensionArray
     from pandas.core.indexes.frozen import FrozenList
@@ -98,9 +101,7 @@ class _Unstacker:
     unstacked : DataFrame
     """
 
-    def __init__(self, index: MultiIndex, level=-1, constructor=None) -> None:
-        if constructor is None:
-            constructor = DataFrame
+    def __init__(self, index: MultiIndex, level: Level, constructor) -> None:
         self.constructor = constructor
 
         self.index = index.remove_unused_levels()
@@ -374,13 +375,14 @@ def new_index(self) -> MultiIndex:
         )
 
 
-def _unstack_multiple(data, clocs, fill_value=None):
+def _unstack_multiple(data: Series | DataFrame, clocs, fill_value=None):
     if len(clocs) == 0:
         return data
 
     # NOTE: This doesn't deal with hierarchical columns yet
 
     index = data.index
+    index = cast(MultiIndex, index)  # caller is responsible for checking
 
     # GH 19966 Make sure if MultiIndexed index has tuple name, they will be
     # recognised as a whole
@@ -433,10 +435,10 @@ def _unstack_multiple(data, clocs, fill_value=None):
             return result
 
         # GH#42579 deep=False to avoid consolidating
-        dummy = data.copy(deep=False)
-        dummy.index = dummy_index
+        dummy_df = data.copy(deep=False)
+        dummy_df.index = dummy_index
 
-        unstacked = dummy.unstack("__placeholder__", fill_value=fill_value)
+        unstacked = dummy_df.unstack("__placeholder__", fill_value=fill_value)
         if isinstance(unstacked, Series):
             unstcols = unstacked.index
         else:
@@ -497,7 +499,7 @@ def unstack(obj: Series | DataFrame, level, fill_value=None):
         )
 
 
-def _unstack_frame(obj: DataFrame, level, fill_value=None):
+def _unstack_frame(obj: DataFrame, level, fill_value=None) -> DataFrame:
     assert isinstance(obj.index, MultiIndex)  # checked by caller
     unstacker = _Unstacker(obj.index, level=level, constructor=obj._constructor)
 
@@ -617,7 +619,7 @@ def factorize(index):
     return frame._constructor_sliced(new_values, index=new_index)
 
 
-def stack_multiple(frame, level, dropna: bool = True):
+def stack_multiple(frame: DataFrame, level, dropna: bool = True):
     # If all passed levels match up to column names, no
     # ambiguity about what to do
     if all(lev in frame.columns.names for lev in level):

pandas/core/reshape/tile.py

@@ -32,7 +32,10 @@
     is_scalar,
     is_timedelta64_dtype,
 )
-from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.dtypes import (
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
 from pandas.core.dtypes.generic import ABCSeries
 from pandas.core.dtypes.missing import isna
 
@@ -47,7 +50,10 @@
 import pandas.core.algorithms as algos
 
 if TYPE_CHECKING:
-    from pandas._typing import IntervalLeftRight
+    from pandas._typing import (
+        DtypeObj,
+        IntervalLeftRight,
+    )
 
 
 def cut(
@@ -399,7 +405,7 @@ def _bins_to_cuts(
     labels=None,
     precision: int = 3,
     include_lowest: bool = False,
-    dtype=None,
+    dtype: DtypeObj | None = None,
     duplicates: str = "raise",
     ordered: bool = True,
 ):
@@ -481,7 +487,7 @@ def _coerce_to_type(x):
     this method converts it to numeric so that cut or qcut method can
     handle it
     """
-    dtype = None
+    dtype: DtypeObj | None = None
 
     if is_datetime64tz_dtype(x.dtype):
         dtype = x.dtype
@@ -508,7 +514,7 @@ def _coerce_to_type(x):
     return x, dtype
 
 
-def _convert_bin_to_numeric_type(bins, dtype):
+def _convert_bin_to_numeric_type(bins, dtype: DtypeObj | None):
     """
     if the passed bin is of datetime/timedelta type,
     this method converts it to integer
@@ -542,7 +548,7 @@ def _convert_bin_to_numeric_type(bins, dtype):
     return bins
 
 
-def _convert_bin_to_datelike_type(bins, dtype):
+def _convert_bin_to_datelike_type(bins, dtype: DtypeObj | None):
     """
     Convert bins to a DatetimeIndex or TimedeltaIndex if the original dtype is
     datelike
@@ -557,22 +563,26 @@ def _convert_bin_to_datelike_type(bins, dtype):
     bins : Array-like of bins, DatetimeIndex or TimedeltaIndex if dtype is
            datelike
     """
-    if is_datetime64tz_dtype(dtype):
+    if isinstance(dtype, DatetimeTZDtype):
         bins = to_datetime(bins.astype(np.int64), utc=True).tz_convert(dtype.tz)
     elif is_datetime_or_timedelta_dtype(dtype):
         bins = Index(bins.astype(np.int64), dtype=dtype)
     return bins
 
 
 def _format_labels(
-    bins, precision: int, right: bool = True, include_lowest: bool = False, dtype=None
+    bins,
+    precision: int,
+    right: bool = True,
+    include_lowest: bool = False,
+    dtype: DtypeObj | None = None,
 ):
     """based on the dtype, return our labels"""
     closed: IntervalLeftRight = "right" if right else "left"
 
     formatter: Callable[[Any], Timestamp] | Callable[[Any], Timedelta]
 
-    if is_datetime64tz_dtype(dtype):
+    if isinstance(dtype, DatetimeTZDtype):
         formatter = lambda x: Timestamp(x, tz=dtype.tz)
         adjust = lambda x: x - Timedelta("1ns")
     elif is_datetime64_dtype(dtype):