CoW: Switch to copy=False everywhere for Series constructor (#52068)

Author: phofl

pandas/core/algorithms.py

@@ -838,7 +838,7 @@ def value_counts(
     if bins is not None:
         from pandas.core.reshape.tile import cut
 
-        values = Series(values)
+        values = Series(values, copy=False)
         try:
             ii = cut(values, bins, include_lowest=True)
         except TypeError as err:
@@ -861,7 +861,7 @@ def value_counts(
     else:
         if is_extension_array_dtype(values):
             # handle Categorical and sparse,
-            result = Series(values)._values.value_counts(dropna=dropna)
+            result = Series(values, copy=False)._values.value_counts(dropna=dropna)
             result.name = name
             result.index.name = index_name
             counts = result._values
@@ -893,7 +893,7 @@ def value_counts(
                 idx = idx.astype(object)
             idx.name = index_name
 
-            result = Series(counts, index=idx, name=name)
+            result = Series(counts, index=idx, name=name, copy=False)
 
     if sort:
         result = result.sort_values(ascending=ascending)

pandas/core/arrays/_mixins.py

@@ -445,7 +445,7 @@ def value_counts(self, dropna: bool = True) -> Series:
 
         index_arr = self._from_backing_data(np.asarray(result.index._data))
         index = Index(index_arr, name=result.index.name)
-        return Series(result._values, index=index, name=result.name)
+        return Series(result._values, index=index, name=result.name, copy=False)
 
     def _quantile(
         self,

pandas/core/arrays/arrow/array.py

@@ -1124,7 +1124,7 @@ def value_counts(self, dropna: bool = True) -> Series:
 
         index = Index(type(self)(values))
 
-        return Series(counts, index=index, name="count")
+        return Series(counts, index=index, name="count", copy=False)
 
     @classmethod
     def _concat_same_type(cls, to_concat) -> Self:

pandas/core/arrays/categorical.py

@@ -1535,7 +1535,9 @@ def value_counts(self, dropna: bool = True) -> Series:
         ix = coerce_indexer_dtype(ix, self.dtype.categories)
         ix = self._from_backing_data(ix)
 
-        return Series(count, index=CategoricalIndex(ix), dtype="int64", name="count")
+        return Series(
+            count, index=CategoricalIndex(ix), dtype="int64", name="count", copy=False
+        )
 
     # error: Argument 2 of "_empty" is incompatible with supertype
     # "NDArrayBackedExtensionArray"; supertype defines the argument type as
@@ -1793,7 +1795,9 @@ def _values_for_rank(self):
             #  reorder the categories (so rank can use the float codes)
             #  instead of passing an object array to rank
             values = np.array(
-                self.rename_categories(Series(self.categories).rank().values)
+                self.rename_categories(
+                    Series(self.categories, copy=False).rank().values
+                )
             )
         return values
 

pandas/core/arrays/masked.py

@@ -995,7 +995,7 @@ def value_counts(self, dropna: bool = True) -> Series:
         )
 
         if dropna:
-            res = Series(value_counts, index=keys, name="count")
+            res = Series(value_counts, index=keys, name="count", copy=False)
             res.index = res.index.astype(self.dtype)
             res = res.astype("Int64")
             return res
@@ -1011,7 +1011,7 @@ def value_counts(self, dropna: bool = True) -> Series:
         mask = np.zeros(len(counts), dtype="bool")
         counts_array = IntegerArray(counts, mask)
 
-        return Series(counts_array, index=index, name="count")
+        return Series(counts_array, index=index, name="count", copy=False)
 
     @doc(ExtensionArray.equals)
     def equals(self, other) -> bool:

pandas/core/arrays/sparse/accessor.py

@@ -219,6 +219,7 @@ def to_dense(self) -> Series:
             self._parent.array.to_dense(),
             index=self._parent.index,
             name=self._parent.name,
+            copy=False,
         )
 
 

pandas/core/arrays/sparse/array.py

@@ -886,7 +886,7 @@ def value_counts(self, dropna: bool = True) -> Series:
             index = Index(keys)
         else:
             index = keys
-        return Series(counts, index=index)
+        return Series(counts, index=index, copy=False)
 
     # --------
     # Indexing

pandas/core/arrays/sparse/scipy_sparse.py

@@ -195,7 +195,7 @@ def coo_to_sparse_series(
     from pandas import SparseDtype
 
     try:
-        ser = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
+        ser = Series(A.data, MultiIndex.from_arrays((A.row, A.col)), copy=False)
     except AttributeError as err:
         raise TypeError(
             f"Expected coo_matrix. Got {type(A).__name__} instead."

pandas/core/groupby/generic.py

@@ -720,7 +720,7 @@ def value_counts(
             llab = lambda lab, inc: lab[inc]
         else:
             # lab is a Categorical with categories an IntervalIndex
-            cat_ser = cut(Series(val), bins, include_lowest=True)
+            cat_ser = cut(Series(val, copy=False), bins, include_lowest=True)
             cat_obj = cast("Categorical", cat_ser._values)
             lev = cat_obj.categories
             lab = lev.take(

pandas/core/groupby/groupby.py

@@ -1570,7 +1570,7 @@ def _agg_py_fallback(
 
         if values.ndim == 1:
             # For DataFrameGroupBy we only get here with ExtensionArray
-            ser = Series(values)
+            ser = Series(values, copy=False)
         else:
             # We only get here with values.dtype == object
             # TODO: special case not needed with ArrayManager