Completed implementation, testing and documentation

Author: mpak123

doc/source/whatsnew/v3.0.0.rst

@@ -191,6 +191,7 @@ Other enhancements
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`DataFrame.pivot_table` and :func:`pivot_table` now allow the passing of keyword arguments to ``aggfunc`` through ``**kwargs`` (:issue:`57884`)
 - :meth:`DataFrame.to_json` now encodes ``Decimal`` as strings instead of floats (:issue:`60698`)
+- :meth:`DataFrame.unstack` and :meth:`Series.unstack` now support a ``no_fill`` parameter that raises a ``ValueError`` if any missing values would need to be filled during the unstack operation, allowing users to enforce data integrity when a complete 1:1 mapping between stacked and unstacked representations is expected (:issue:`XXXXX`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)

pandas/core/frame.py

@@ -10320,7 +10320,11 @@ def explode(
         return result.__finalize__(self, method="explode")
 
     def unstack(
-        self, level: IndexLabel = -1, fill_value=None, sort: bool = True
+        self,
+        level: IndexLabel = -1,
+        fill_value=None,
+        sort: bool = True,
+        no_fill: bool = False,
     ) -> DataFrame | Series:
         """
         Pivot a level of the (necessarily hierarchical) index labels.
@@ -10339,13 +10343,25 @@ def unstack(
             Replace NaN with this value if the unstack produces missing values.
         sort : bool, default True
             Sort the level(s) in the resulting MultiIndex columns.
+        no_fill : bool, default False
+            If True, raise a ValueError if any missing values would need to be filled.
+            This is useful to ensure data integrity when you expect a complete
+            1:1 mapping between stacked and unstacked representations.
+
+            .. versionadded:: 3.0.0
 
         Returns
         -------
         Series or DataFrame
             If index is a MultiIndex: DataFrame with pivoted index labels as new
             inner-most level column labels, else Series.
 
+        Raises
+        ------
+        ValueError
+            If `no_fill` is True and the unstacking operation would require filling
+            missing values.
+
         See Also
         --------
         DataFrame.pivot : Pivot a table based on column values.
@@ -10389,7 +10405,7 @@ def unstack(
         """
         from pandas.core.reshape.reshape import unstack
 
-        result = unstack(self, level, fill_value, sort)
+        result = unstack(self, level, fill_value, sort, no_fill)
 
         return result.__finalize__(self, method="unstack")
 

pandas/core/reshape/reshape.py

@@ -119,10 +119,16 @@ class _Unstacker:
     """
 
     def __init__(
-        self, index: MultiIndex, level: Level, constructor, sort: bool = True
+        self,
+        index: MultiIndex,
+        level: Level,
+        constructor,
+        sort: bool = True,
+        no_fill: bool = False,
     ) -> None:
         self.constructor = constructor
         self.sort = sort
+        self.no_fill = no_fill
 
         self.index = index.remove_unused_levels()
 
@@ -290,6 +296,29 @@ def get_new_values(self, values, fill_value=None):
         mask = self.mask
         mask_all = self.mask_all
 
+        if self.no_fill and not mask_all:
+            missing_positions = np.where(~mask)[0]
+            if len(missing_positions) > 0:
+                first_missing = missing_positions[0]
+                row_idx = first_missing // width
+                col_idx = first_missing % width
+
+                index_label = (
+                    self.new_index[row_idx]
+                    if row_idx < len(self.new_index)
+                    else row_idx
+                )
+                col_label = (
+                    self.removed_level[col_idx]
+                    if col_idx < len(self.removed_level)
+                    else col_idx
+                )
+
+                raise ValueError(
+                    f"Cannot unstack with no_fill=True because filling is required. "
+                    f"Missing value at index {index_label}, column {col_label}."
+                )
+
         # we can simply reshape if we don't have a mask
         if mask_all and len(values):
             # TODO: Under what circumstances can we rely on sorted_values
@@ -457,7 +486,11 @@ def new_index(self) -> MultiIndex | Index:
 
 
 def _unstack_multiple(
-    data: Series | DataFrame, clocs, fill_value=None, sort: bool = True
+    data: Series | DataFrame,
+    clocs,
+    fill_value=None,
+    sort: bool = True,
+    no_fill: bool = False,
 ):
     if len(clocs) == 0:
         return data
@@ -503,7 +536,9 @@ def _unstack_multiple(
         dummy = data.copy(deep=False)
         dummy.index = dummy_index
 
-        unstacked = dummy.unstack("__placeholder__", fill_value=fill_value, sort=sort)
+        unstacked = dummy.unstack(
+            "__placeholder__", fill_value=fill_value, sort=sort, no_fill=no_fill
+        )
         new_levels = clevels
         new_names = cnames
         new_codes = recons_codes
@@ -515,7 +550,7 @@ def _unstack_multiple(
                 # error: Incompatible types in assignment (expression has type
                 # "DataFrame | Series", variable has type "DataFrame")
                 result = result.unstack(  # type: ignore[assignment]
-                    val, fill_value=fill_value, sort=sort
+                    val, fill_value=fill_value, sort=sort, no_fill=no_fill
                 )
                 clocs = [v if v < val else v - 1 for v in clocs]
 
@@ -528,7 +563,7 @@ def _unstack_multiple(
         # error: Incompatible types in assignment (expression has type "DataFrame |
         # Series", variable has type "DataFrame")
         unstacked = dummy_df.unstack(  # type: ignore[assignment]
-            "__placeholder__", fill_value=fill_value, sort=sort
+            "__placeholder__", fill_value=fill_value, sort=sort, no_fill=no_fill
         )
         if isinstance(unstacked, Series):
             unstcols = unstacked.index
@@ -559,18 +594,28 @@ def unstack(obj: Series, level, fill_value=..., sort: bool = ...) -> DataFrame:
 
 @overload
 def unstack(
-    obj: Series | DataFrame, level, fill_value=..., sort: bool = ...
+    obj: Series | DataFrame,
+    level,
+    fill_value=...,
+    sort: bool = ...,
+    no_fill: bool = ...,
 ) -> Series | DataFrame: ...
 
 
 def unstack(
-    obj: Series | DataFrame, level, fill_value=None, sort: bool = True
+    obj: Series | DataFrame,
+    level,
+    fill_value=None,
+    sort: bool = True,
+    no_fill: bool = False,
 ) -> Series | DataFrame:
     if isinstance(level, (tuple, list)):
         if len(level) != 1:
             # _unstack_multiple only handles MultiIndexes,
             # and isn't needed for a single level
-            return _unstack_multiple(obj, level, fill_value=fill_value, sort=sort)
+            return _unstack_multiple(
+                obj, level, fill_value=fill_value, sort=sort, no_fill=no_fill
+            )
         else:
             level = level[0]
 
@@ -580,7 +625,9 @@ def unstack(
 
     if isinstance(obj, DataFrame):
         if isinstance(obj.index, MultiIndex):
-            return _unstack_frame(obj, level, fill_value=fill_value, sort=sort)
+            return _unstack_frame(
+                obj, level, fill_value=fill_value, sort=sort, no_fill=no_fill
+            )
         else:
             return obj.T.stack()
     elif not isinstance(obj.index, MultiIndex):
@@ -592,19 +639,25 @@ def unstack(
         )
     else:
         if is_1d_only_ea_dtype(obj.dtype):
-            return _unstack_extension_series(obj, level, fill_value, sort=sort)
+            return _unstack_extension_series(
+                obj, level, fill_value, sort=sort, no_fill=no_fill
+            )
         unstacker = _Unstacker(
-            obj.index, level=level, constructor=obj._constructor_expanddim, sort=sort
+            obj.index,
+            level=level,
+            constructor=obj._constructor_expanddim,
+            sort=sort,
+            no_fill=no_fill,
         )
         return unstacker.get_result(obj, value_columns=None, fill_value=fill_value)
 
 
 def _unstack_frame(
-    obj: DataFrame, level, fill_value=None, sort: bool = True
+    obj: DataFrame, level, fill_value=None, sort: bool = True, no_fill: bool = False
 ) -> DataFrame:
     assert isinstance(obj.index, MultiIndex)  # checked by caller
     unstacker = _Unstacker(
-        obj.index, level=level, constructor=obj._constructor, sort=sort
+        obj.index, level=level, constructor=obj._constructor, sort=sort, no_fill=no_fill
     )
 
     if not obj._can_fast_transpose:
@@ -617,7 +670,7 @@ def _unstack_frame(
 
 
 def _unstack_extension_series(
-    series: Series, level, fill_value, sort: bool
+    series: Series, level, fill_value, sort: bool, no_fill: bool = False
 ) -> DataFrame:
     """
     Unstack an ExtensionArray-backed Series.
@@ -636,6 +689,8 @@ def _unstack_extension_series(
         ``series.values.take``.
     sort : bool
         Whether to sort the resulting MuliIndex levels
+    no_fill : bool, default False
+        Whether to raise an error if any missing values are encountered
 
     Returns
     -------
@@ -645,7 +700,7 @@ def _unstack_extension_series(
     """
     # Defer to the logic in ExtensionBlock._unstack
     df = series.to_frame()
-    result = df.unstack(level=level, fill_value=fill_value, sort=sort)
+    result = df.unstack(level=level, fill_value=fill_value, sort=sort, no_fill=no_fill)
 
     # equiv: result.droplevel(level=0, axis=1)
     #  but this avoids an extra copy

pandas/core/series.py

@@ -4282,6 +4282,7 @@ def unstack(
         level: IndexLabel = -1,
         fill_value: Hashable | None = None,
         sort: bool = True,
+        no_fill: bool = False,
     ) -> DataFrame:
         """
         Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
@@ -4294,6 +4295,10 @@ def unstack(
             Value to use when replacing NaN values.
         sort : bool, default True
             Sort the level(s) in the resulting MultiIndex columns.
+        no_fill : bool, default False
+            If True, raise a ValueError if any missing values would need to be filled.
+            This is useful to ensure data integrity when you expect a complete
+            1:1 mapping between stacked and unstacked representations.
 
         Returns
         -------
@@ -4333,7 +4338,7 @@ def unstack(
         """
         from pandas.core.reshape.reshape import unstack
 
-        return unstack(self, level, fill_value, sort)
+        return unstack(self, level, fill_value, sort, no_fill)
 
     # ----------------------------------------------------------------------
     # function application

pandas/tests/frame/test_stack_unstack.py

@@ -2779,3 +2779,56 @@ def test_stack_preserves_na(dtype, na_value, test_multiindex):
         )
     expected = Series(1, index=expected_index)
     tm.assert_series_equal(result, expected)
+
+
+def test_unstack_no_fill_complete_data():
+    df = DataFrame(
+        {"value": [1, 2, 3, 4]},
+        index=MultiIndex.from_product([["A", "B"], ["x", "y"]]),
+    )
+
+    result = df.unstack(level=-1, no_fill=True)
+    expected = DataFrame(
+        [[1, 2], [3, 4]],
+        index=["A", "B"],
+        columns=MultiIndex.from_tuples([("value", "x"), ("value", "y")]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_no_fill_incomplete_data():
+    df = DataFrame(
+        {"value": [1, 2, 3]},
+        index=MultiIndex.from_tuples([("A", "x"), ("A", "y"), ("B", "x")]),
+    )
+
+    # Should raise ValueError when no_fill=True and filling is required
+    msg = "Cannot unstack with no_fill=True because filling is required"
+    with pytest.raises(ValueError, match=msg):
+        df.unstack(level=-1, no_fill=True)
+
+
+def test_unstack_no_fill_default_behavior():
+    df = DataFrame(
+        {"value": [1, 2, 3]},
+        index=MultiIndex.from_tuples([("A", "x"), ("A", "y"), ("B", "x")]),
+    )
+
+    result = df.unstack(level=-1, no_fill=False)
+    expected = DataFrame(
+        [[1.0, 2.0], [3.0, np.nan]],
+        index=["A", "B"],
+        columns=MultiIndex.from_tuples([("value", "x"), ("value", "y")]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_no_fill_with_fill_value():
+    df = DataFrame(
+        {"value": [1, 2, 3]},
+        index=MultiIndex.from_tuples([("A", "x"), ("A", "y"), ("B", "x")]),
+    )
+
+    msg = "Cannot unstack with no_fill=True because filling is required"
+    with pytest.raises(ValueError, match=msg):
+        df.unstack(level=-1, fill_value=0, no_fill=True)

pandas/tests/series/methods/test_unstack.py

@@ -167,3 +167,38 @@ def test_unstack_mixed_level_names():
         index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]),
     )
     tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_no_fill_complete_data():
+    index = MultiIndex.from_product([["one", "two"], ["a", "b"]])
+    ser = Series(np.arange(1.0, 5.0), index=index)
+
+    result = ser.unstack(level=-1, no_fill=True)
+    expected = DataFrame(
+        [[1.0, 2.0], [3.0, 4.0]],
+        index=["one", "two"],
+        columns=["a", "b"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_no_fill_incomplete_data():
+    index = MultiIndex.from_tuples([("one", "a"), ("one", "b"), ("two", "a")])
+    ser = Series([1, 2, 3], index=index)
+
+    msg = "Cannot unstack with no_fill=True because filling is required"
+    with pytest.raises(ValueError, match=msg):
+        ser.unstack(level=-1, no_fill=True)
+
+
+def test_unstack_no_fill_default_behavior():
+    index = MultiIndex.from_tuples([("one", "a"), ("one", "b"), ("two", "a")])
+    ser = Series([1, 2, 3], index=index)
+
+    result = ser.unstack(level=-1, no_fill=False)
+    expected = DataFrame(
+        [[1.0, 2.0], [3.0, np.nan]],
+        index=["one", "two"],
+        columns=["a", "b"],
+    )
+    tm.assert_frame_equal(result, expected)