BUG: series.interpolate(inplace=True) *actually* inplace (#44749)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -701,6 +701,7 @@ Missing
 - Bug in :meth:`DataFrame.fillna` with limit and no method ignores axis='columns' or ``axis = 1`` (:issue:`40989`)
 - Bug in :meth:`DataFrame.fillna` not replacing missing values when using a dict-like ``value`` and duplicate column names (:issue:`43476`)
 - Bug in constructing a :class:`DataFrame` with a dictionary ``np.datetime64`` as a value and ``dtype='timedelta64[ns]'``, or vice-versa, incorrectly casting instead of raising (:issue:`??`)
+- Bug in :meth:`Series.interpolate` and :meth:`DataFrame.interpolate` with ``inplace=True`` not writing to the underlying array(s) in-place (:issue:`44749`)
 -
 
 MultiIndex

pandas/core/internals/blocks.py

@@ -1079,7 +1079,7 @@ def interpolate(
         data = self.values if inplace else self.values.copy()
         data = cast(np.ndarray, data)  # bc overridden by ExtensionBlock
 
-        interp_values = missing.interpolate_array_2d(
+        missing.interpolate_array_2d(
             data,
             method=method,
             axis=axis,
@@ -1091,7 +1091,7 @@ def interpolate(
             **kwargs,
         )
 
-        nb = self.make_block_same_class(interp_values)
+        nb = self.make_block_same_class(data)
         return nb._maybe_downcast([nb], downcast)
 
     def take_nd(

pandas/core/missing.py

@@ -217,11 +217,13 @@ def interpolate_array_2d(
     coerce: bool = False,
     downcast: str | None = None,
     **kwargs,
-) -> np.ndarray:
+) -> None:
     """
     Wrapper to dispatch to either interpolate_2d or _interpolate_2d_with_fill.
 
-    Returned ndarray has same dtype as 'data'.
+    Notes
+    -----
+    Alters 'data' in-place.
     """
     try:
         m = clean_fill_method(method)
@@ -240,11 +242,10 @@ def interpolate_array_2d(
             limit=limit,
             limit_area=limit_area,
         )
-        interp_values = data
     else:
         assert index is not None  # for mypy
 
-        interp_values = _interpolate_2d_with_fill(
+        _interpolate_2d_with_fill(
             data=data,
             index=index,
             axis=axis,
@@ -255,7 +256,7 @@ def interpolate_array_2d(
             fill_value=fill_value,
             **kwargs,
         )
-    return interp_values
+    return
 
 
 def _interpolate_2d_with_fill(
@@ -268,13 +269,15 @@ def _interpolate_2d_with_fill(
     limit_area: str | None = None,
     fill_value: Any | None = None,
     **kwargs,
-) -> np.ndarray:
+) -> None:
     """
     Column-wise application of _interpolate_1d.
 
     Notes
     -----
-    The signature does differs from _interpolate_1d because it only
+    Alters 'data' in-place.
+
+    The signature does differ from _interpolate_1d because it only
     includes what is needed for Block.interpolate.
     """
     # validate the interp method
@@ -314,12 +317,10 @@ def _interpolate_2d_with_fill(
 
     indices = _index_to_interp_indices(index, method)
 
-    def func(yvalues: np.ndarray) -> np.ndarray:
-        # process 1-d slices in the axis direction, returning it
+    def func(yvalues: np.ndarray) -> None:
+        # process 1-d slices in the axis direction
 
-        # should the axis argument be handled below in apply_along_axis?
-        # i.e. not an arg to _interpolate_1d
-        return _interpolate_1d(
+        _interpolate_1d(
             indices=indices,
             yvalues=yvalues,
             method=method,
@@ -332,7 +333,8 @@ def func(yvalues: np.ndarray) -> np.ndarray:
         )
 
     # interp each column independently
-    return np.apply_along_axis(func, axis, data)
+    np.apply_along_axis(func, axis, data)
+    return
 
 
 def _index_to_interp_indices(index: Index, method: str) -> np.ndarray:
@@ -370,23 +372,25 @@ def _interpolate_1d(
     **kwargs,
 ):
     """
-    Logic for the 1-d interpolation.  The result should be 1-d, inputs
+    Logic for the 1-d interpolation.  The input
     indices and yvalues will each be 1-d arrays of the same length.
 
     Bounds_error is currently hardcoded to False since non-scipy ones don't
     take it as an argument.
+
+    Notes
+    -----
+    Fills 'yvalues' in-place.
     """
 
     invalid = isna(yvalues)
     valid = ~invalid
 
     if not valid.any():
-        result = np.empty(indices.shape, dtype=np.float64)
-        result.fill(np.nan)
-        return result
+        return
 
     if valid.all():
-        return yvalues
+        return
 
     # These are sets of index pointers to invalid values... i.e. {0, 1, etc...
     all_nans = set(np.flatnonzero(invalid))
@@ -432,17 +436,15 @@ def _interpolate_1d(
     # sort preserve_nans and convert to list
     preserve_nans = sorted(preserve_nans)
 
-    result = yvalues.copy()
-
     if method in NP_METHODS:
         # np.interp requires sorted X values, #21037
 
         indexer = np.argsort(indices[valid])
-        result[invalid] = np.interp(
+        yvalues[invalid] = np.interp(
             indices[invalid], indices[valid][indexer], yvalues[valid][indexer]
         )
     else:
-        result[invalid] = _interpolate_scipy_wrapper(
+        yvalues[invalid] = _interpolate_scipy_wrapper(
             indices[valid],
             yvalues[valid],
             indices[invalid],
@@ -453,8 +455,8 @@ def _interpolate_1d(
             **kwargs,
         )
 
-    result[preserve_nans] = np.nan
-    return result
+    yvalues[preserve_nans] = np.nan
+    return
 
 
 def _interpolate_scipy_wrapper(

pandas/tests/frame/methods/test_interpolate.py

@@ -12,6 +12,23 @@
 
 
 class TestDataFrameInterpolate:
+    def test_interpolate_inplace(self, frame_or_series, using_array_manager, request):
+        # GH#44749
+        if using_array_manager and frame_or_series is DataFrame:
+            mark = pytest.mark.xfail(reason=".values-based in-place check is invalid")
+            request.node.add_marker(mark)
+
+        obj = frame_or_series([1, np.nan, 2])
+        orig = obj.values
+
+        obj.interpolate(inplace=True)
+        expected = frame_or_series([1, 1.5, 2])
+        tm.assert_equal(obj, expected)
+
+        # check we operated *actually* inplace
+        assert np.shares_memory(orig, obj.values)
+        assert orig.squeeze()[1] == 1.5
+
     def test_interp_basic(self):
         df = DataFrame(
             {