Merge branch 'master' of https://github.com/pandas-dev/pandas into accum

pandas-dev · jreback · Dec 30, 2019 · Nov 26, 2019 · Dec 20, 2019 · Dec 26, 2019
commit 4abecfc967f0eea9c7b1c5155a7ec2f9a27ad409
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
@@ -11106,12 +11106,43 @@ def cum_func(self, axis=None, skipna=True, *args, **kwargs):
 
         def na_accum_func(blk_values):
             # We will be applying this function to block values
-            if skipna and issubclass(
-                blk_values.dtype.type, (np.datetime64, np.timedelta64)
-            ):
-                result = accum_func(blk_values.T, axis)
-                mask = isna(blk_values.T)
-                np.putmask(result, mask, iNaT)
+            if blk_values.dtype.kind in ["m", "M"]:
+                # numpy 1.18 started sorting NaTs at the end instead of beginning,
+                #  so we need to work around to maintain backwards-consistency.
+                orig_dtype = blk_values.dtype
+
+                # We need to define mask before masking NaTs
+                mask = isna(blk_values)
+
+                if accum_func == np.minimum.accumulate:
+                    # Note: the accum_func comparison fails as an "is" comparison
+                    y = blk_values.view("i8")
+                    y[mask] = np.iinfo(np.int64).max
+                    changed = True
+                else:
+                    y = blk_values
+                    changed = False
+
+                result = accum_func(y.view("i8"), axis)
+                if skipna:
+                    np.putmask(result, mask, iNaT)
+                elif accum_func == np.minimum.accumulate:
+                    # Restore NaTs that we masked previously
+                    nz = (~np.asarray(mask)).nonzero()[0]
+                    if len(nz):
+                        # everything up to the first non-na entry stays NaT
+                        result[: nz[0]] = iNaT
+
+                if changed:
+                    # restore NaT elements
+                    y[mask] = iNaT  # TODO: could try/finally for this?
+
+                if isinstance(blk_values, np.ndarray):
+                    result = result.view(orig_dtype)
+                else:
+                    # DatetimeArray
+                    result = type(blk_values)._from_sequence(result, dtype=orig_dtype)
+
             elif skipna and not issubclass(
                 blk_values.dtype.type, (np.integer, np.bool_)
             ):
@@ -11122,9 +11153,12 @@ def na_accum_func(blk_values):
                 np.putmask(result, mask, mask_b)
             else:
                 result = accum_func(blk_values.T, axis)
-            return result.T
+
+            # transpose back for ndarray, not for EA
+            return result.T if hasattr(result, "T") else result
 
         result = self._data.apply(na_accum_func)
+
         d = self._construct_axes_dict()
         d["copy"] = False
         return self._constructor(result, **d).__finalize__(self)