consolidate+simplify

pandas-dev · jreback · Jan 1, 2020 · Nov 25, 2019 · Nov 25, 2019 · Nov 25, 2019
commit ebb33c1bc93e88b62fc3d88ef5107c493b67cedc
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
@@ -7593,31 +7593,26 @@ def _get_data(axis_matters):
                 raise NotImplementedError(msg)
             return data
 
-        if self.size == 0:
-            pass
+        if numeric_only is not None and axis in [0, 1]:
+            df = self
+            if numeric_only is True:
+                df = _get_data(axis_matters=True)
+            if axis == 1:
+                df = df.T
+                axis = 0
+
+            out_dtype = "bool" if filter_type == "bool" else None
 
-        elif numeric_only is False:
-            res = self._data.reduce(op)
+            # After possibly _get_data and transposing, we are now in the
+            #  simple case where we can use BlockManager._reduce
+            res = df._data.reduce(op, axis=1, skipna=skipna, **kwds)
             assert isinstance(res, dict)
-            assert len(res) == max(list(res.keys())) + 1, res.keys()
-            out = self._constructor_sliced(res, index=range(len(res)))
-            out.index = self.columns
+            if len(res):
+                assert len(res) == max(list(res.keys())) + 1, res.keys()
+            out = df._constructor_sliced(res, index=range(len(res)), dtype=out_dtype)
+            out.index = df.columns
             return out
 
-        elif numeric_only is True and axis in [0, 1]:
-            data = _get_data(axis_matters=True)
-            if axis == 1:
-                data = data.T
-            return data._reduce(
-                op,
-                name,
-                axis=0,
-                skipna=skipna,
-                numeric_only=False,
-                filter_type=filter_type,
-                **kwds,
-            )
-
         if numeric_only is None:
             values = self.values
             try:

diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
@@ -350,19 +350,14 @@ def reduce(self, func, *args, **kwargs):
         res = {}
         for blk in self.blocks:
             bres = func(blk.values, *args, **kwargs)
-            if np.ndim(bres) == 0 and blk.shape[0] != 1:
-                # i.e. we reduced over all axes and not just one; re-do column-wise
-                new_res = {
-                    blk.mgr_locs.as_array[i]: func(blk.values[i], *args, **kwargs)
-                    for i in range(len(blk.values))
-                }
-            elif np.ndim(bres) == 0:
+
+            if np.ndim(bres) == 0:
                 # EA
                    assert blk.shape[0] == 1
                 new_res = zip(blk.mgr_locs.as_array, [bres])
             else:
                 assert bres.ndim == 1, bres.shape
-                assert blk.shape[0] == len(bres)
+                assert blk.shape[0] == len(bres), (blk.shape, bres.shape, args, kwargs)
                 new_res = zip(blk.mgr_locs.as_array, bres)
 
             nr = dict(new_res)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
@@ -831,7 +831,7 @@ def reduction(values, axis=None, skipna=True, mask=None):
             try:
                 result = getattr(values, meth)(axis, dtype=dtype_max)
                 result.fill(np.nan)
-            except (AttributeError, TypeError, ValueError, np.core._internal.AxisError):
+            except (AttributeError, TypeError, ValueError):
                 result = np.nan
         else:
             result = getattr(values, meth)(axis)

diff --git a/pandas/tests/frame/test_reductions.py b/pandas/tests/frame/test_reductions.py
@@ -0,0 +1,12 @@
+"""
+Tests for DataFrame reductions that are DataFrame-specific, i.e. cannot
+be shared in tests.reductions.
+"""
+import pandas as pd
+import numpy as np
+
+
+if True:#def test_blockwise_reduction():
+	arr = np.arange(10)
+	tdarr = arr.astype("m8[s]")
+	df = pd.DataFrame({"A": arr, "B": tdarr})