Slicing an index with DateTime throws AttributeError: 'int' object has no attribute 'stop'

doc/source/whatsnew/v0.25.0.rst

@@ -672,7 +672,7 @@ Indexing
 - Bug in which :meth:`DataFrame.to_csv` caused a segfault for a reindexed data frame, when the indices were single-level :class:`MultiIndex` (:issue:`26303`).
 - Fixed bug where assigning a :class:`arrays.PandasArray` to a :class:`pandas.core.frame.DataFrame` would raise error (:issue:`26390`)
 - Allow keyword arguments for callable local reference used in the :meth:`DataFrame.query` string (:issue:`26426`)
-
+- Bug which produced ``AttributeError`` on partial matching :class:`Timestamp` in a :class:`MultiIndex`  (:issue:`26944`)
 
 Missing
 ^^^^^^^

pandas/core/indexes/multi.py

@@ -2755,7 +2755,9 @@ def convert_indexer(start, stop, step, indexer=indexer,
                 # a partial date slicer on a DatetimeIndex generates a slice
                 # note that the stop ALREADY includes the stopped point (if
                 # it was a string sliced)
-                return convert_indexer(start.start, stop.stop, step)
+                start = getattr(start, 'start', start)
+                stop = getattr(stop, 'stop', stop)
+                return convert_indexer(start, stop, step)
 
             elif level > 0 or self.lexsort_depth == 0 or step is not None:
                 # need to have like semantics here to right

pandas/tests/indexes/multi/test_indexing.py

@@ -397,3 +397,24 @@ def test_get_indexer_categorical_time():
          Categorical(date_range("2012-01-01", periods=3, freq='H'))])
     result = midx.get_indexer(midx)
     tm.assert_numpy_array_equal(result, np.arange(9, dtype=np.intp))
+
+
+def test_timestamp_multiindex_indexer():
+    # https://github.com/pandas-dev/pandas/issues/26944
+    idx = pd.MultiIndex.from_product([
+        pd.date_range("2019-01-01T00:15:33", periods=100, freq="H",
+                      name="date"),
+        ['x'],
+        [3]
+    ])
+    df = pd.DataFrame({'foo': np.arange(len(idx))}, idx)
+    result = df.loc[pd.IndexSlice['2019-1-2':, "x", :], 'foo']
+    qidx = pd.MultiIndex.from_product([
+        pd.date_range(start="2019-01-02T00:15:33", end='2019-01-05T02:15:33',
+                      freq="H", name="date"),
+        ['x'],
+        [3]
+    ])
+    should_be = pd.Series(data=np.arange(24, len(qidx) + 24), index=qidx,
+                          name="foo")
+    tm.assert_series_equal(result, should_be)