Fix for pandas-dev#18178 and pandas-dev#18187 by changing the concat of empty RangeIndex

The `_concat_rangeindex_same_dtype` now keeps track of the last non-empty RangeIndex to extract the new stop value.

This fixes two issues with concatenating non-empty and empty DataFrames and Series.

Two regression tests were added as well.

Author: Robert Meyer

pandas/core/dtypes/concat.py

@@ -572,11 +572,13 @@ def _concat_rangeindex_same_dtype(indexes):
     indexes = [RangeIndex(3), RangeIndex(4, 6)] -> Int64Index([0,1,2,4,5])
     """
 
-    start = step = next = None
+    start = step = next = last_non_empty = None
 
     for obj in indexes:
         if not len(obj):
             continue
+        # Remember the last non-empty index for the stop value
+        last_non_empty = obj
 
         if start is None:
             # This is set by the first non-empty index
@@ -599,8 +601,12 @@ def _concat_rangeindex_same_dtype(indexes):
         if step is not None:
             next = obj[-1] + step
 
-    if start is None:
+    if last_non_empty is None:
+        # Here all "indexes" had 0 length, i.e. were empty.
+        # Simply take start, stop, and step from the last "obj".
         start = obj._start
         step = obj._step
-    stop = obj._stop if next is None else next
+        stop = obj._stop
+    else:
+        stop = last_non_empty._stop if next is None else next
     return indexes[0].__class__(start, stop, step)

pandas/tests/reshape/test_concat.py

@@ -1983,3 +1983,21 @@ def test_concat_will_upcast(dt, pdt):
                pdt(np.array([5], dtype=dt, ndmin=dims))]
         x = pd.concat(dfs)
         assert x.values.dtype == 'float64'
+
+
+def test_concat_empty_and_non_empty_frame_regression():
+    # GH 18178 regression test
+    df1 = pd.DataFrame({'foo': [1]})
+    df2 = pd.DataFrame({'foo': []})
+    expected = pd.DataFrame({'foo': [1.0]})
+    result = pd.concat([df1, df2])
+    assert_frame_equal(result, expected)
+
+
+def test_concat_empty_and_non_empty_series_regression():
+    # GH 18187 regression test
+    s1 = pd.Series([1])
+    s2 = pd.Series([])
+    expected = s1
+    result = pd.concat([s1, s2])
+    tm.assert_series_equal(result, expected)