Clean up py36 build

ci/requirements-3.6.run

@@ -1,3 +1,4 @@
 python-dateutil
 pytz
 numpy
+scipy

pandas/core/groupby.py

@@ -2466,7 +2466,7 @@ def is_in_obj(gpr):
                          "Defaulting to column but "
                          "this will raise an ambiguity error in a "
                          "future version") % gpr,
-                        FutureWarning, stacklevel=2)
+                        FutureWarning, stacklevel=5)
                 in_axis, name, gpr = True, gpr, obj[gpr]
                 exclusions.append(name)
             elif gpr in obj.index.names:
@@ -4025,7 +4025,9 @@ def __iter__(self):
         sdata = self._get_sorted_data()
 
         if self.ngroups == 0:
-            raise StopIteration
+            # we are inside a generator, rather than raise StopIteration
+            # we merely return signal the end
+            return
 
         starts, ends = lib.generate_slices(self.slabels, self.ngroups)
 

pandas/io/parsers.py

@@ -385,14 +385,20 @@ def _read(filepath_or_buffer, kwds):
         raise NotImplementedError("'nrows' and 'chunksize' cannot be used"
                                   " together yet.")
     elif nrows is not None:
-        data = parser.read(nrows)
-        parser.close()
+        try:
+            data = parser.read(nrows)
+        finally:
+            parser.close()
         return data
+
     elif chunksize or iterator:
         return parser
 
-    data = parser.read()
-    parser.close()
+    try:
+        data = parser.read()
+    finally:
+        parser.close()
+
     return data
 
 _parser_defaults = {

pandas/io/stata.py

@@ -169,10 +169,13 @@ def read_stata(filepath_or_buffer, convert_dates=True,
     if iterator or chunksize:
         data = reader
     else:
-        data = reader.read()
-        reader.close()
+        try:
+            data = reader.read()
+        finally:
+            reader.close()
     return data
 
+
 _date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"]
 
 

pandas/io/tests/parser/test_unsupported.py

@@ -69,9 +69,9 @@ def test_c_engine(self):
         msg = 'Error tokenizing data'
 
         with tm.assertRaisesRegexp(ParserError, msg):
-            read_table(StringIO(text), sep='\s+')
+            read_table(StringIO(text), sep='\\s+')
         with tm.assertRaisesRegexp(ParserError, msg):
-            read_table(StringIO(text), engine='c', sep='\s+')
+            read_table(StringIO(text), engine='c', sep='\\s+')
 
         msg = "Only length-1 thousands markers supported"
         data = """A|B|C

pandas/sparse/tests/test_array.py

@@ -361,7 +361,7 @@ def test_astype(self):
             arr.astype('i8')
 
         arr = SparseArray([0, np.nan, 0, 1], fill_value=0)
-        msg = 'Cannot convert non-finite values \(NA or inf\) to integer'
+        msg = 'Cannot convert non-finite values \\(NA or inf\\) to integer'
         with tm.assertRaisesRegexp(ValueError, msg):
             arr.astype('i8')
 
@@ -708,7 +708,7 @@ def test_cumsum(self):
             tm.assert_sp_array_equal(out, expected)
 
             axis = 1  # SparseArray currently 1-D, so only axis = 0 is valid.
-            msg = "axis\(={axis}\) out of bounds".format(axis=axis)
+            msg = "axis\\(={axis}\\) out of bounds".format(axis=axis)
             with tm.assertRaisesRegexp(ValueError, msg):
                 SparseArray(data).cumsum(axis=axis)
 

pandas/tests/frame/test_dtypes.py

@@ -390,7 +390,7 @@ def test_astype_cast_nan_inf_int(self):
         # GH14265, check nan and inf raise error when converting to int
         types = [np.int32, np.int64]
         values = [np.nan, np.inf]
-        msg = 'Cannot convert non-finite values \(NA or inf\) to integer'
+        msg = 'Cannot convert non-finite values \\(NA or inf\\) to integer'
 
         for this_type in types:
             for this_val in values:

pandas/tests/series/test_dtypes.py

@@ -46,7 +46,7 @@ def test_astype_cast_nan_inf_int(self):
         # GH14265, check nan and inf raise error when converting to int
         types = [np.int32, np.int64]
         values = [np.nan, np.inf]
-        msg = 'Cannot convert non-finite values \(NA or inf\) to integer'
+        msg = 'Cannot convert non-finite values \\(NA or inf\\) to integer'
 
         for this_type in types:
             for this_val in values:

pandas/tools/tests/test_pivot.py

@@ -381,21 +381,26 @@ def _check_output(result, values_col, index=['A', 'B'],
         df = df.set_index(['JOB', 'NAME', 'YEAR', 'MONTH'], drop=False,
                           append=False)
 
-        result = df.pivot_table(index=['JOB', 'NAME'],
-                                columns=['YEAR', 'MONTH'],
-                                values=['DAYS', 'SALARY'],
-                                aggfunc={'DAYS': 'mean', 'SALARY': 'sum'},
-                                margins=True)
-
-        expected = df.pivot_table(index=['JOB', 'NAME'],
-                                  columns=['YEAR', 'MONTH'], values=['DAYS'],
-                                  aggfunc='mean', margins=True)
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            result = df.pivot_table(index=['JOB', 'NAME'],
+                                    columns=['YEAR', 'MONTH'],
+                                    values=['DAYS', 'SALARY'],
+                                    aggfunc={'DAYS': 'mean', 'SALARY': 'sum'},
+                                    margins=True)
+
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            expected = df.pivot_table(index=['JOB', 'NAME'],
+                                      columns=['YEAR', 'MONTH'],
+                                      values=['DAYS'],
+                                      aggfunc='mean', margins=True)
 
         tm.assert_frame_equal(result['DAYS'], expected['DAYS'])
 
-        expected = df.pivot_table(index=['JOB', 'NAME'],
-                                  columns=['YEAR', 'MONTH'], values=['SALARY'],
-                                  aggfunc='sum', margins=True)
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            expected = df.pivot_table(index=['JOB', 'NAME'],
+                                      columns=['YEAR', 'MONTH'],
+                                      values=['SALARY'],
+                                      aggfunc='sum', margins=True)
 
         tm.assert_frame_equal(result['SALARY'], expected['SALARY'])