Add tests with time zones, and clean up DataFrame test

Author: evanpw

pandas/tests/test_frame.py

@@ -4194,27 +4194,24 @@ def test_astype_cast_nan_int(self):
 
     def test_astype_str(self):
         # GH9757
-        dts = Series(date_range('2010-01-04', periods=5))
-        tds = Series([Timedelta(x, unit='d') for x in range(5)])
-        ns = Series(range(5))
-        fs = Series([0.0, 0.2, 0.4, 0.6, 0.8])
+        a = Series(date_range('2010-01-04', periods=5))
+        b = Series(date_range('3/6/2012 00:00', periods=5, tz='US/Eastern'))
+        c = Series([Timedelta(x, unit='d') for x in range(5)])
+        d = Series(range(5))
+        e = Series([0.0, 0.2, 0.4, 0.6, 0.8])
 
-        df = DataFrame({
-            'dts' : dts.values,
-            'tds' : tds.values,
-            'ns' : ns.values,
-            'fs' : fs.values,
-            })
+        df = DataFrame({'a' : a, 'b' : b, 'c' : c, 'd' : d, 'e' : e})
 
         # Test str and unicode on python 2.x and just str on python 3.x
         for tt in set([str, compat.text_type]):
             result = df.astype(tt)
 
             expected = DataFrame({
-                'dts' : list(map(tt, dts.values)),
-                'tds' : list(map(tt, tds.values)),
-                'ns' : list(map(tt, ns.values)),
-                'fs' : list(map(tt, fs.values)),
+                'a' : list(map(tt, a.values)),
+                'b' : list(map(tt, b.values)),
+                'c' : list(map(tt, c.values)),
+                'd' : list(map(tt, d.values)),
+                'e' : list(map(tt, e.values)),
                 })
 
             assert_frame_equal(result, expected)

pandas/tests/test_series.py

@@ -5519,6 +5519,11 @@ def test_astype_str(self):
             expected = Series([tt(ts.values[0])])
             assert_series_equal(s, expected)
 
+            ts = Series([Timestamp('2010-01-04 00:00:00', tz='US/Eastern')])
+            s = ts.astype(tt)
+            expected = Series([tt(ts.values[0])])
+            assert_series_equal(s, expected)
+
             td = Series([Timedelta(1, unit='d')])
             s = td.astype(tt)
             expected = Series([tt(td.values[0])])