BUG: fix replace bug when a nested dict was passed

Author: cpcloud

doc/source/release.rst

@@ -314,6 +314,8 @@ pandas 0.12
   - Fix bug where ``HDFStore`` will fail to append because of a different block
     ordering on-disk (:issue:`4096`)
   - Better error messages on inserting incompatible columns to a frame (:issue:`4107`)
+  - Fixed bug in ``DataFrame.replace`` where a nested dict wasn't being
+    iterated over when regex=False (:issue:`4115`)
 
 
 pandas 0.11.0

doc/source/v0.12.0.txt

@@ -457,6 +457,8 @@ Bug Fixes
     rewritten in an incompatible way (:issue:`4062`, :issue:`4063`)
   - Fixed bug where sharex and sharey were not being passed to grouped_hist
     (:issue:`4089`)
+  - Fixed bug in ``DataFrame.replace`` where a nested dict wasn't being
+    iterated over when regex=False (:issue:`4115`)
 
 See the :ref:`full release notes
 <release>` or issue tracker

pandas/core/internals.py

@@ -756,7 +756,7 @@ def replace(self, to_replace, value, inplace=False, filter=None,
             blk = super(ObjectBlock, self).replace(to_replace, value,
                                                    inplace=inplace,
                                                    filter=filter, regex=regex)
-        elif both_lists and regex:
+        elif both_lists:
             for to_rep, v in itertools.izip(to_replace, value):
                 blk[0], = blk[0]._replace_single(to_rep, v, inplace=inplace,
                                                  filter=filter, regex=regex)

pandas/tests/test_frame.py

@@ -6737,6 +6737,11 @@ def test_regex_replace_dict_nested(self):
         assert_frame_equal(res3, expec)
         assert_frame_equal(res4, expec)
 
+    def test_regex_replace_dict_nested_gh4115(self):
+        df = pd.DataFrame({'Type':['Q','T','Q','Q','T'], 'tmp':2})
+        expected = DataFrame({'Type': [0,1,0,0,1], 'tmp': 2})
+        assert_frame_equal(df.replace({'Type': {'Q':0,'T':1}}), expected)
+
     def test_regex_replace_list_to_scalar(self):
         mix = {'a': range(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
         df = DataFrame(mix)