BUG: incorrect default in df.convert_objects was converting object types (#2909)

RELEASE.rst

@@ -41,10 +41,11 @@ pandas 0.11.0
   - added ``blocks`` attribute to DataFrames, to return a dict of dtypes to
     homogeneously dtyped DataFrames
   - added keyword ``convert_numeric`` to ``convert_objects()`` to try to
-    convert object dtypes to numeric types
+    convert object dtypes to numeric types (default is False)
   - ``convert_dates`` in ``convert_objects`` can now be ``coerce`` which will
     return a datetime64[ns] dtype with non-convertibles set as ``NaT``; will
-    preserve an all-nan object (e.g. strings)
+    preserve an all-nan object (e.g. strings), default is True (to perform
+    soft-conversion
   - Series print output now includes the dtype by default
   - Optimize internal reindexing routines (GH2819_, GH2867_)
   - ``describe_option()`` now reports the default and current value of options.
@@ -69,12 +70,14 @@ pandas 0.11.0
   - Integer block types will upcast as needed in where operations (GH2793_)
   - Series now automatically will try to set the correct dtype based on passed
     datetimelike objects (datetime/Timestamp)
-     - timedelta64 are returned in appropriate cases (e.g. Series - Series,
-       when both are datetime64)
-     - mixed datetimes and objects (GH2751_) in a constructor witll be casted
-       correctly
-     - astype on datetimes to object are now handled (as well as NaT
-       conversions to np.nan)
+
+    - timedelta64 are returned in appropriate cases (e.g. Series - Series,
+      when both are datetime64)
+    - mixed datetimes and objects (GH2751_) in a constructor witll be casted
+      correctly
+    - astype on datetimes to object are now handled (as well as NaT
+      conversions to np.nan)
+
   - arguments to DataFrame.clip were inconsistent to numpy and Series clipping
     (GH2747_)
 
@@ -92,14 +95,17 @@ pandas 0.11.0
     overflow ``int64`` and some mixed typed type lists (GH2845_)
   - Fix issue with slow printing of wide frames resulting (GH2807_)
 
-  ``HDFStore``
+  - ``HDFStore``
 
     - Fix weird PyTables error when using too many selectors in a where
     - Provide dotted attribute access to ``get`` from stores
       (e.g. store.df == store['df'])
     - Internally, change all variables to be private-like (now have leading
       underscore)
 
+  - Bug showing up in applymap where some object type columns are converted (GH2909_)
+    had an incorrect default in convert_objects
+
 .. _GH622: https://github.com/pydata/pandas/issues/622
 .. _GH797: https://github.com/pydata/pandas/issues/797
 .. _GH2681: https://github.com/pydata/pandas/issues/2681
@@ -113,6 +119,7 @@ pandas 0.11.0
 .. _GH2845: https://github.com/pydata/pandas/issues/2845
 .. _GH2867: https://github.com/pydata/pandas/issues/2867
 .. _GH2807: https://github.com/pydata/pandas/issues/2807
+.. _GH2909: https://github.com/pydata/pandas/issues/2909
 
 pandas 0.10.1
 =============

pandas/core/frame.py

@@ -1658,7 +1658,7 @@ def info(self, verbose=True, buf=None, max_cols=None):
     def dtypes(self):
         return self.apply(lambda x: x.dtype)
 
-    def convert_objects(self, convert_dates=True, convert_numeric=True):
+    def convert_objects(self, convert_dates=True, convert_numeric=False):
         """
         Attempt to infer better dtype for object columns
         Always returns a copy (even if no object columns)

pandas/tests/test_frame.py

@@ -6793,6 +6793,15 @@ def test_applymap(self):
         result = self.frame.applymap(lambda x: (x, x))
         self.assert_(isinstance(result['A'][0], tuple))
 
+        # GH 2909, object conversion to float in constructor?
+        df = DataFrame(data=[1,'a'])
+        result = df.applymap(lambda x: x)
+        self.assert_(result.dtypes[0] == object)
+
+        df = DataFrame(data=[1.,'a'])
+        result = df.applymap(lambda x: x)
+        self.assert_(result.dtypes[0] == object)
+
     def test_filter(self):
         # items