BUG: GH10747 where 'timestamp' is not inferred to be datetime column

doc/source/io.rst

@@ -1484,7 +1484,13 @@ be set to ``False`` if you need to preserve string-like numbers (e.g. '1', '2')
 
 .. note::
 
-  Large integer values may be converted to dates if ``convert_dates=True`` and the data and / or column labels appear 'date-like'. The exact threshold depends on the ``date_unit`` specified.
+  Large integer values may be converted to dates if ``convert_dates=True`` and the data and / or column labels appear 'date-like'. The exact threshold depends on the ``date_unit`` specified. 'date-like' means that the column label meets one of the following criteria:
+
+     * it ends with ``'_at'``     
+     * it ends with ``'_time'``
+     * it begins with ``'timestamp'``    
+     * it is ``'modified'``
+     * it is ``'date'``
 
 .. warning::
 

pandas/io/json.py

@@ -150,7 +150,18 @@ def read_json(path_or_buf=None, orient=None, typ='frame', dtype=True,
         Try to convert the axes to the proper dtypes.
     convert_dates : boolean, default True
         List of columns to parse for dates; If True, then try to parse
-        datelike columns default is True
+        datelike columns default is True; a column label is datelike if
+
+        * it ends with ``'_at'``,
+
+        * it ends with ``'_time'``,
+
+        * it begins with ``'timestamp'``,
+
+        * it is ``'modified'``, or
+
+        * it is ``'date'``
+
     keep_default_dates : boolean, default True.
         If parsing dates, then parse the default datelike columns
     numpy : boolean, default False
@@ -543,11 +554,13 @@ def is_ok(col):
             if not isinstance(col, compat.string_types):
                 return False
 
-            if (col.endswith('_at') or
-                    col.endswith('_time') or
-                    col.lower() == 'modified' or
-                    col.lower() == 'date' or
-                    col.lower() == 'datetime'):
+            col_lower = col.lower()
+            if (col_lower.endswith('_at') or
+                    col_lower.endswith('_time') or
+                    col_lower == 'modified' or
+                    col_lower == 'date' or
+                    col_lower == 'datetime' or
+                    col_lower.startswith('timestamp')):
                 return True
             return False
 

pandas/io/tests/test_json/test_pandas.py

@@ -1,13 +1,12 @@
 # pylint: disable-msg=W0612,E1101
 from pandas.compat import range, lrange, StringIO, OrderedDict
-from pandas import compat
 import os
 
 import numpy as np
-from pandas import Series, DataFrame, DatetimeIndex, Timestamp, CategoricalIndex
+from pandas import (Series, DataFrame, DatetimeIndex, Timestamp, CategoricalIndex,
+                    read_json, compat)
 from datetime import timedelta
 import pandas as pd
-read_json = pd.read_json
 
 from pandas.util.testing import (assert_almost_equal, assert_frame_equal,
                                  assert_series_equal, network,
@@ -574,6 +573,16 @@ def test_convert_dates(self):
         result = read_json(json, typ='series')
         assert_series_equal(result, ts)
 
+    def test_convert_dates_infer(self):
+        #GH10747
+        infer_words = ['trade_time', 'date', 'datetime', 'sold_at',
+                       'modified', 'timestamp', 'timestamps']
+        for infer_word in infer_words:
+            data = [{'id': 1, infer_word: 1036713600000}, {'id': 2}]
+            expected = DataFrame([[1, Timestamp('2002-11-08')], [2, pd.NaT]], columns=['id', infer_word])
+            result = read_json(pd.json.dumps(data))[['id', infer_word]]
+            assert_frame_equal(result, expected)
+
     def test_date_format_frame(self):
         df = self.tsframe.copy()