ENH: Allow to_sql to recognize single sql type (GH11886) (#13614)

doc/source/whatsnew/v0.19.0.txt

@@ -310,6 +310,7 @@ Other enhancements
 - ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`)
 - A function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`)
 - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`)
+- ``DataFrame.to_sql `` now allows a single value as the SQL type for all columns (:issue:`11886`).
 - ``Series.append`` now supports the ``ignore_index`` option (:issue:`13677`)
 - ``.to_stata()`` and ``StataWriter`` can now write variable labels to Stata dta files using a dictionary to make column names to labels (:issue:`13535`, :issue:`13536`)
 - ``.to_stata()`` and ``StataWriter`` will automatically convert ``datetime64[ns]`` columns to Stata format ``%tc``, rather than raising a ``ValueError`` (:issue:`12259`)
@@ -322,7 +323,6 @@ Other enhancements
                        index=['row1', 'row2'])
      df.sort_values(by='row2', axis=1)
 
-
 .. _whatsnew_0190.api:
 
 API changes

pandas/io/sql.py

@@ -14,7 +14,7 @@
 import pandas.lib as lib
 from pandas.types.missing import isnull
 from pandas.types.dtypes import DatetimeTZDtype
-from pandas.types.common import (is_list_like,
+from pandas.types.common import (is_list_like, is_dict_like,
                                  is_datetime64tz_dtype)
 
 from pandas.compat import (map, zip, raise_with_traceback,
@@ -448,9 +448,10 @@ def to_sql(frame, name, con, flavor=None, schema=None, if_exists='fail',
     chunksize : int, default None
         If not None, then rows will be written in batches of this size at a
         time.  If None, all rows will be written at once.
-    dtype : dict of column name to SQL type, default None
+    dtype : single SQLtype or dict of column name to SQL type, default None
         Optional specifying the datatype for columns. The SQL type should
         be a SQLAlchemy type, or a string for sqlite3 fallback connection.
+        If all columns are of the same type, one single value can be used.
 
     """
     if if_exists not in ('fail', 'replace', 'append'):
@@ -1121,11 +1122,15 @@ def to_sql(self, frame, name, if_exists='fail', index=True,
         chunksize : int, default None
             If not None, then rows will be written in batches of this size at a
             time.  If None, all rows will be written at once.
-        dtype : dict of column name to SQL type, default None
+        dtype : single type or dict of column name to SQL type, default None
             Optional specifying the datatype for columns. The SQL type should
-            be a SQLAlchemy type.
+            be a SQLAlchemy type. If all columns are of the same type, one
+            single value can be used.
 
         """
+        if dtype and not is_dict_like(dtype):
+            dtype = {col_name: dtype for col_name in frame}
+
         if dtype is not None:
             from sqlalchemy.types import to_instance, TypeEngine
             for col, my_type in dtype.items():
@@ -1473,11 +1478,15 @@ def to_sql(self, frame, name, if_exists='fail', index=True,
         chunksize : int, default None
             If not None, then rows will be written in batches of this
             size at a time. If None, all rows will be written at once.
-        dtype : dict of column name to SQL type, default None
+        dtype : single type or dict of column name to SQL type, default None
             Optional specifying the datatype for columns. The SQL type should
-            be a string.
+            be a string. If all columns are of the same type, one single value
+            can be used.
 
         """
+        if dtype and not is_dict_like(dtype):
+            dtype = {col_name: dtype for col_name in frame}
+
         if dtype is not None:
             for col, my_type in dtype.items():
                 if not isinstance(my_type, str):

pandas/io/tests/test_sql.py

@@ -1537,6 +1537,15 @@ def test_dtype(self):
         self.assertTrue(isinstance(sqltype, sqlalchemy.String))
         self.assertEqual(sqltype.length, 10)
 
+        # single dtype
+        df.to_sql('single_dtype_test', self.conn, dtype=sqlalchemy.TEXT)
+        meta = sqlalchemy.schema.MetaData(bind=self.conn)
+        meta.reflect()
+        sqltypea = meta.tables['single_dtype_test'].columns['A'].type
+        sqltypeb = meta.tables['single_dtype_test'].columns['B'].type
+        self.assertTrue(isinstance(sqltypea, sqlalchemy.TEXT))
+        self.assertTrue(isinstance(sqltypeb, sqlalchemy.TEXT))
+
     def test_notnull_dtype(self):
         cols = {'Bool': Series([True, None]),
                 'Date': Series([datetime(2012, 5, 1), None]),
@@ -2006,6 +2015,13 @@ def test_dtype(self):
         self.assertRaises(ValueError, df.to_sql,
                           'error', self.conn, dtype={'B': bool})
 
+        # single dtype
+        df.to_sql('single_dtype_test', self.conn, dtype='STRING')
+        self.assertEqual(
+            self._get_sqlite_column_type('single_dtype_test', 'A'), 'STRING')
+        self.assertEqual(
+            self._get_sqlite_column_type('single_dtype_test', 'B'), 'STRING')
+
     def test_notnull_dtype(self):
         if self.flavor == 'mysql':
             raise nose.SkipTest('Not applicable to MySQL legacy')