diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index 1d49dbdee9c03..5a90ae23dc84d 100644
--- a/doc/source/user_guide/io.rst
+++ b/doc/source/user_guide/io.rst
@@ -5047,6 +5047,17 @@ Example of a callable using PostgreSQL `COPY clause
   from io import StringIO
 
   def psql_insert_copy(table, conn, keys, data_iter):
+      """
+      Execute SQL statement inserting data
+
+      Parameters
+      ----------
+      table : pandas.io.sql.SQLTable
+      conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
+      keys : list of str
+          Column names
+      data_iter : Iterable that iterates the values to be inserted
+      """
       # gets a DBAPI connection that can provide a cursor
       dbapi_conn = conn.connection
       with dbapi_conn.cursor() as cur:
@@ -5080,6 +5091,18 @@ table name and optionally a subset of columns to read.
 
    pd.read_sql_table('data', engine)
 
+.. note::
+
+  Note that pandas infers column dtypes from query outputs, and not by looking
+  up data types in the physical database schema. For example, assume ``userid``
+  is an integer column in a table. Then, intuitively, ``select userid ...`` will
+  return integer-valued series, while ``select cast(userid as text) ...`` will
+  return object-valued (str) series. Accordingly, if the query output is empty,
+  then all resulting columns will be returned as object-valued (since they are
+  most general). If you foresee that your query will sometimes generate an empty
+  result, you may want to explicitly typecast afterwards to ensure dtype
+  integrity.
+
 You can also specify the name of the column as the ``DataFrame`` index,
 and specify a subset of columns to be read.