@@ -3108,11 +3108,7 @@ below and the SQLAlchemy `documentation <http://docs.sqlalchemy.org/en/rel_0_9/c
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Writing DataFrames
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~~~~~~~~~~~~~~~~~~
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- <<<<<<< HEAD
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- Assuming the following data is in a DataFrame "data", we can insert it into
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- =======
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Assuming the following data is in a DataFrame ``data ``, we can insert it into
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- >>>>>>> 6314e6f... ENH #4163 Tweaks to docs, avoid mutable default args, mysql tests
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the database using :func: `~pandas.io.sql.to_sql `.
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@@ -3209,28 +3205,19 @@ Of course, you can specify a more "complex" query.
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You can also run a plain query without creating a dataframe with
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:func: `~pandas.io.sql.execute `. This is useful for queries that don't return values,
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such as INSERT. This is functionally equivalent to calling ``execute `` on the
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- SQLAlchemy engine or db connection object. Again, ou must use the SQL syntax
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+ SQLAlchemy engine or db connection object. Again, you must use the SQL syntax
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variant appropriate for your database.
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.. code-block :: python
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sql.execute(' SELECT * FROM table_name' , engine)
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- <<<<<<< HEAD
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- <<<<<<< HEAD
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- :func: `~pandas.io.sql.tquery ` returns a list of tuples corresponding to each row.
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+ sql.execute(' INSERT INTO table_name VALUES(?, ?, ?)' , engine, params = [(' id' , 1 , 12.2 , True )])
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- :func: `~pandas.io.sql.uquery ` does the same thing as tquery, but instead of
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- returning results it returns the number of related rows.
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- =======
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- >>>>>>> ac6bf42... ENH #4163 Added more robust type coertion, datetime parsing, and parse date options. Updated optional dependancies
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In addition, the class :class: `~pandas.io.sql.PandasSQLWithEngine ` can be
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instantiated directly for more manual control over the SQL interaction.
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- =======
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- sql.execute('INSERT INTO table_name VALUES(?, ?, ?)', engine, params=[('id', 1, 12.2, True)])
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- >>>>>>> 6314e6f... ENH #4163 Tweaks to docs, avoid mutable default args, mysql tests
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Engine connection examples
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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