Getting started

Connect to a Database

Make a direct connection to a database and create a cursor.

cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=testdb;UID=me;PWD=pass')
cursor = cnxn.cursor()

Make a connection using a DSN. Since DSNs usually don't store passwords, you'll probably need to provide the PWD keyword.

cnxn = pyodbc.connect('DSN=test;PWD=password')
cursor = cnxn.cursor()

There are lots of options when connecting, so see the pyodbc.connect() function and the Connecting to databases Wiki section for more details.

Make sure you set the encoding or decoding settings needed for your database:

# This is just an example that works for PostgreSQL and MySQL.  Set the right settings for your database.
cnxn.setdecoding(pyodbc.SQL_CHAR, encoding='utf-8')
cnxn.setdecoding(pyodbc.SQL_WCHAR, encoding='utf-8')
cnxn.setencoding(encoding='utf-8')

Selecting Some Data

Select Basics

All SQL statements are executed using the cursor.execute() function. If the statement returns rows, such as a select statement, you can retrieve them using the Cursor fetch functions - fetchone(), fetchall(), fetchmany(). If there are no rows, fetchone() will return None; fetchall() and fetchmany() will both return empty lists.

cursor.execute("select user_id, user_name from users")
row = cursor.fetchone()
if row:
    print(row)

Row objects are similar to tuples, but they also allow access to columns by name:

cursor.execute("select user_id, user_name from users")
row = cursor.fetchone()
print('name:', row[1])          # access by column index
print('name:', row.user_name)   # or access by name

The fetchone function returns None when all rows have been retrieved.

while True:
    row = cursor.fetchone()
    if not row:
        break
    print('id:', row.user_id)

The fetchall function returns all remaining rows in a list. If there are no rows, an empty list is returned. (If there are a lot of rows, this will use a lot of memory. Unread rows are stored by the database driver in a compact format and are often sent in batches from the database server. Reading in only the rows you need at one time will save a lot of memory.)

cursor.execute("select user_id, user_name from users")
rows = cursor.fetchall()
for row in rows:
    print(row.user_id, row.user_name)

If you are going to process the rows one at a time, you can use the cursor itself as an iterator:

cursor.execute("select user_id, user_name from users"):
for row in cursor:
    print(row.user_id, row.user_name)

Since cursor.execute() always returns the cursor, you can simplify this even more:

for row in cursor.execute("select user_id, user_name from users"):
    print(row.user_id, row.user_name)

A lot of SQL statements don't fit on one line very easily, so you can always use triple quoted strings:

cursor.execute("""
               select user_id, user_name
                 from users
                where last_logon < '2001-01-01'
                  and bill_overdue = 'y'
               """)

or use line continuations (but don't forget a space at the end of each string):

cursor.execute("select user_id, user_name " \
               "from users " \
               "where last_logon < '2001-01-01' " \
                 "and bill_overdue = 'y'")

Parameters

ODBC supports parameters using a question mark as a place holder in the SQL. You provide the values for the question marks by passing them after the SQL:

cursor.execute("""
               select user_id, user_name
                 from users
                where last_logon < ?
                  and bill_overdue = ?
               """, '2001-01-01', 'y')

This is safer than putting the values into the string because the parameters are passed to the database separately, protecting against SQL injection attacks. It is also be more efficient if you execute the same SQL repeatedly with different parameters. The SQL will be prepared only once. (pyodbc only keeps the last statement prepared, so if you switch between statements, each will be prepared multiple times.)

The Python DB API specifies that parameters should be passed in a sequence, so this is also supported by pyodbc:

cursor.execute("""
               select user_id, user_name
                 from users
                where last_logon < ?
                  and bill_overdue = ?
               """, ['2001-01-01', 'y'])

Inserting Data

To insert data, pass the insert SQL to Cursor.execute(), along with any parameters necessary:

cursor.execute("insert into products(id, name) values ('pyodbc', 'awesome library')")
cnxn.commit()

cursor.execute("insert into products(id, name) values (?, ?)", 'pyodbc', 'awesome library')
cnxn.commit()

Note the calls to cnxn.commit(). You must call commit or your changes will be lost! When the connection is closed, any pending changes will be rolled back. This makes error recovery very easy, but you must remember to call commit.

Updating and Deleting

Updating and deleting work the same way, pass the SQL to execute. However, you often want to know how many records were affected when updating and deleting, in which case you can use the cursor.rowcount value:

cursor.execute("delete from products where id <> ?", 'pyodbc')
print(cursor.rowcount, 'products deleted')
cnxn.commit()

Since execute() always returns the cursor, you will sometimes see code like this. (Notice rowcount on the end.)

deleted = cursor.execute("delete from products where id <> 'pyodbc'").rowcount
cnxn.commit()

Note the calls to cnxn.commit(). You must call commit or your changes will be lost! When the connection is closed, any pending changes will be rolled back. This makes error recovery very easy, but you must remember to call commit.

Tips and Tricks

Quotes

Since single quotes are valid in SQL, use double quotes to surround your SQL:

deleted = cursor.execute("delete from products where id <> 'pyodbc'").rowcount

It's also worthwhile considering using 'raw' strings for your SQL to avoid any inadvertent escaping (unless you really do want to specify control characters):

cursor.execute("delete from products where name like '%bad\name%'") # Python will convert \n to 'new line'!
cursor.execute(r"delete from products where name like '%bad\name%'") # no escaping

Naming Columns

Some databases (e.g. SQL Server) do not generate column names for calculations, in which case you need to access the columns by index. You can also use the 'as' keyword to name columns (the "as user_count" in the SQL below).

row = cursor.execute("select count(*) as user_count from users").fetchone()
print('%s users' % row.user_count)

fetchval

If you are selecting a single value you can use the fetchval convenience method. If the statement generates a row, it returns the value of the first column of the first row. If there are no rows, None is returned:

maxid = cursor.execute("select max(id) from users").fetchval()

Most databases support COALESCE or ISNULL which can be used to convert NULL to a hardcoded value, but note that this will not cause a row to be returned if the SQL returns no rows. That is, COALESCE is great with aggregate functions like max or count, but fetchval is better when attempting to retrieve the value from a particular row:

cursor.execute("select coalesce(max(id), 0) from users").fetchone()[0]
cursor.execute("select coalesce(count(*), 0) from users").fetchone()[0]

However, fetchval is a better choice if the statement can return an empty set:

# Careful!
cursor.execute(
    """
    select create_timestamp
    from photos
    where user_id = 1
    order by create_timestamp desc
    limit 1
    """).fetchone()[0]

# Preferred
cursor.execute(
    """
    select max(updatetime), 0)
    from photos
    where user = 1
    order by create_timestamp desc
    limit 1
    """).fetchval()

The first example will raise an exception if there are no rows for user_id 1. The fetchone() call returns None. Python then attempts to apply [0] to the result (None[0]) which is not valid.

The fetchval method was created just for this situation - it will detect the fact that there are no rows and will return None.