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PostgreSQL Python tutorial

This is a Python programming tutorial for the PostgreSQL database. It covers the basics of PostgreSQL programming with the Python language. You might also want to check the Python tutorial or PostgreSQL PHP tutorial on ZetCode.

Several libraries exist for connecting to the PostgreSQL database from the Python language. In this tutorial we will use the psycopg2 module. It is a PostgreSQL database adapter for the Python programming language. According to the module documentation it is currently the most popular Python module for the PostgreSQL database. It is mostly implemented in C as a libpq wrapper.

About PostgreSQL database

PostgreSQL is a powerful, open source object-relational database system. It is a multi-user database management system. It runs on multiple platforms including Linux, FreeBSD, Solaris, Microsoft Windows and Mac OS X. PostgreSQL is developed by the PostgreSQL Global Development Group.

Prerequisites

To work with this tutorial, we must have Python language, PostgreSQL database and psycopg2 language binding installed on our system.

$ sudo apt-get install postgresql

On an Ubuntu based system we can install the PostgreSQL database using the above command.

$ sudo update-rc.d -f postgresql remove
 Removing any system startup links for /etc/init.d/postgresql ...
   /etc/rc0.d/K21postgresql
   /etc/rc1.d/K21postgresql
   /etc/rc2.d/S19postgresql
   /etc/rc3.d/S19postgresql
   /etc/rc4.d/S19postgresql
   /etc/rc5.d/S19postgresql
   /etc/rc6.d/K21postgresql

If we install the PostgreSQL database from packages, it is automatically added to the start up scripts of the operating system. If we are only learning to work with the database, it is unnecessary to start the database each time we boot the system. The above command removes any system startup links for the PostgreSQL database.

$ /etc/init.d/postgresql status
Running clusters: 9.1/main

$ service postgresql status
Running clusters: 9.1/main 

We check if the PostgreSQL server is running. If not, we need to start the server.

$ sudo service postgresql start
 * Starting PostgreSQL 9.1 database server        [ OK ]

On Ubuntu Linux we can start the server with the service postgresql start command.

$ sudo service postgresql stop
[sudo] password for janbodnar: 
 * Stopping PostgreSQL 9.1 database server        [ OK ] 

We use the service postgresql stop command to stop the PostgreSQL server.

$ sudo apt-get install python-psycopg2

Here we install the psycopg2 module on a Ubuntu system.

$ sudo -u postgres createuser janbodnar
Shall the new role be a superuser? (y/n) n
Shall the new role be allowed to create databases? (y/n) y
Shall the new role be allowed to create more new roles? (y/n) n

We create a new role in the PostgreSQL system. We allow it to have ability to create new databases. A role is a user in a database world. Roles are separate from operating system users. We have created a new user without the -W option, e.g. we have not specified a password. This enables us to connect to a database with this user without password authentication. Note that this works only on localhost.

$ sudo -u postgres createdb testdb -O janbodnar

The createdb command creates a new PostgreSQL database with the owner janbodnar.

Version

In the first code example, we will get the version of the PostgreSQL database.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

try:
     
    con = psycopg2.connect(database='testdb', user='janbodnar') 
    cur = con.cursor()
    cur.execute('SELECT version()')          
    ver = cur.fetchone()
    print ver    
    

except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

In the above Python script we connect to the previously created testdb database. We execute an SQL statement which returns the version of the PostgreSQL database.

import psycopg2

The psycopg2 is a Python module which is used to work with the PostgreSQL database.

con = None

We initialize the con variable to None. In case we could not create a connection to the database (for example the disk is full), we would not have a connection variable defined. This would lead to an error in the finally clause.

con = psycopg2.connect(database='testdb', user='janbodnar') 

The connect() method creates a new database session and returns a connection object. The user was created without a password. On localhost, we can omit the password option. Otherwise, it must be specified.

cur = con.cursor()
cur.execute('SELECT version()')   

From the connection, we get the cursor object. The cursor is used to traverse the records from the result set. We call the execute() method of the cursor and execute the SQL statement.

er = cur.fetchone()

We fetch the data. Since we retrieve only one record, we call the fetchone() method.

print ver   

We print the data that we have retrieved to the console.

except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)

In case of an exception, we print an error message and exit the script with an error code 1.

finally:
    
    if con:
        con.close()) 

In the final step, we release the resources.

$ ./version2.py 
('PostgreSQL 9.3.5 on i686-pc-linux-gnu, compiled by gcc 
    (Ubuntu 4.8.2-19ubuntu1) 4.8.2, 32-bit',)

Running the version.py script.

Inserting data

We will create a Cars table and insert several rows to it.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

try:
     
    con = psycopg2.connect("dbname='testdb' user='janbodnar'")   
    
    cur = con.cursor()
  
    cur.execute("CREATE TABLE Cars(Id INTEGER PRIMARY KEY, Name VARCHAR(20), Price INT)")
    cur.execute("INSERT INTO Cars VALUES(1,'Audi',52642)")
    cur.execute("INSERT INTO Cars VALUES(2,'Mercedes',57127)")
    cur.execute("INSERT INTO Cars VALUES(3,'Skoda',9000)")
    cur.execute("INSERT INTO Cars VALUES(4,'Volvo',29000)")
    cur.execute("INSERT INTO Cars VALUES(5,'Bentley',350000)")
    cur.execute("INSERT INTO Cars VALUES(6,'Citroen',21000)")
    cur.execute("INSERT INTO Cars VALUES(7,'Hummer',41400)")
    cur.execute("INSERT INTO Cars VALUES(8,'Volkswagen',21600)")
    
    con.commit()
    

except psycopg2.DatabaseError, e:
    
    if con:
        con.rollback()
    
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

The above script creates a Cars table and inserts 8 rows into the table.

cur.execute("CREATE TABLE Cars(Id INTEGER PRIMARY KEY, Name VARCHAR(20), Price INT)")

This SQL statement creates a new Cars table. The table has three columns.

cur.execute("INSERT INTO Cars VALUES(1,'Audi',52642)")
cur.execute("INSERT INTO Cars VALUES(2,'Mercedes',57127)")

These two lines insert two cars into the table.

con.commit()

The changes are committed to the database.

if con:
    con.rollback()

In case of an error, we roll back any possible changes to our database table.

$ psql testdb
psql (9.3.5)
Type "help" for help.

testdb=> SELECT * FROM Cars;
 id |    name    | price  
----+------------+--------
  1 | Audi       |  52642
  2 | Mercedes   |  57127
  3 | Skoda      |   9000
  4 | Volvo      |  29000
  5 | Bentley    | 350000
  6 | Citroen    |  21000
  7 | Hummer     |  41400
  8 | Volkswagen |  21600
(8 rows)

We verify the written data with the psql tool.

We are going to create the same table. This time using the convenience executemany() method.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


cars = (
    (1, 'Audi', 52642),
    (2, 'Mercedes', 57127),
    (3, 'Skoda', 9000),
    (4, 'Volvo', 29000),
    (5, 'Bentley', 350000),
    (6, 'Citroen', 21000),
    (7, 'Hummer', 41400),
    (8, 'Volkswagen', 21600)
)

con = None

try:
     
    con = psycopg2.connect("dbname='testdb' user='janbodnar'")   
  
    cur = con.cursor()  
    
    cur.execute("DROP TABLE IF EXISTS Cars")
    cur.execute("CREATE TABLE Cars(Id INT PRIMARY KEY, Name TEXT, Price INT)")
    query = "INSERT INTO Cars (Id, Name, Price) VALUES (%s, %s, %s)"
    cur.executemany(query, cars)
        
    con.commit()
    

except psycopg2.DatabaseError, e:
    
    if con:
        con.rollback()
    
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

This script drops a Cars table if it exists and (re)creates it.

cur.execute("DROP TABLE IF EXISTS Cars")
cur.execute("CREATE TABLE Cars(Id INT PRIMARY KEY, Name TEXT, Price INT)")

The first SQL statement drops the Cars table if it exists. The second SQL statement creates the Cars table.

query = "INSERT INTO Cars (Id, Name, Price) VALUES (%s, %s, %s)"

This is the query that we will use.

cur.executemany(query, cars)

We insert 8 rows into the table using the convenience executemany() method. The first parameter of this method is a parameterized SQL statement. The second parameter is the data, in the form of tuple of tuples.

Retrieving data

Now that we have inserted some data into the database, we want to retrieve it back.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

try:
     
    con = psycopg2.connect("dbname='testdb' user='janbodnar'") 
    
    cur = con.cursor()    
    cur.execute("SELECT * FROM Cars")

    rows = cur.fetchall()

    for row in rows:
        print row
    

except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

In this example, we retrieve all data from the Cars table.

cur.execute("SELECT * FROM Cars")

This SQL statement selects all data from the Cars table.

rows = cur.fetchall()

The fetchall() method gets all records. It returns a result set. Technically, it is a tuple of tuples. Each of the inner tuples represent a row in the table.

for row in rows:
    print row

We print the data to the console, row by row.

$ ./fetch1.py
(1, 'Audi', 52642)
(2, 'Mercedes', 57127)
(3, 'Skoda', 9000)
(4, 'Volvo', 29000)
(5, 'Bentley', 350000)
(6, 'Citroen', 21000)
(7, 'Hummer', 41400)
(8, 'Volkswagen', 21600)

This is the output of the example.

Returning all data at a time may not be feasible. We can fetch rows one by one.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

try:
     
    con = psycopg2.connect("dbname='testdb' user='janbodnar'") 
    
    cur = con.cursor()     
    cur.execute("SELECT * FROM Cars")

    while True:
      
        row = cur.fetchone()
        
        if row == None:
            break
            
        print row[0], row[1], row[2]
    

except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

In this script we connect to the database and fetch the rows of the Cars table one by one.

while True:

We access the data from the while loop. When we read the last row, the loop is terminated.

row = cur.fetchone()

if row == None:
    break

The fetchone() method returns the next row from the table. If there is no more data left, it returns None. In this case we break the loop.

print row[0], row[1], row[2]

The data is returned in the form of a tuple. Here we select records from the tuple. The first is the Id, the second is the car name and the third is the price of the car.

$ ./retrieveonebyone.py
1 Audi 52642
2 Mercedes 57127
3 Skoda 9000
4 Volvo 29000
5 Bentley 350000
6 Citroen 21000
7 Hummer 41400
8 Volkswagen 21600

This is the output of the example.

The dictionary cursor

The default cursor returns the data in a tuple of tuples. When we use a dictionary cursor, the data is sent in a form of Python dictionaries. This way we can refer to the data by their column names.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import psycopg2.extras
import sys


con = None

try:
     
    con = psycopg2.connect("dbname='testdb' user='janbodnar'") 
    
    cursor = con.cursor(cursor_factory=psycopg2.extras.DictCursor)
    cursor.execute("SELECT * FROM Cars")
    
    rows = cursor.fetchall()

    for row in rows:
        print "%s %s %s" % (row["id"], row["name"], row["price"])
   

except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

In this example, we print the contents of the Cars table using the dictionary cursor.

import psycopg2.extras

The dictionary cursor is located in the extras module.

cursor = con.cursor(cursor_factory=psycopg2.extras.DictCursor)

We create a DictCursor.

for row in rows:
    print "%s %s %s" % (row["id"], row["name"], row["price"])

The data is accessed by the column names. The column names are folded to lowercase in PostgreSQL (unless quoted) and are case sensitive. Therefore, we have to provide the column names in lowercase.

Parameterized queries

Now we will concern ourselves with parameterized queries. When we use parameterized queries, we use placeholders instead of directly writing the values into the statements. Parameterized queries increase security and performance.

The Python psycopg2 module supports two types of placeholders. Ansi C printf format and Python extended format.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

uId = 1
uPrice = 62300 

try:
     
    con = psycopg2.connect("dbname='testdb' user='janbodnar'") 
    
    cur = con.cursor()

    cur.execute("UPDATE Cars SET Price=%s WHERE Id=%s", (uPrice, uId))        
    con.commit()
    
    print "Number of rows updated: %d" % cur.rowcount
    
   

except psycopg2.DatabaseError, e:
    
    if con:
        con.rollback()
    
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

We update a price of one car. In this code example, we use the question mark placeholders.

cur.execute("UPDATE Cars SET Price=%s WHERE Id=%s", (uPrice, uId))

The characters (%s) are placeholders for values. The values are added to the placeholders.

print "Number of rows updated: %d" % cur.rowcount

The rowcount property returns the number of updated rows. In our case one row was updated.

$ ./parameterized1.py
Number of rows updated: 1

testdb=> SELECT * FROM cars WHERE id=1;
 id | name | price 
----+------+-------
  1 | Audi | 62300
(1 row)

The price of the car was updated. We check the change with the psql tool.

The second example uses parameterized statements with Python extended format.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

uid = 3

try:
     
    con = psycopg2.connect("dbname='testdb' user='janbodnar'") 
    
    cur = con.cursor()

    cur.execute("SELECT * FROM Cars WHERE Id=%(id)s", {'id': uid } )
    
    print cur.fetchone()
   

except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

We select a name and a price of a car using pyformat parameterized statement.

cur.execute("SELECT * FROM Cars WHERE Id=%(id)s", {'id': uid } )

The named placeholders start with a colon character.

$ ./parameterized2.py
(3, 'Skoda', 9000)

Output of the example.

Inserting images

In this section, we are going to insert an image to the PostgreSQL database. Note that some people argue against putting images into databases. Here we only show how to do it. We do not talk about technical issues of whether to save images in databases or not.

testdb=> CREATE TABLE Images(Id INT PRIMARY KEY, Data BYTEA);

For this example, we create a new table called Images. For the images, we use the BYTEA data type. It allows to store binary strings.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


def readImage():

    try:
        fin = open("woman.jpg", "rb")
        img = fin.read()
        return img
        
    except IOError, e:

        print "Error %d: %s" % (e.args[0],e.args[1])
        sys.exit(1)

    finally:
        
        if fin:
            fin.close()


try:
    con = psycopg2.connect(database="testdb", user="janbodnar") 
    
    cur = con.cursor()
    data = readImage()
    binary = psycopg2.Binary(data)
    cur.execute("INSERT INTO Images(Id, Data) VALUES (1, %s)", (binary,) )

    con.commit()    
    
except psycopg2.DatabaseError, e:

    if con:
        con.rollback()

    print 'Error %s' % e    
    sys.exit(1)
    
finally:
    
    if con:
        con.close()   

In this script, we read an image from the current working directory and write it into the Images table of the PostgreSQL testdb database.

try:
    fin = open("woman.jpg", "rb")
    img = fin.read()
    return img

We read binary data from the filesystem. We have a JPG image called woman.jpg.

binary = psycopg2.Binary(data)

The data is encoded using the psycopg2 Binary object.

cur.execute("INSERT INTO Images(Id, Data) VALUES (1, %s)", (binary,) )

This SQL statement is used to insert the image into the database.

Reading images

In this section, we are going to perform the reverse operation. We will read an image from the database table.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


def writeImage(data):
    
    try:
        fout = open('woman2.jpg','wb')
        fout.write(data)
    
    except IOError, e:    
        print "Error %d: %s" % (e.args[0], e.args[1])
        sys.exit(1)
        
    finally:
        
        if fout:
            fout.close()  


try:
    con = psycopg2.connect(database="testdb", user="janbodnar") 
    
    cur = con.cursor()    
    cur.execute("SELECT Data FROM Images LIMIT 1")
    data = cur.fetchone()[0]
    
    writeImage(data)
    
except psycopg2.DatabaseError, e:

    print 'Error %s' % e    
    sys.exit(1)
    
finally:
    
    if con:
        con.close()      

We read image data from the images table and write it to another file, which we call woman2.jpg.

try:
    fout = open('woman2.jpg','wb')
    fout.write(data)

We open a binary file in a writing mode. The data from the database is written to the file.

cur.execute("SELECT Data FROM Images LIMIT 1")
data = cur.fetchone()[0]

These two lines select and fetch data from the Images table. We obtain the binary data from the first row.

Metadata

Metadata is information about the data in the database. Metadata in a PostgreSQL database contains information about the tables and columns, in which we store data. Number of rows affected by an SQL statement is a metadata. Number of rows and columns returned in a result set belong to metadata as well.

Metadata in PostgreSQL can be obtained using from the description property of the cursor object or from the information_schema table.

Next we will print all rows from the Cars table with their column names.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

try:
     
    con = psycopg2.connect("dbname='testdb' user='janbodnar'") 
    
    cur = con.cursor()
 
    cur.execute('SELECT * FROM Cars')
    
    col_names = [cn[0] for cn in cur.description]
    
    rows = cur.fetchall()
    
    print "%s %-10s %s" % (col_names[0], col_names[1], col_names[2])

    for row in rows:    
        print "%2s %-10s %s" % row
    
   

except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

We print the contents of the Cars table to the console. Now, we include the names of the columns too. The records are aligned with the column names.

col_names = [cn[0] for cn in cur.description]

We get the column names from the description property of the cursor object.

print "%s %-10s %s" % (col_names[0], col_names[1], col_names[2])

This line prints three column names of the Cars table.

for row in rows:    
    print "%2s %-10s %s" % row

We print the rows using the for loop. The data is aligned with the column names.

$ ./colnames.py 
id name       price
 2 Mercedes   57127
 3 Skoda      9000
 4 Volvo      29000
 5 Bentley    350000
 6 Citroen    21000
 7 Hummer     41400
 8 Volkswagen 21600
 1 Audi       62300

Output.

In the following example we will list all tables in the testdb database.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

try:
     
    con = psycopg2.connect(database='testdb', user='janbodnar') 
    
    cur = con.cursor() 
    cur.execute("""SELECT table_name FROM information_schema.tables 
       WHERE table_schema = 'public'""")    
        
    rows = cur.fetchall()

    for row in rows:
        print row[0]
    
   
except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

The code example prints all available tables in the current database to the terminal.

cur.execute("""SELECT table_name FROM information_schema.tables 
    WHERE table_schema = 'public'""") 

The table names are stored inside the system information_schema table.

$ ./list_tables.py
cars
images

These were the tables on our system.

Export and import of data

We can export and import data using copy_to() and copy_from() methods.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None
fout = None


try:
     
    con = psycopg2.connect(database='testdb', user='janbodnar') 
    
    cur = con.cursor()
    fout = open('cars', 'w')
    cur.copy_to(fout, 'cars', sep="|")                        
   

except psycopg2.DatabaseError, e:
    print 'Error %s' % e    
    sys.exit(1)

except IOError, e:    
    print 'Error %s' % e   
    sys.exit(1)
    
finally:
    
    if con:
        con.close()

    if fout:
        fout.close() 

In the above example, we copy the data from the Cars table into the cars file.

fout = open('cars.sql','w')

We open a file where we will write the data from the Cars table.

cur.copy_to(fout, 'cars', sep="|")   

The copy_to method copies data from the Cars table to the opened file. The columns are separated with a | character.

$ cat cars
2|Mercedes|57127
3|Skoda|9000
4|Volvo|29000
5|Bentley|350000
6|Citroen|21000
7|Hummer|41400
8|Volkswagen|21600
1|Audi|62300

These are the contents of the cars file.

Now we are going to perform a reverse operation. We will import the dumped table back into the database table.

testdb=> DELETE FROM Cars;
DELETE 8

We delete the data from the Cars table.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None
f = None

try:
     
    con = psycopg2.connect(database='testdb', user='janbodnar') 
    
    cur = con.cursor()
    f = open('cars', 'r')
    cur.copy_from(f, 'cars', sep="|")                    
    con.commit()
   
except psycopg2.DatabaseError, e:
    
    if con:
        con.rollback()
    
    print 'Error %s' % e    
    sys.exit(1)

except IOError, e:    

    if con:
        con.rollback()

    print 'Error %s' % e   
    sys.exit(1)
    
finally:
    
    if con:
        con.close()

    if f:
        f.close()  

In this script, we read the contents of the cars file and copy it back to the cars table.

f = open('cars', 'r')
cur.copy_from(f, 'cars', sep="|")                    
con.commit()

We open the cars file for reading and copy the contents to the Cars table. The changes are committed.

SELECT * FROM Cars;
 id |    name    | price  
----+------------+--------
  2 | Mercedes   |  57127
  3 | Skoda      |   9000
  4 | Volvo      |  29000
  5 | Bentley    | 350000
  6 | Citroen    |  21000
  7 | Hummer     |  41400
  8 | Volkswagen |  21600
  1 | Audi       |  62300
(8 rows)

The output shows that we have successfully recreated the saved Cars table.

Transactions

A transaction is an atomic unit of database operations against the data in one or more databases. The effects of all the SQL statements in a transaction can be either all committed to the database or all rolled back.

In psycopg2 module transactions are handled by the connection class. The first command of a connection cursor starts a transaction. (We do not need to enclose our SQL commands by BEGIN and END statements to create a transaction. This is handled automatically by psycopg2.) The following commands are executed in the context of this new transaction. In case of an error, the transaction is aborted and no further commands are executed until the rollback() method.

The documentation to the psycopg2 module says that the connection is responsible to terminate its transaction, calling either the commit() or rollback() method. Committed changes are immediately made persistent into the database. Closing the connection using the close() method or destroying the connection object (using del or letting it fall out of scope) will result in an implicit rollback() call.

The psycopg2 module also supports an autocommit mode, where all changes to the tables are immediately effective. To run in autocommit mode, we set the autocommit property of the connection object to True.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

try:
     
    con = psycopg2.connect(database='testdb', user='janbodnar') 
    
    cur = con.cursor() 

    cur.execute("DROP TABLE IF EXISTS Friends")
    cur.execute("CREATE TABLE Friends(Id serial PRIMARY KEY, Name VARCHAR(10))")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Tom')")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Rebecca')")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Jim')")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Robert')")
    
    #con.commit()
       
except psycopg2.DatabaseError, e:
    
    if con:
        con.rollback()
        
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

We create a Friends table and try to fill it with data. However, as we will see, the data will be not committed.

#con.commit()

The commit() method is commented. If we uncomment the line, the data will be written to the table.

finally:
    
    if con:
        con.close()

The finally block is always executed. If we have not committed the changes and no error occures (which would roll back the changes) the transaction is still opened. The connection is closed with the close() method and the transaction is terminated with an implicit call to the rollback() method.

testdb=> \dt
          List of relations
 Schema |  Name   | Type  |   Owner   
--------+---------+-------+-----------
 public | cars    | table | janbodnar
 public | friends | table | janbodnar
 public | images  | table | janbodnar
(3 rows)

Only after we have uncommented the line, the Friends table is created.

In the autocommit mode, an SQL statement is executed immediately.

#!/usr/bin/python
# -*- coding: utf-8 -*-

import psycopg2
import sys


con = None

try:
     
    con = psycopg2.connect(database='testdb', user='janbodnar') 
    
    con.autocommit = True
    
    cur = con.cursor() 

    cur.execute("DROP TABLE IF EXISTS Friends")
    cur.execute("CREATE TABLE Friends(Id serial PRIMARY KEY, Name VARCHAR(10))")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Jane')")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Tom')")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Rebecca')")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Jim')")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Robert')")
    cur.execute("INSERT INTO Friends(Name) VALUES ('Patrick')")
    
      
except psycopg2.DatabaseError, e:
            
    print 'Error %s' % e    
    sys.exit(1)
    
    
finally:
    
    if con:
        con.close()

In this example, we connect to the database in the autocommit mode. We do not call neither commit() nor rollback() methods.

con.autocommit = True

We set the connection to the autocommit mode.

$ ./autocommit.py

testdb=> SELECT * FROM Friends;
 id |  name   
----+---------
  1 | Jane
  2 | Tom
  3 | Rebecca
  4 | Jim
  5 | Robert
  6 | Patrick
(6 rows)

The data was successfully committed to the Friends table.

This was the PostgreSQL Python tutorial.