
        
        
            
                
                
                    import pandas as pd
df = pd.DataFrame({'A':[3,4], 'B':['a','b']})
df
                
            
               A  B
0  3  a
1  4  b

To connect with the PostgreSQL database, we must use the create_engine(~) method of the sqlalchemy library:


        
        
            
                
                
                    from sqlalchemy import create_engine
engine = create_engine('postgresql://postgres@localhost:5432/test_db')

Here:

we are connecting to a PostgreSQL database called test_db, which is hosted on our local machine (localhost) on the default port 5432.
The user that we log in as is the default superuser postgres.

WARNING

If you are using a Mac and run into problems when connecting with the database, please consult our troubleshooting sectionlink here.

NOTE

To connect with your PostgreSQL server with a password or to connect with a remote server, please consult the guide here.

To save our DataFrame as a table called my_table in our PostgreSQL database, use the to_sql(~) method of the DataFrame:


        
        
            
                
                
                    df.to_sql('my_table', con=engine)

If a table with the name 'my_table' already exists, the above will throw an error. To replace the existing my_table instead:


        
        
            
                
                
                    df.to_sql('my_table', con=engine, if_exists='replace')

To confirm that our table has been created:


        
        
            
                
                
                    SELECT * FROM my_table;
                
            
             index | A | B 
-------+---+---
     0 | 3 | a
     1 | 4 | b
(2 rows)

Published by Isshin Inada

Edited by 0 others

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