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Exporting Pandas DataFrame to PostgreSQL table

schedule Aug 12, 2023
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Consider the following DataFrame:

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)
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Published by Isshin Inada
Edited by 0 others
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