Connecting Pandas to a Database with SQLAlchemy

In this article, we will discuss how to connect pandas to a database and perform database operations using SQLAlchemy.

The first step is to establish a connection with your existing database, using the create_engine() function of SQLAlchemy.

Syntax:

from sqlalchemy import create_engine

engine = create_engine(dialect+driver://username:password@host:port/database)

Explanation:

• dialect – Name of the DBMS
• driver – Name of the DB API that moves information between SQLAlchemy and the database.
• Username, Password – DB User credentials
• host: port – Specify the type of host and port number.
• Database – Database name

Connecting Pandas to a Database with SQLAlchemy

Syntax: pandas.DataFrame.to_sql(table_name, engine_name, if_exists, index)

Explanation:

• table_name – Name in which the table has to be stored
• engine_name – Name of the engine which is connected to the database
• if_exists – By default, pandas throws an error if the table_name already exists. Use ‘REPLACE’ to replace this dataset with the old one or “APPEND” to add the data to the existing table.
• index – (bool), Adds index column to the table that identifies each row uniquely.

For this example, we can use a PostgreSQL database, which is one of the easiest ways to do things, but then the procedure is just the same for all the other databases supported by SQLAlchemy. You can download the sample dataset here.

Let us first Import the necessary dataset. Now, let’s Establish the connection with the PostgreSQL database and make it interactable to python using the psycopg2 driver. Next, we shall load the dataframe to be pushed to our SQLite database using the to_sql() function as shown. 

Output:

This will create a table named loan_data in the PostgreSQL database.

Connecting a table to PostgreSQL database

Converting a PostgreSQL table to pandas dataframe

Like we did above, we can also convert a PostgreSQL table to a pandas dataframe using the read_sql_table() function as shown below. Here, let us read the loan_data table as shown below.

Syntax: pandas.DataFrame.read_sql_table(table_name, con = engine_name, columns)

Explanation:

• table_name – Name in which the table has to be stored
• con – Name of the engine which is connected to the database
• columns – list of columns that has to be read from the SQL table

Output:

Postgresql table read as a dataframe using SQLAlchemy

Passing SQL queries to query table data

We can also pass SQL queries to the read_sql_table function to read-only specific columns or records from the PostgreSQL database. The procedure is still the same. The SQL syntax remains the same as a conventional syntax to query data from a SQL table. The below example shows how to get all records of loan_data table using SQL query.

Output:

Postgresql table read as a dataframe using SQLAlchemy