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1. #PYTHON SQL SERVER CONNECTION STRING INSTALL#
2. #PYTHON SQL SERVER CONNECTION STRING UPDATE#
3. #PYTHON SQL SERVER CONNECTION STRING CODE#

The 'connect' function returns a sqlalchemy engine object, which we can again use in our Jupyter notebook to query the SQL database. We use the getpass library to hide the password from view. Now we can prompt the user for a valid SQL username and password for the database directly in our Jupyter notebook. 'DATABASE=' + params + ' ' \ĭb = create_engine( 'mssql+pyodbc:///?odbc_connect=%s' % p)

#PYTHON SQL SERVER CONNECTION STRING UPDATE#

We also update the database.py file by replacing the db object with a 'connect' function, which takes a username and password as parameters.

Make sure you substitute your own values for the 'DATABASE', 'UID', and 'PWD' parameters, or you won't be able to connect.

Below we define the connection string and parse it into the necessary format for pyodbc using the urllib library. For Python beginners, the simplest way to provide that string is to define it directly in the notebook. To connect to a SQL Server via ODBC, the sqlalchemy library requires a connection string that provides all of the parameter values necessary to (1) identify the database and (2) authenticate and authorize the user. Now that we have the initial imports out of the way, we can establish our first database connection. The pyodbc library can be tricky to install, so a bit of light googling might also be required.

#PYTHON SQL SERVER CONNECTION STRING INSTALL#

If you do not already have these packages installed, you can install them using pip. If you are curious, sqlalchemy's 'create_engine' function can leverage the pyodbc library to connect to a SQL Server, so we import that library, as well. The sqlalchemy engine works well with pandas data frames, so we will use those libraries to perform our SQL queries below. Getting Startedīefore we do anything, we will need to install some third-party Python packages to help us establish and use our connections.

#PYTHON SQL SERVER CONNECTION STRING CODE#

We will start with the least secure method - hardcoding credentials into the connection code - because it is easy to understand, but we will build toward our ultimate goal, a connection that is both secure and easy to use. We will also assume that there is an existing SQL user with all of the permissions required to access the database. For purposes of this tutorial, we will assume the database is stored on a Microsoft SQL Server, but the connection process should be about the same no matter what type of database management system you are using. Here we explore some methods for establishing a connection to a SQL database using Python in a Jupyter notebook. With the pandas library, extracting data from a SQL database in a Jupyter notebook is almost trivial, but before we can extract the data, we need to establish a connection to the database. SQL is everywhere, and if you are doing any sort of analysis in an enterprise setting, it is more likely than not that you will need to access a SQL database for at least some of your data.