Rajendra Gupta
Sample notebook

Learn Jupyter Notebooks for SQL Server

February 25, 2020 by


The Jupyter notebook is a powerful and interactive tool that supports various programming languages such as Python, R, Julia. This open-source utility is popular among data scientists and engineers. This notebook integrates both code and text in a document that allows you to execute code, view visualization, solve mathematical equations.

We have covered SQL Notebooks in the Azure Data Studio in the following articles:

SQL Notebook is a version or reference from the Jupyter notebook. In this article, we will take an overview of the Jupyter notebook.

Getting started with the Jupyter notebook

Jupyter word is derived from the popular programming languages – Julia, Python, and R.

Jupyter word

We can start by going through the project Jupyter website.

Jupyter Notebook URL

Scroll down, and you get options to install Jupyter lab in your machine or test it in your browser. It is an interactive development environment for the notebook.

JupyterLab 1.0

Try it in your browser

It gives you an option to try in the web browser without installing locally. Let’s click on this link. It gives you various options such as try the classic notebook, try JupyterLab, try Jupyter with Julia.

Try it in your browser

Let’s go with JupyterLab that provides a new interface for the Jupyter notebooks. It takes some time to load JupyterLab. It provides documentation for the JupyterLab as shown below.

Sample notebook

We will not focus on the online Jupyter lab in this article. You can explore it using the available documentation.

Install the Notebook locally

On the homepage of the Jupyter webpage, click on Install the notebook. It opens a documentation page and gives you two different ways to install it.

  • Install using the Python’s package manager utility pip
  • Install using the Anaconda distribution

In the article, Use Python SQL scripts in SQL Notebooks of Azure Data Studio, we use Python in SQL Notebooks. It would be easy for you as well to correlate the things so we will install Jupyter notebook using Python.

Open Azure Data Studio and a new SQL notebook. In this notebook, switch to kernel Python. First, upgrade the pip utility using the following command and restart the Azure Data Studio.

Install and upgrade PIP utlity

The following command installs the Jupyter lab with pip utility.

It downloads the required packages, dependencies for your system. If the requirement is already satisfied, you get messages like requirements already satisfied.

Install Jupyter Lab locally

Once the set up completes, you can see a message that it successfully installed Jupyter lab.

Success message

In the next step, install the sqlachemy library as well. We will use it to connect to the SQL Server database. It simplifies the connection string format for us. We can use it connecting with a different database such as Microsoft SQL Server, Oracle, PostgreSQL, MySQL.

I already installed it for my demonstration, and hence the following output.

Install sqlalchemy module

Launch Jupiter notebook

Once we installed it, go to start and launch jupyter-lab.exe. It is a good practice to right-click on it and launch using administrative rights.

Launch Jupyter notebook

It opens an administrative command prompt and loads the necessary configurations. This command prompt is open throughout the Jupyter session. We can view the essential messages on this command prompt while we work with Jupyter notebook. It also shows any error message.

Loads necessary configuration

It launches the Jupyter notebook web page installed locally as shown below.

Homepage of Jupyter notebook

In this notebook, we can see various options.

  • Notebook: Python 3, PowerShell, PySpark, Spark R, Spark Scala
  • Console: Python 3, PowerShell, PySpark, Spark R, Spark Scala
  • Other: Terminal, text file, Markdown file and Show contextual help

Let’ s launch Python 3 notebook.

Python 3 notebook

By default, this notebook is named Untitled. We can give it a descriptive name. We can do it by moving the mouse over the word Untitled and rename the notebook. We can give it a name as per our requirement.

Jupyter notebooks also use Markdown language like a SQL Notebook in Azure Data Studio. You can refer to Azure Data Studio to learn this Markdown language.

In this article, we will execute SQL queries in the Jupyter notebook. It requires an ODBC system DSN pointing to SQL instance. Let’s create a DSN connection in the next step.

Create a system DSN for SQL Server

We require a system DSN ODBC connection to prepare a DB connection with SQL Server. Open ODBC from the start, and it opens ODBC Data Source Administrator (64-bit) as shown below.

Launch ODBC and click on System DSN.

System DSN

Enter the DSN connection name and SQL instance details.

Enter DSN connection name and SQL instance

Choose an authentication method (Windows NT authentication) or SQL Server authentication. For SQL Server authentication, enter a username and password. Please ensure the user is having enough permission for executing queries in the database.

Specify authentication method

Skip the configuration on the next screen. We can change the default database if required.

change the default database

Click on the finish on the next page.

Finish ODBC configuraiton

Review the ODBC configuration and test data source.

Test Data Source

It connects to SQL Server using the credentials and instance details.

Successful test message

Click Ok and close the ODBC configuration page. At this point, for this demo, we have a data source SQL for a database connection.

Connect Jupyter Notebook to SQL Server instance

Now, let’s prepare a connection string using sqlalchemy package.

  • Connection format : mssql+pyodbc://user:password@mydsn

For my demo, the connection string for SQL Server is as follows.

We also need to load the iPython SQL extension for SQL queries. In the python notebook, execute the following commands.

Click on the play icon to make a connection. Once it connects to SQL Server, it shows a connected message along with the username. It shows none in output because we did not specify any database in the connection string.

Connect with SQL Server

Execute a single line SQL query

We can execute SQL queries now in this Jupyter notebook. We need to specify %sql for the SQL query. If we directly execute a query, it gives the syntax error message.

Execute a single line SQL query

Now, execute the query with %SQL prefix.

Execute a single line SQL query notebook

Execute a multiline SQL query

if we split the same query into multiple lines, we get a similar error invalid syntax error message.

Execute a multiline SQL query

We can add %sql in multiple lines as well, but it does not work also. You get multiple invalid syntax errors this time.

Error message

For multiline queries, we can use a %% prefix. It makes the entire code block as a single code block.

Run the following code in the notebook.

Use %%sql for multi line query

Use Variable in SQL query for Jupyter Notebook

We can use a variable as well to execute a multiline query. In the following query, we specified the query in db_query variable and enclosed query in quotes.

Then call this variable along with %sql variable.

You get the query output as shown below.

Use Variable in SQL query for Jupyter Notebook

Query parameter and variable for SQL query

We can also use variables for query parameters as well. Suppose in the previous query, we want to retrieve the record for a specific condition. We can use the WHERE clause for defining a condition. We can use the following way to define where clause variable in Jupyter notebook.

Query parameter and variable for SQL query

Data frame result and control the output

We can capture the result in a variable and control the results using a data frame. The below select statement returns the top 5 rows from the [Person].[Person] table.

Next, we execute SQL query and get the result in the data frame.

Now let’s see how it helps us to control data in the output.

To view all records, we can use the query result for the print full data set, as shown below.

Data frame result and control the output

Now suppose we want to retrieve 3rd row from the output. The first row starts with the result [0] therefore, for 3rd row, use result[2] in the query.

Retrieve s specific column data

We can further filter results for a specific column. Let’s say we want to retrieve the 5th column value for the 3rd row. We execute the following code for it.

Result [2][4]

View specific column data

Create graphs using a matplotlib python library

We can use Matplotlib in Python to create various charts such as bar column, scatter, line, image. We can use this Matplolib in the Jupyter notebook as well. We will create a bar and pie chart for our sample data.

First, install the matplotlib library using the following PIP command. Here we specify –user to install matplotlib for logged in user. If we want it for all users, we can remove this parameter.

Create graphs using MATPLOTLIB python library

The following code fetches the output of the select statement in a result data frame.

We can call this data frame with the required chart such as pie and bar.

Pie Chart

Execute result.pie()for a pie chart.

Execute SQL query and view bar chart

Bar Chart

Similarly, execute result.bar() for a bar chart from the same data set.

View column chart


In this article, we explored the Jupyter Notebook which is a popular tool among data scientists. We also connected it with SQL Server and executed SQL queries. We can also visualize results set in various charts like pie, bar, scatter, etc. Try exploring the Jupyter notebook along with the SQL notebook in Azure Data Studio.

Rajendra Gupta
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