![]() ![]() Let’s search for Python SQL driver (pyodbc) module and install it for the Notebook: In the result, it gives the package summary and version information: ![]() In the following screenshot, we search for “idna” Pip package. Click on Add new and search for specific Pip module: We can search for any specific Pip package as well. We use this for local Python development:Ĭlick on Manage Packages, and you can see a list of installed Pip packages: We can also see Attach to is localhost for the Python3 kernel. You can see kernel: Python 3 in SQL Notebook after installation: It installs the Python and starts notebook Python kernel: It also shows the commands for installation of Python kernel: It downloads the required package and starts the installation for Notebooks. You should have an active internet connection for downloading the software: We can see that the Python installer size is 144.21 MB. It logs the installation in the task window on Azure Data Studio. Let’s choose the default option New Python installation and click on the Install button at the bottom. Use existing Python installation: If we have an existing Python on the server, we can browse to Python directory and use existing installation.You can see an information message as well in the middle of the Python configuration page It takes some time for downloading and installs Python. New Python installation: If we do not have an existing Python installation, we can choose this option, and Azure Data Studio does Python installation for us.We get two options for Python installation: Once we change the selection to Python 3, it gives the following option for configuring Python for Notebooks: Let’s change the kernel from SQL to Python 3 in SQL Notebook. You should also explore the Python articles and be familiar with the Python queries. You might think – Why should we worry about the Python programming language? If yes, go through this article: Why would a SQL Server DBA be interested in Python? PowerShell: We can write PowerShell code using PowerShell kernel.Python 3: We can use Python code for connecting with SQL Server and execute queries.Spark Scala and Spark R: We can use scala code using spark compute from a cluster.PySpark: We can use this for writing Python code using spark compute from a cluster.In the kernel list, we see following kernels apart from SQL: By default, it launches SQL kernel for executing T-SQL queries for SQL Server. It launches SQL Notebook, as shown below. Right-click on a SQL instance and from the context menu choose New Notebook: Connect to a SQL instance in Azure Data Studio. Let’s create a new notebook for this article. A handy SQL Notebook for the purposes of troubleshooting in Azure Data Studio.SQL Notebook in SQL Notebooks introduction and overview.You should explore the following articles before going through this article: It is gaining popularity among database administrators and developers. SQL Notebook is an exciting feature of Azure Data Studio. This article explores the Python SQL scripts in SQL Notebook of Azure Data Studio. ![]()
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