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Fabric offers a variety of powerful and unique features that greatly improve the development and execution of database-centric tasks. In this section, we will highlight some of its unique features that will give you an idea of the enormous potential of Microsoft Fabric Notebook. Notable capabilities include advanced features such as seamless integration with Apache Spark, rich visualization, an interactive web coding environment, support for multiple programming languages, collaborative features, and simplified workflows for machine learning experiments and job development. Here we are going to introduce some of their unique features, you can experiment more with Microsoft Fabric notebooks and accelerate data preparation with Data Wrangler in Microsoft Fabric.
Use multiple languagesS
Multiple programming languages can be enabled in the notebook by specifying the language magic command at the beginning of the cell. In addition, the language of the cell can be changed using the language selector. For example…
I. The cell works with the language of the spark
ii. Cell works with HTML language
Or you can choose the default language from the notebook languages. Then all cells will work with the selected language.
Drag and drop to insert fragments
Using the scroll function in the notebook is a convenient way to read data in Lakehouse Explorer. This feature allows users to seamlessly import data by dragging and dropping various file formats, including text files, spreadsheets, images, and more. Subsequently, the notebook automatically generates the appropriate code snippet that facilitates data viewing and manipulation operations.
variable researcher
The built-in Microsoft Fabric notebook tool allows users to view a comprehensive list of variables in the current session, including their names, types, lengths, and values. As variables are defined in code cells, they are automatically reflected in the Variable Explorer. Moreover, by clicking on any column header, users can conveniently sort the variables within the table according to their respective properties. To access the Variables Explorer, users can go to the View tab on the notebook ribbon and select the Variables button.
display (df) function
display function display(df) Enables the transformation of SQL query results and Apache Spark data frames into visually appealing data representations. It can be applied to data frameworks built in PySpark and Scala, providing a convenient way to generate rich data visualizations for enhanced analysis and presentation purposes.
Data Wrangler
In the Data tab of the notebook ribbon, users can use the Data Wrangler drop-down query to access and browse the active Pandas DataFrames available for editing. By selecting the desired DataFrame from the drop-down menu, users can seamlessly open it in Data Wrangler, facilitating efficient data exploration and manipulation.
- Data Wrangler generates a descriptive overview of the DataFrame displayed in the summary pane, providing information on dimensions, missing values, and more. Selecting a column in the grid prompts the summary panel to update with column-specific descriptive statistics, and a quick view is available in the column headers.
- The Operations panel in Data Wrangler contains a searchable list of data cleaning steps. Users can easily access this panel to explore and use various operations to clean and transform their data.
- When a particular operation is selected in Data Wrangler, the resulting changes are automatically displayed in the screen grid, accompanied by the corresponding code shown in the panel below. Users can choose to use the previewed code by selecting “Apply” or “Discard” the changes.
- The toolbar above the Data Wrangler display grid provides functions for saving generated code. Users can copy the code to the clipboard or export it as a function in a notebook, close Data Wrangler and add the function to the code cell. Additionally, the updated DataFrame Data Wrangler display grid can be downloaded as a CSV file.
Check out our other blog Mastering Data Science with Microsoft Fabric: A tutorial for beginners to harness the power of Microsoft Fabric Notebook for machine learning experiments.
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