Data Wrangler: A Data Analysis Tool in Microsoft Fabric

Microsoft Fabric offers an end-to-end analytics Software-as-a-Service platform. An integral tool in this platform is the Data Wrangler, highly useful for data scientists. 

What Is Microsoft Fabric?

Microsoft Fabric is a comprehensive data analytics platform supporting all services related to data analytics. This includes activities such as data integration, storage, data warehousing, data engineering, business intelligence, and data science.

What is Data Wrangler?

Data Wrangler is a tool in Fabric designed for data scientists. It enables users to work with data, cleaning, grouping, and aggregating it. This tool can be utilised to transform data, prepare, and even generate Python code for larger data analytics projects.

How Does Data Wrangler Work?

Data Wrangler connects to a data table and you can load data into a dataframe using pandas, a popular Python library for data manipulation. Once data is loaded into a dataframe, you can launch Data Wrangler in the notebook and choose your dataframe for it.

Data Wrangler: The Experience

The Data Wrangler editor has several areas to assist with data preparation: 

1. Data preview: You can check changes after applying any operation.
2. Data quality and profiling summary for each column.
3. Detailed data quality and profiling for selected column or table.
4. Data transformations.
5. Cleaning steps or applied operations in order.
6. The Python code for the selected step.

Data Wrangler is user-friendly and further customization is possible by changing parameters in the code. The generated Python code can be integrated into a notebook making it part of a larger project.

Power Query Editor Vs Data Wrangler

While Power BI's Power Query Editor is not replaced by Data Wrangler, there are differences. Power Query Editor provides a richer graphical interface with many data transformations. However, Power Query Editor generates M script while Data Wrangler generates Python code.

The Power Query Editor is geared toward citizen data analysts while Data Wrangler is aimed more at data scientists.

In conclusion, Data Wrangler simplifies the Python code writing process for data cleaning and preparation. It may not be as robust in transformation power as Power Query Editor, but its use in data science operations and the fact that it generates Python code makes it handy for data scientists. The choice between these tools depends on specific data analytics scenarios.