Power BI Masterclass - Python for Data Visualization in Power BI Part3

This video tutorial demonstrates how to create and customize Python visuals in Power BI. It covers importing necessary libraries, setting up a linear model plot, and customizing visuals using various options. The tutorial also explains how to use slicers and filters to interact with Python visuals in Power BI, highlighting the limitations of static images. The video encourages exploring Matplotlib and Seaborn documentation for more advanced visualizations.

What is the first step in creating a Python visual in Power BI?

• Customizing the visual appearance

• Running the Python script

• Selecting fields and ensuring they are not summarized

• Importing necessary Python packages

Which Python packages are imported to create a linear model plot in Power BI?

• TensorFlow and Keras

• NumPy and Pandas

• Matplotlib and Seaborn

• SciPy and Scikit-learn

How can you remove the trend line from a linear model plot in Python?

• Change the 'hue' parameter

• Set the 'fit_reg' parameter to True

• Set the 'fit_reg' parameter to False

• Remove the 'data' parameter

What parameter is used to color data points by category in a Python plot?

• color

• hue

• shade

• tint

Where can you specify the location of a legend in a Python plot?

• In the 'fit_reg' parameter

• In the 'data' parameter

• In the 'hue' parameter

• In the 'loc' parameter

How do Python visuals in Power BI interact with filters?

• They can filter other visuals but cannot be filtered

• They can be filtered but cannot filter other visuals

• They can be filtered and also filter other visuals

• They cannot be filtered at all

What is a limitation of Python visuals in Power BI?

• They cannot display data from multiple fields

• They cannot be used in reports

• They are static images and cannot filter other visuals

• They cannot be customized