In the Python script editor, under Paste or type your script code here, enter this code: import matplotlib.pyplot as pltĭot(kind='scatter', x='Age', y='Weight', color='red') To get a larger view of the visualizations, you can minimize the Python script editor.Ĭreate a scatter plot to see if there's a correlation between age and weight. For error details, select See details in the message. When you run a Python script that results in an error, the Python visual isn't plotted, and an error message appears on the canvas. Power BI Desktop replots the visual when you select Run from the Python script editor title bar, or whenever a data change occurs due to data refresh, filtering, or highlighting. For example, you can code dataset in your Python script to access the age field. You can access columns in the dataset by using their names. In those cases, you can add an index field to your dataset that causes all rows to be considered unique and prevents grouping. In some cases, you might not want automatic grouping to occur, or you might want all rows to appear, including duplicates. As you select or remove fields from the Values section, supporting code in the Python script editor is automatically generated or removed. Power BI Desktop automatically detects field changes. You can add or remove fields while you work on your Python script. Your Python script can use only fields that are added to the Values section. When the script is complete, select the Run icon from the Python script editor title bar to run the script and generate the visual. With the dataframe automatically generated by the fields you selected, you can write a Python script that results in plotting to the Python default device.
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