Python visual in Power BI

[sm_accordion]

I have simple Python code that I run in the Jupyter editor:

import matplotlib.pyplot as plt
import seaborn as sns

data = [["a", 4], ["a", 5], ["b", 8], ["b", 9]]

df = pd.DataFrame(data, columns = ["x", "y"])

sns.violinplot(x = df["x"], y = df["y"])

which correctly produces the following visual:

In Power BI, I create the same table:

Insert a Python visual with the code:

but the result is not the same:

Anyone know what I am doing wrong here?  Why is it not the same graph as in the Jupyter notebook?  This graphs seems to be summing the data and not showing the distribution.

[dm-p · Accepted solution]

Hi @sm_accordion,

Power BI will typically group data by unique values. This can be mitigated by specifying Don't Summarize for your fields in the visual, e.g.:

This will produce a dataset like the following:

But, even this will consolidate rows if you have the same value repeated multiple times, e.g. x=a, y=4, and this will mess with your distribution. I've added a duplicate row to the above dataset and added a count to the table to show what happens:

If you truly want to have individual rows passed into your visual, you'll need to add unique value to each row of your dataset (such as an auto-incrementing int) to prevent grouping, so it looks something like this:

index	x	y
1	a	4
2	a	5
3	b	8
4	b	9
...	...	...

Here's the table representation now (note that count = 1):

If you want further details, I've produced a violin plot custom visual, and have written an article on how grouping/sampling needs to work in these situations. Hopefully this will be useful for you.

Good luck!

Daniel

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If my post helps, then please consider accepting as a solution to help other forum members find the answer more quickly 🙂

[sm_accordion]

Thanks a ton.  This solved the problem.  I appreciate the detailed response and for taking the time to read through the post.