FAQ: Learn Seaborn Introduction - Aggregating by Multiple Columns

Frequently Asked Questions Python FAQ
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This community-built FAQ covers the “Aggregating by Multiple Columns” exercise from the lesson “Learn Seaborn Introduction”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Data Science

Data Visualization in Python

FAQs on the exercise Aggregating by Multiple Columns

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2

There seems to be an issue with a few of the exercises in this Seaborn module.

Each time I write the sns.barplot() code, I sends back that it’s wrong and also returns the following error:

/usr/local/lib/python3.5/dist-packages/scipy/stats/stats.py:1633: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq] . In the future this will be interpreted as an array index, arr[np.array(seq)] , which will result either in an error or a different result. return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval {“passed”: true}

Even when I ask the Solution, copy it, restart the exercise, and paste the solution given, I get the same error.

I even paste my code to my own editor and run it on my computer and it works just fine. It seems to be just when running it in codecademy that I get a problem

2 Likes
3

Having same problem!

4

Can you post a link to the lesson?

5

I’m having the same problem as well, in for example this excercise:

https://www.codecademy.com/paths/data-science/tracks/data-visualization/modules/dspath-seaborn/lessons/learn-seaborn-introduction/exercises/seaborn-hue

6

Still a problem

https://www.codecademy.com/paths/data-science/tracks/data-visualization/modules/dspath-seaborn/lessons/learn-seaborn-introduction/exercises/seaborn-hue
Same as an earlier exercise.

Three months since first reported. Come on chaps, let’s have it fixed

1 Like
7

On the previous lessons I noticed an import that ignores that error. I had the same problem until I copied the import.

import warnings
warnings.filterwarnings('ignore')

Hope this helps!