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Data Cleaning Walkthrough: Analyzing and Visualizing the Data Course

In page no 7. we are arriving at the following,

“Our research on the last screen revealed that most of the high schools with low total enrollment and low SAT scores have high percentages of English language learners. This indicates that it’s actually ell_percent that correlates strongly with sat_score , rather than total_enrollment . To explore this relationship further, let’s plot out ell_percent vs sat_score .”

How did we conclude that most of the high schools with low total enrollment and low SAT scores have high percentages of English language learners?
I do not get it.
Because in the previous screen we get a list of 12 schools with low enrollment only.

Hi @brindhaganesan25, I recall doing this mission before but I can’t quite remember all the necessary details in order to answer your question. Can you please provide a link to the mission so that I can get a bit more context? Also, you may want to read this post for other tips and tricks for asking technical questions in the community.

Hello @mathmike314 Thank you for your response and I’m sorry for the vague question. I will have a look at the post you shared.

Here is the link to the mission.

https://app.dataquest.io/m/138/data-cleaning-walkthrough%3A-analyzing-and-visualizing-the-data/7/plotting-language-learning-percentage

No worries! I was able to manually find the mission by using the title of your post and now I remember what this was all about.

The code we used on this screen (or something very similar) is shown below:

low_enrollment = combined[(combined['total_enrollment']<1000) & (combined['sat_score']<1000)]
print(low_enrollment['SCHOOL NAME'])

which will give us a list of schools with low total_enrollment (under 1000) and low sat_score (also under 1000) but it doesn’t tell us anything about English language learners…so where are they/we getting this from?! I remember being very frustrated about this as well!

The answer is: it comes from our research on Wikipedia and Google! As per the last instruction on this screen:

  • Use Wikipedia and Google to research the names of the schools. Can you discover anything interesting about them?

Upon completing the research, the “interesting” thing we are meant to discover is that these schools:

91                  INTERNATIONAL COMMUNITY HIGH SCHOOL
125                 ACADEMY FOR LANGUAGE AND TECHNOLOGY
126                     BRONX INTERNATIONAL HIGH SCHOOL
139               KINGSBRIDGE INTERNATIONAL HIGH SCHOOL
141               INTERNATIONAL SCHOOL FOR LIBERAL ARTS
176    PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE
179                       HIGH SCHOOL OF WORLD CULTURES
188                  BROOKLYN INTERNATIONAL HIGH SCHOOL
225       INTERNATIONAL HIGH SCHOOL AT PROSPECT HEIGHTS
237                          IT TAKES A VILLAGE ACADEMY
253                           MULTICULTURAL HIGH SCHOOL
286              PAN AMERICAN INTERNATIONAL HIGH SCHOOL
Name: SCHOOL NAME, dtype: object

all have a high enrollment for ELL students.

I know it’s not very satisfying but I hope this at least clarifies the screen for you.

Happy coding!

Ah ok. I did print School Name and ell_percent columns in low_enrollment together now and they indeed have high ell_percent.
But I don’t think I would have arrived at this discovery myslef without them mentioning it. I hope this ability comes with practice!

print(low_enrollment[[‘School Name’, ‘ell_percent’]])

Thank you! Happy Coding to you too :slight_smile:

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Did you actually look up every school in the list and read about them? I know I didn’t the first time I did this mission and therefore completely missed the connection.

I think if you were doing something like this on your own, you’d find the connection between the schools. When I was doing this mission, I was more focused on the code and not so much on the “soft skills” required for this screen.

Everything comes with time. Just keep at it and it will become easier :smiley:

EDIT_1:
Also, noticing that many of the schools have “INTERNATIONAL” in their name should have been a clue :man_facepalming:

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