Guided Project: Analyzing the Star Wars Dataset

[jesmaxavier]

Hi DQers!

Hope you are well! This is the project related to the Star Wars survey. I was trying to replicate some visuals like you would seem them in Power BI. The challenge was replicating the second-last visualization and getting something similar to what was in the article, and I think I did a good job

Let me know if you have any feedback for the same.
Link to last screen of mission: Learn data science with Python and R projects
Link to project:
Analysing Star Wars Survey.ipynb (1.4 MB)

Cheers

Click here to view the jupyter notebook file in a new tab

[israelogunmola]

Hi @jesmaxavier ,

Despite the fact that I have not attempted this project myself, I couldn’t help but get drawn into the compelling storytelling you have used in the course of this analysis. I had to get myself a drink, then sit through and read the project in entirety. Nicely crafted puns and phrases too :

Not so Long ago, In a Dataset
Clean the Data We Must…
Fan Force
Wait… I have seen that!
Quite the Character
Hans? Greedo? Who Knows?
May the force be with you (An appropriate way to conclude things).

YOUR CODE
Your code is understandable and easy to follow. Even without reading the comments, I found it so easy to understand what you were executing at each point. This is amazing!

SOME VISUALS CAUGHT MY EYE
I loved all your visualisations, especially the horizontal bar charts, good data ink ratio, and the central message in them stood out (Gestalt genius!!)

IF I MAY ASK
In the visualisation for fans demographic data. I was immediately drawn to the green color signifying prevalent groups (Males, Gen X, Lower middle and Bachelors), did you do this intentionally or was it a coincidence. If it was intentional, it worked, and you are more than a Gestalt genius, my friend!!

SUGGESTIONS
I slightly got distracted by some libraries that were being imported later along the code. I’d suggest importing all your libraries in one code cell, that way readers like me can know all the libraries you are employing at a glance.

I was once advised here that it could be beneficial to have a summary of your findings up at the top. However, your storytelling was so smooth that I believe anyone would have read the entire work either ways.

You initially mentioned

‘I think I did a good job’

You have never been more wrong. You did an amaaaaazing Job with great touch of originality!!! Definitely belongs in the community champion class! @Elena_Kosourova

[biengioichantroi]

The pie donut plus the barplot is what catches my eyes the most, please let me save this prj to reference the visualization for my other prj

[Elena_Kosourova]

We missed you with your awesome projects in our Community, @jesmaxavier ! It’s cool that you’re back, and you’ve done a really great job, as usual! Thank you, @israelogunmola, for drawing my attention to this project, and also many thanks for providing such thoughtful and informative feedback on it!

[jesmaxavier]

@israelogunmola thank you for your kind words. They are a motivation to do more.

@israelogunmola

IF I MAY ASK
In the visualisation for fans demographic data. I was immediately drawn to the green color signifying prevalent groups (Males, Gen X, Lower middle and Bachelors), did you do this intentionally or was it a coincidence.

It was intentional. I just wanted to give a quick picture of what that definition of a typical Star Wars fan is.

#Set color scheme for donut plots
gender_color = ['#007A33' if gender_percent[i]==gender_percent.max() else '#6fb9e6' for i in range(len(gender_percent))]
age_color = ['#007A33' if age_percent[i]==age_percent.max() else '#6fb9e6' for i in range(len(age_percent))]
income_color = ['#007A33' if income_percent[i]==income_percent.max() else '#6fb9e6' for i in range(len(income_percent))]
education_color = ['#007A33' if education_percent[i]==education_percent.max() else '#6fb9e6' for i in range(len(education_percent))]

This bit of code is what highlights the maximum value for each characteristic of a Star Wars fan.

@israelogunmola

I slightly got distracted by some libraries that were being imported later along the code. I’d suggest importing all your libraries in one code cell, that way readers like me can know all the libraries you are employing at a glance.

I was once advised here that it could be beneficial to have a summary of your findings up at the top.

• With regard to the library mentions, that is my bad. I missed removing those while cleaning up.
• I have been advised the same. It has been, however, difficult for me to get into that mindset. I see these analysis projects as stories. Every minor analysis is like an event in that story. So I wonder whether it is right to give the conclusion right at the beginning. However, that is my take. It is a preference, I guess.

Thank you for your in-depth review. Sincerely appreciate you taking the time to go through my work.

You are doing amazing work with your projects and your reviews. Hope to see more of it, and I appreciate your recommendation.

Cheers!

[jesmaxavier]

@biengioichantroi glad you like it and happy to know that my project could contribute to your own. You could download the same using the download button in the link in my post. Check the highlight in the image

Cheers!

[jesmaxavier]

Thanks @Elena_Kosourova!

It has been a while, hasn’t it? I have been swamped. I shall have to do a better job managing work.
Cheers!