Hi all,
I am uploading a project on Star Wars Survey. It was a very interesting project for me to work on as I have never seen any Star Wars series. At least I know something about this famous series now :smiley: that too being from Physics grimacing: Hope I have done justice to all Star Wars series fans.

Please get back to me with any kind of suggestion/s.

Star Wars Survey: Data Science Project — Next Steps | Dataquest

Star Wars Survey.ipynb (539.1 KB)


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Hello @Bhagyashree ,
So great to see your shared “Star Wars Survey” project.

  1. I noticed your comments for each line of code were not regular till cells 16 downwards.
  2. It is good practice to import all modules at the beginning of your analysis rather than as seen in your code like so:

#importing modules for the project
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import as style
%matplotlib inline

  1. Visualizations are great and explanatory but remember we design for everyone and there is need to extract your insight at first glance. This is regarding colouration of columns to attract attention to where you want exactly.
  2. I find your conclusion well worded and interesting.
    Great analysis you did there.
1 Like

Hi @Ezemonye_Omereji,
Thanks for the useful comments. Let me address the comments one by one.

  1. In my earlier projects I had commented on each line of code and also given separate explanations for each cell. Later it was pointed out by reviewers that, it is not necessary to comment on each code. So, I am little confused about 1) which code/codes I should comment on and 2) should I comment on codes if I explain the analysis/program I’m going to perform beforehand.
  2. I will keep in mind to import all modules at the beginning from now onwards.
  3. I need to spend some more time in understanding data visualizations. And also try using seaborn in the upcoming projects. Thanks for mentioning this point. I should try making graphs more insightful.

Thanks for all the comments,


Hi @Bhagyashree,

Code commenting is a bit subjective thing, so there are no strict rules. In my opinion, the best strategy is to keep balance and also take into account the technical proficiency of the audience who would potentially read your project in the future. This regards also your own level of proficiency since you’ll be, most probably, one of those people who will need to re-read this project in the future. In general, avoid too obvious comments (like # Import the necessary libraries or # Print the results) and focus on commenting on more advanced pieces of code.

should I comment on codes if I explain the analysis/program I’m going to perform beforehand.

Here I’d suggest you to use markdown cells not for technical explanations (e.g., Now, we're going to use this or that pandas method:) but rather for strategically planning your analysis (e.g., Now, we're going to find the TOP5 popular car brands:) or commenting on your results, whether final or intermediate ones (e.g., As we see on the above graph...).

Hope it was helpful. Happy learning! :rocket: