Hi @artur.sannikov96 , once more thank you for your valuable comments! They have triggered me to dig deeper, learn a couple of new things while doing so, ask multiple questions on this forum, and make some improvements!
I uploaded an updated version in my original post (after learning how to do that in the first place, see this post).
Let me comment inline to each of your bullet points individually.
- It’s better to tell us what the surveys are about and make some examples (like the quality of education, safety, etc). You can also tell us your hypotheses about what you expect
- Make sure to tell the readers what are the project’s objectives
Point taken. I must admit that for this guided project I focused a bit more on the Python code (which I was struggling with) than on the storytelling part. I may improve it later still.
- You can limit the number of subheadings in the Data Preparation section but make sure that it’s still readable
Updated layout a bit. Also see my comment for the next bullet point though.
- You did not leave any code comment in the first section. Why?
The answer is in the Notebook, in a code cell that reads like this:
Note: the code that follows next in this section is courtesy Dataquest. That is, during ‘guided excercises’ I actually did write the code to perform all the steps done below, but for convenience, for this notebook I have here reused (uncommented) code that was provided by Dataquest rather than copying all of my own code snippets. Some minor changes were made.
Further down there will be another marker to indicate till where this applies. After that marker, all code was written by me.
I understand this may not be a good practice, it is however how Dataquest set up this guided project and it felt too time-consuming for me to change all the code to my own code.
- Did you find something interesting while exploring the correlation between the SAT score and other measures? Could you tell us about your findings?*
I think I did put observations below each graph and conclusions at the end, but sure I agree that my storytelling can be improved for this project, but for this guided project I focused a bit more on the Python code. (I may update it on a later occasion.)
- It’s better to import all the libraries in the first code cell to improve the project’s readability
Good to know!
- Your plots do not have titles, and axes labels. I would also increase the size of tick labels and rename them to something clearer. Also, the plot of SAT scores by boro is very crowded with narrow bars and it’s extremely difficult to understand what it communicates. You’d also better limit the number of colors and u see less intensive color palette to make the plot more accessible to the reader’s eyes.
Yes, creating good graphs isn’t easy I found, I spent quite some time on this for this project, and will definitely want to improve this further in the future. I have added titles now at least, some have axes labels. I agree that I should better use other colors. Note that the colors do have a function in the last chart. It is not a random coloring of bars, but I have 32 bars for 32 districts belonging to 5 boro’s, so to indicate which boro each distric belongs to, I used coloring and added a legend.
Creating this chart was actually a challenge I created for myself to better learn (1) aggregating data of dataframes (2) creating more complex charts using matplotlib. It definitely did its job in a sense that I learnt from it (with help, e.g. see this post), but also fully agree it can be further improved both in terms of layout as in expressing what we actually see.
- One of your images in the " Correlation of SAT scores with survey results" section does not load
Good catch! Solved now, with help of this post.
- I’d round values in tables to improve the readability
Improved (I hope)! With help of this post