Guided Project: Exploring eBay Car Sale Data

All feedback is welcome.

Ebay+Car+Sale+Analysis+Project+2.ipynb (167.3 KB)

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Hi!

I’ve enjoy reading your project, good job! I’ve recently finished same one, so I was very interested how you did all the tasks.

It’s very well structured and I like all your explanations .However I think it would be better if you would keep your code cell shorter(I mean split it for few extra) For me it was sometimes hard to follow your code and output.

In[5:] I’m not sure why are you overcomplicating by writing a function instead just apply new names as a dictionary.

In[15:] You can calculate top8 like this: sum(autos['brand'].value_counts().head(8)) I would avoid typing data manually, since if you data change your calculation will be wrong.
Same for Percent_of_total_cars : sum(autos['brand'].value_counts(normalize=True).head(8)) use normalize=True for percentages.

7.1 I like your comment about Pareto Principle, very interesting!

In[16:] I would use simpler code, you don’t have to use all elif.

top8 = autos['brand'].value_counts().index[:8]
mean_price = {} 
for brand in top8:
    selected_rows = autos[autos['brand'] == brand] 
    price = selected_rows['price'].mean() 
    mean_price[brand] = int(price) `

In[18]: Don’t use print when you want to show table at the end.

Hope it makes sense what I wrote and it will be usefull for you!

Good luck!

Thank you Basti for your great feedback! Much appreciated!
This is all very new to me. It’s a challenge to remember the key differences between, Series, DataFrame, Dictionary, … and so on.
I appreciate feedback to help me continue learning.
Regards,
Bruce

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