I would like to share my third Guided Project to get feedback from the community.
I spend over a month on this project, I think. While working, I discovered that Python methods such as ‘.groupby()’ provide a lot of options that should make data analysis much easier and faster, so I didn’t try to write complex code, but rather dive into the coding logic
Also, I discovered that there is a ‘.corr()’ method for calculating correlations, so I’m going to look into that as well instead of writing long lines of code.
And since I’m sure in fact, I would use any mechanism for rendering and visualisation, for example, Preset\Superset, I didn’t try to write long code for charts either.
However, I tried my best and really learned a lot during this project.
I hope to get a few words of criticism to improve my path in Python).
Exploring_eBay_Car_Sales_Cleaned.ipynb (566.2 KB)
Click here to view the jupyter notebook file in a new tab
Hi! Thank you for sharing your project!
I didn’t have much time to look into it, but I really liked that you were using quite a lot of things other than what you have learned so far here on Dataquest, which we’ll be necessary in a working environment as well. So you are on the right track
Something I noticed is that you created a function to convert strings to datetime objects. If you check
df.info(), you’ll see that your columns weren’t actually converted (they are still object types, rather than datetime64). Unfortunately, I couldn’t figure out why your function doesn’t work, but pandas actually has a function
pd.to_datetime() which converts strings to datetime type easily.
Thanks for your feedback! I really put more effort into using whatever I knew rather than the following instructions. I can even say that I was surprised when I got to the last pages of the lesson and found that I had already done by myself what we are asking.
About these data time objects. I also noticed this strange fact. Of course, I tried to understand and fix it. Then I had a situation where Pandas and Numpy consider these objects to be not strings, but data-time objects. So, I left them as such, since I could operate on them as data-time objects.
About the pd.to_datetime() function, thanks. Python is really something, it provides functions, methods and little tricks to get the result in a nutshell instead of writing long complicated queries. It is the genius invention.
It’s difficult to deal with datetime objects, isn’t it? I’m redoing this guided project at the moment and I’m really struggling with it