Noe - Project II

[niniet98]

https://github.com/niniet98/PROJECT-II

[sh-ih]

You did great on this project! Some comments:

• Repository You have a very organized repository with a great folder structure. And each folder contains the expected files (pictures in the images folder, graphs on the figures folder, etc). It is also great that your files have clear and informative names, it makes navigating your repository a very pleasant process.

• Acquisition and enrichment of database Your use of comments and titles/subtitles on your notebooks is a very good habit to have. Keep coding like that.

Merging the budget data from wikipedia with the information from Pixar creates a very interesting dataset. I would only suggest you to be even more specific when working with money data. The information from wikipedia about budgets and revenue has million as part of the column names, but it would be better to have the currency explicitly shown, maybe million USD, or even a comment added to your jupyter cell telling everyone: “all financial figures are in million of US dollars”.

The same information should be added to your graphs, the y axis of your graph Relationship between budget and amount of characters & assets” says “Budget”, but a better axis is: “Budget (in millions US dollars)”.

Also it would be interesting to analyze the relationship of those two variables with a scatterplot, where the x axis is the number of characters, instead of the names of the movies, to really see the relationship with the budget.

• Reporting You have your analysis and conclusions mainly on your README, but it is important that you include that too in the notebook that will work as your final report, in this case it could be the visualization.ipynb or you could even create a notebook just for the final report, where you can develop your storytelling. You have the opportunity to write more in depth analysis and conclusions there and just keep the most attractive points in the README.

• README You have a clear and well organized README. It is a great idea to accompany your analysis of the data with visualizations, to make it even more powerful.

• Bonus: modularization/encapsulation I see that you created all your .py files with your functions. The next step would be to import them to your notebooks. Then you can modify your notebooks to call those modules to use the functions.