Daily_exchange_rates_per_euro

Hello all,

I would love some feedback on my guided project, thanks for your time :slightly_smiling_face:.

https://app.dataquest.io/c/96/m/529/guided-project%3A-storytelling-data-visualization-on-exchange-rates/7/next-steps

daily_exchange_rates_per_euro.ipynb (357.7 KB)

Click here to view the jupyter notebook file in a new tab

@rudythealchemist good work :+1: on completing your project. The following are a few tips I have to hopefully improve the same.

Presentation Style
  • When you feel that you are done with the project re-run the entire project so that the cells start their number from 1.
    image
  • Include a proper introduction and conclusion section. They are especially useful to readers who want a quick overview of your project and don’t have the time to read.
  • You could replace the Basic Facts section with exchange_rates.info() which gives a more detailed table on the details of the dataframe. You could also include a title by emboldening using escape codes. Check this for the same. This could help to differentiate your code from your output
  • Its best to avoid short forms like Col, non-technical readers may be confused by what it means.
  • While you have included titles but their size could be increased by using #
    image
  • Since you are trying to present a story it would be good to include some context to your graph. You could do some research as to why the the crisis caused the rates to both increase and decrease. If there are bigger factors at play, they same could be presented to the user. Either way readers would be curious to get more detail from you the analyst.
Coding Style
  • You seem to have commented on each step trying to explain what you are trying to do. This can be avoided in its entirety in the final iteration (i.e. when you clean up this project and put this out). Your current comments should help during that review. In the final version you could put down simple comments like #Calculate the rolling means for cell [145] so its helpful to both dev and non-dev readers.
  • In your final iteration, take note to remove comments that were meant to be code, just so that the code looks clean. e.g
Bugs/Inaccuracies
  • I could not find any issues but I haven’t gone too deep in to your code.
Miscellaneous
  • In your plot you could add more details like the highest and lowest point during the crisis and during the period that did not involve the crisis.
  • You could include ax.hline() to highlight the average during this period after you’ve identified the average during the entire timeline from 2006-10

Hope that helps. Keep going :man_running:, you are on track.