My 1st Dashboard :)

 

15 buildings, 35 aspects per building, each weighted according to importance, and then condensed into 6 key categories, with colour coding depending on how the results tumble out.

What helped me get the final result?

• Pandas
  • to import data from spreadsheet
  • extract only the data I need
  • manipulate it so we end up with scores (0 – 1) instead of qualitative assessments like “in progress”
  • finally pivot the data into a summarized format
• Matplotlib.GridSpec
  • to draw a grid into which I place the graphs
  • to easily achieve the correct spacing and placement between graphs
• Matplotlib.PyPlot
  • a function to draw each graph and colour-code the bars instead of writing my code 15 times – don’t laugh, I’m really proud of this part: it was challenging for me!
  • and then to draw the actual graphs
• Normal Python
  • for everything else like making sure the graphs appear in descending order of building size

What was difficult about it?

I ended up with different lists, dictionaries, and dataframes for different aspects of the project – the most challenging thing at the beginning was wrapping my head around extracting a value from one source, and passing it to another source as a selection criterion.

What was useful in overcoming the difficulties?

• In trying to make my code as generic as possible I quickly discovered the merits of .iloc() over .loc() – especially in my for loops!
• For a df with index names df1.index[0] is useful for getting the index names without referring to them by name!
• Which would then allow me to get the column number based on the named index value in a related dataframe: df2.columns.get_loc(df1.index[0])
• And this would allow me to extract values based on numeric locations: df2.columns.values[df1[0]]
• And then zip was great for quickly building a dictionary from 2 lists: my_dict = dict(zip(my_list1, my_list2))