Load zacksfc$\_$example.xlsx excel file.

b$)$ Drop every column that is all nan values.

c$)$ Count how many columns have been dropped. $($see on far right$)$

d$)$ Calculate $\%$ of removed columns. $($see on far right$)$

e$)$ Get a list of all column names from the dropped df and extract $[\text{'}$ticker$\text{'},$ 'comp$\_$name', 'per$\_$type', 'sic$\_$code', 'filing$\_$date', 'tot$\_$oper$\_$exp', 'oper$\_$income'$]$ columns only to a new DataFrame.

f$)$ Filter column per$\_$type so only returns Q $.$ Save as a DataFrame. $($see new$\_$filtered$\_$data below$)$

g$)$ Now filter if 'tot$\_$oper$\_$exp $>10000$ and oper$\_$income $>10000.$ Return a DataFrame. $($see new$\_$filtered$\_$data$2$ below$)$

h$)$ Calculate sum and average of 'tot$\_$oper$\_$exp', 'oper$\_$income$($see print below$)$

i$)$ Sort data on column tot$\_$oper$\_$exp. See col$\_$sort below.

j$)$ Do a test calculation calculate total$\_$oper$\_$exp $$ oper$\_$income and add a column called test$\_$calc. Also put this calculation into a function and add column called test$\_$calc$\_$fun. $($see print below$)$

k$)$ Transpose this new filtered DataFrame. $($see transposed$\_$data below$)$