Write in Python:

a$)$ Reshape your dataframe so that you have columns of columns. Your outer set of columns should be the building, your inner set of columns should be the utility, and your rows should be the date.

Example data $=\{$

$($Date$)$: $[2024-01-01,2024-01-02,2024-01-03,2024-01-04,2024-01-05],$

$($Building$)$: $[100,100,120,100,120],$

$($Utility$1)$: $[5,33333877\mathrm{.}00293,11111,-,$ NoneType object

$,$ nan$],$

$($Utility$2)$: $[$NaN$,6,6667733877\mathrm{.}00293,22,2\mathrm{.}4,000\mathrm{.}20],$

$($Utility$3)$: $[2,333300,250,-,220,$ NoneType object

$],$

$($Utility$4)$: $[15,17,16,18,15]$

$\}$

b$)$ To exammine the impact of the newly opened Building $100$ of task a$)$ calculate the average utility use as a running average in $2-$day increments, $3-$day increments, and $4-$day increments, across the span of data. In what time period do you think this new addition may have opened? Keep in mind that such projects may not have a single date. Plot your solutions appropriately.

c$)$ In separate plots for each building, plot the utility $1$ use and the utility$2$ use in the same plot.