Consider the following Python dictionary data and Python list labels:

data $=\{\text{'}$animal$\text{'}$: $[\text{'}$cat$\text{'},$ 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'$],$

'age': $[2\mathrm{.}5,3,0\mathrm{.}5,$ np$.$nan, $5,2,4\mathrm{.}5,$ np$.$nan, $7,3],$

'visits': $[1,3,2,3,2,3,1,1,2,1],$

'priority': $[\text{'}$yes$\text{'},$ 'yes', $\text{'}$no$\text{'},$ 'yes', $\text{'}$no$\text{'},\text{'}$no$\text{'},\text{'}$no$\text{'},$ 'yes', $\text{'}$no$\text{'},\text{'}$no$\text{'}]\}$

labels $=[\text{'}$a$\text{'},\text{'}$b$\text{'},\text{'}$c$\text{'},\text{'}$d$\text{'},\text{'}$e$\text{'},\text{'}$f$\text{'},\text{'}$g$\text{'},\text{'}$h$\text{'},\text{'}$i$\text{'},\text{'}$j$\text{'}]$

Calculate the mean age for each different animal in df$.$

Append a new row $\text{'}$k$\text{'}$ to df with your choice of values for each column. Then delete that row to return the original DataFrame.

Count the number of each type of animal in df$.$

Sort df first by the values in the 'age' in decending order, then by the value in the 'visits' column in ascending order $($so row i should be first, and row d should be last$).$

For each animal type and each number of visits, find the mean age. In other words, each row is an animal, each column is a number of visits and the values are the mean ages $($hint: use a pivot table$).$