i need a python code to cluster the animals in this table $,$first we need to transform non numeric features to numeric ones $($use other method than one hot encoding$),$ standardize the data $,$plot the elbow graph $($and make sure that it looks like an elbow please $)$ and find the optimal number of clusters, $$print the clusters output $($the animals contained in $)$ and label the clusters, the clusters must realistic as much as possible foe example you can't put a sheep and a cheetah in the same cluster.

and the kmeans algorith can't be imported from sklearn library $,$ it has to be written as a function within the code.

Given a table of $30$ animals and these information about them: Weight $($kg$),$Top Speed $($km$/$h$),$Lifespan $($years$),$ Social Behavior, Diet and Habitat. assume you are part of an organizing team to design a large open$-$range Zoo for the animals.The zoo consists of sections; each section includes a group $($i$.$e$.$ cluster$)$ of animals. These sections will feature

expansive open$-$air houses, catering to either a single species or multiple species. The zoo could include multiple water bodies, wooded areas $...$etc.The ultimate goal is to craft a zoo that appropriate for the animals' physical and mental well$-$being while

simultaneously attractive to our visitors.

Your tasks are:

$1-$Cluster the animals into groups to assist in designing the Park.

$2-$Label each cluster with proper name i$.$e$.$large funny animals$.$

$3-$To achieve this, write a python program to find the optimum clustering for the animals listed in the

table. The python program performs the following tasks:

o Read animal from the Table $($Use Panda library$),$ Write a function for K$-$means algorithm.

o Apply K$-$means algorithm repeatedly with different K$$s$.$

o Plot the elbow graph. Find optimum clustering. Give meaningful labels to the clusters.$\backslash$table$[[,$Animal,Weight $($kg$),\backslash$table$[[$Top Speed$],[(k\frac{m}{h})$