Problem
A [polar plot](https://en.wikipedia.org/wiki/Polar_coordinate_system) can be used for visualizing high-dimensional data.
One particularly useful way to use a polar plot is for visualizing clusters. Demonstrate how to make a polar plot in Python using Matplotlib. In particular, use the Iris and Wine data available from Scikit.
```python
from sklearn.datasets import load_iris
from sklearn.datasets import load_wine
def get_wine():
X, y = load_wine(return_X_y=True, as_frame=True)
Xy = X.assign(y=y)
return Xy
def get_iris():
X, y = load_iris(return_X_y=True, as_frame=True)
Xy = X.assign(y=y) \
.rename(columns={
'sepal length (cm)': 's_length',
'sepal width (cm)': 's_width',
'petal length (cm)': 'p_length',
'petal width (cm)': 'p_width'
})
return Xy
```
A snippet of the Iris data looks like the following.
```
| | s_length | s_width | p_length | p_width | y |
|---:|-----------:|----------:|-----------:|----------:|----:|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | 0 |
| 1 | 4.9 | 3 | 1.4 | 0.2 | 0 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | 0 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | 0 |
| 4 | 5 | 3.6 | 1.4 | 0.2 | 0 |
```
A snippet of the wine data looks like the following.
```
| | alcohol | malic_acid | ash | alcalinity_of_ash | magnesium | total_phenols | flavanoids | nonflavanoid_phenols |
|---:|----------:|-------------:|------:|--------------------:|------------:|----------------:|-------------:|-----------------------:|
| 0 | 14.23 | 1.71 | 2.43 | 15.6 | 127 | 2.8 | 3.06 | 0.28 |
| 1 | 13.2 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 |
| 2 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.8 | 3.24 | 0.3 |
| 3 | 14.37 | 1.95 | 2.5 | 16.8 | 113 | 3.85 | 3.49 | 0.24 |
| 4 | 13.24 | 2.59 | 2.87 | 21 | 118 | 2.8 | 2.69 | 0.39 |
```