Bug in `.ww.describe()` that stems from the computation of `percentile`: will either give KeyError or return the wrong value.

## The problem

There is a `.iat` missing in the definition of `percentile` that is used by `.ww.describe()` (see [here](https://github.com/alteryx/woodwork/blob/e2f0adadd9371858f0838f264f3fd755040c8b31/woodwork/statistics_utils/_get_describe_dict.py#L45)).

Reproducible example, using `woodwork==0.31.0`

This demonstrates the bug:
```
import pandas as pd
df = pd.DataFrame({'a':[1, 2, 3, 4, 5]})
df['new_index'] = [3, 4, 5, 6, 7] # Note that for this example, the 1st index value can't be 1. Use any other integer.
df.set_index('new_index', inplace=True)
import woodwork as ww
df.ww.init()
df.ww.describe()
```
The error is `KeyError: 1` because it's trying to look up index 1 in the `a` Series. (Here I've set it to `3`).

The bug comes from the woodwork definition of percentile for a pandas series:
```
import math
def percentile(N, percent, count):
"""
Source: https://stackoverflow.com/questions/2374640/how-do-i-calculate-percentiles-with-python-nump

Find the percentile of a list of values.

Args:
N (pd.Series): Series is a list of values. Note N MUST BE already sorted.
percent (float): float value from 0.0 to 1.0.
count (int): Count of values in series

Returns:
Value at percentile.
"""
k = (count - 1) * percent
f = math.floor(k)
c = math.ceil(k)
if f == c:
return N[int(k)] # <-- the stack overflow question was written for lists, and this line needs to be modified for pd.Series.
d0 = N.iat[int(f)] * (c - k)
d1 = N.iat[int(c)] * (k - f)
return d0 + d1

```
Run this example by setting `series = df['a']` and running `percentile()` directly.
```
series = df['a']
percentile(N=series, percent=0.25, count=len(series))
```

Result:
```
In [3]: series = df['a']
...: percentile(N=series, percent=0.25, count=len(series))
...:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/.pyenv/versions/3.11.9/envs/240912_woodwork_bug_minimal_example/lib/python3.11/site-packages/pandas/core/indexes/base.py:3653, in Index.get_loc(self, key)
3652 try:
-> 3653 return self._engine.get_loc(casted_key)
3654 except KeyError as err:

File ~/.pyenv/versions/3.11.9/envs/240912_woodwork_bug_minimal_example/lib/python3.11/site-packages/pandas/_libs/index.pyx:147, in pandas._libs.index.IndexEngine.get_loc()

File ~/.pyenv/versions/3.11.9/envs/240912_woodwork_bug_minimal_example/lib/python3.11/site-packages/pandas/_libs/index.pyx:176, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:2606, in pandas._libs.hashtable.Int64HashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:2630, in pandas._libs.hashtable.Int64HashTable.get_item()

KeyError: 1
```

Note that in cases when int(k) happens to be a value in the index it will give you an answer, but there is no reason it will actually correspond to the intended position!

If I'd had
```
df2 = pd.DataFrame({'a':[1, 2, 3, 4, 5]})
df2['new_index'] = [3, 4, 5, 6, 1]
df2.set_index('new_index', inplace=True)
```
then `percentile(N=df2['a'], percent=0.25, count=len(series))` gives `5` for the 25th percentile 😱
```
In [9]: df2 = pd.DataFrame({'a':[1, 2, 3, 4, 5]})
...: df2['new_index'] = [3, 4, 5, 6, 1]
...: df2.set_index('new_index', inplace=True)

In [10]: df2
Out[10]:
a
new_index
3 1
4 2
5 3
6 4
1 5

In [11]: percentile(N=df2['a'], percent=0.25, count=len(series))
Out[11]: 5
```

## The fix

The fix is quite simple. Add a `.iat` like the lines below have.
```
import math

# Proposed fix (add .iat to the if f==c case):
def percentile(N, percent, count):
"""
Source: https://stackoverflow.com/questions/2374640/how-do-i-calculate-percentiles-with-python-nump

Find the percentile of a list of values.

Args:
N (pd.Series): Series is a list of values. Note N MUST BE already sorted.
percent (float): float value from 0.0 to 1.0.
count (int): Count of values in series

Returns:
Value at percentile.
"""
k = (count - 1) * percent
f = math.floor(k)
c = math.ceil(k)
if f == c:
return N.iat[int(k)] # Fix is just addint .iat
d0 = N.iat[int(f)] * (c - k)
d1 = N.iat[int(c)] * (k - f)
return d0 + d1
```

Now it works:
```
In [5]: series = df['a']
...: percentile(N=series, percent=0.25, count=len(series))
Out[5]: 2
```