Performance regression in rolling.Quantile.time_quantile
https://pandas.pydata.org/speed/pandas/index.html#rolling.Quantile.time_quantile?p-constructor=%27Series%27&p-window=1000&p-dtype=%27int%27&p-percentile=1&p-param5=%27linear%27&commits=df5eee60d60a604969bdc3e36a0da20ea36af3f6.

```python
import pandas as pd
import numpy as np

constructor = "Series"
window = 1000
dtype = "int"
percentile = 1
interpolation = "linear"


N = 10 ** 5
arr = np.random.random(N).astype(dtype)
roll = getattr(pd, constructor)(arr).rolling(window)

%timeit roll.quantile(percentile, interpolation=interpolation)
```

Seems to be from https://github.com/pandas-dev/pandas/pull/33693 (cc @s-scherrer).

Interestingly, I'm not able to reproduce this locally. Is anyone else able to?

```
# 1.0.2
3.16 ms ± 295 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

# master
2.27 ms ± 56.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```

