rolling apply gives different output when raw = True or raw = False
Hi,
I am working with pandas.Series.apply() and got some unexpected output. I created the following  example code (I could not manage to reproduce the issue with any other values, maybe there is something that I am not seeing):

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

data=pd.Series([12.74,
 12.99,
 12.81,
 12.7,
 11.46,
 10.91,
 11.05,
 10.71,
 11.29,
 11.73,
 7.42,
 6.51,
 5.12,
 3.9,
 4.62,
 3.97,
 4.48,
 4.09,
 4.68,
 4.68,
 3.59,
 2.54,
 2.03,
 2.52,
 1.98,
 0.8,
 1.47,
 1.49,
 0.38,
 0.44,
 0.93,
 1.23,
 1.62,
 1.28,
 2.79,
 2.88,
 4.28,
 1.74,
 3.31,
 3.32,
 4.23,
 3.96,
 4.15,
 2.81,
 4.21,
 4.5,
 4.22,
 1.34,
 2.26,
 1.72,
 1.52,
 3.54,
 2.43,
 2.61,
 4.48,
 5.3,
 5.9,
 6.55,
 7.37,
 8.27,
 6.97,
 8.81,
 8.45,
 8.09,
 7.02,
 8.69,
 8.15,
 7.41,
 8.61,
 9.02,
 9.44,
 10.28,
 9.0,
 7.87,
 8.08,
 7.81,
 6.41,
 6.02,
 7.04,
 5.7,
 6.2,
 5.96,
 6.28,
 6.81,
 6.66,
 6.17,
 6.92,
 7.92,
 5.78,
 7.33,
 6.9,
 6.36,
 8.24,
 7.41,
 8.96,
 10.55,
 11.43,
 12.44,
 13.78,
 13.44,
 14.23,
 11.71,
 12.36,
 13.41,
 13.15,
 14.76,
 14.23,
 14.75,
 13.76,
 14.1,
 13.63,
 12.5,
 11.3,
 11.48,
 12.9,
 10.71,
 10.62,
 10.33,
 10.29,
 10.4,
 11.64,
 12.92,
 12.79,
 11.48,
 11.2,
 10.79,
 10.04,
 10.51,
 11.2,
 12.88,
 11.72,
 11.98,
 12.57,
 13.37,
 13.15,
 13.5,
 12.79,
 14.6,
 15.36,
 15.64,
 17.39,
 16.43,
 16.82,
 18.15,
 18.48,
 19.12,
 20.47,
 20.8,
 19.66,
 20.95,
 20.81,
 21.63,
 15.88,
 17.17,
 17.9,
 18.41,
 17.3,
 17.66,
 17.71,
 18.31,
 17.55,
 17.28,
 17.36,
 15.46,
 15.15,
 14.77,
 14.79,
 15.18,
 15.2,
 14.05,
 13.91,
 14.24,
 13.73,
 14.81,
 16.61,
 17.77,
 18.17,
 18.49,
 18.23,
 19.34,
 18.85,
 18.73,
 19.83,
 21.43,
 22.28,
 20.27,
 19.81,
 20.11,
 20.25,
 18.82,
 20.73,
 20.03,
 19.91,
 18.9,
 18.71,
 18.8,
 17.33,
 19.98,
 18.76,
 19.71,
 19.56,
 20.26,
 20.89,
 19.41,
 19.82,
 19.5,
 18.26,
 17.92,
 17.58,
 17.05,
 17.5,
 15.32,
 16.44,
 17.82,
 18.72,
 19.83,
 19.26,
 19.4,
 18.51,
 18.83,
 18.02,
 18.37,
 17.49,
 17.78,
 16.5,
 17.6,
 18.3,
 19.43,
 17.78,
 17.43,
 18.19,
 16.8,
 16.59,
 16.27,
 16.91,
 15.98,
 15.08,
 13.93,
 14.44,
 14.72,
 16.96,
 15.98,
 14.68,
 13.85,
 12.35,
 10.25,
 11.88,
 12.16,
 11.68,
 13.28,
 11.78,
 11.36,
 11.15,
 9.76,
 9.72,
 8.64,
 6.99,
 6.82,
 6.07,
 6.53,
 6.45,
 7.12,
 6.94,
 6.33,
 5.46,
 5.46,
 2.63,
 2.63,
 1.89,
 2.45,
 3.29,
 3.73,
 5.43,
 3.98,
 4.53,
 5.46,
 5.57,
 4.66,
 3.6,
 4.24,
 5.25,
 3.54,
 2.51,
 1.65,
 1.72,
 3.58,
 5.15,
 5.25,
 4.87,
 4.04,
 4.49,
 4.8,
 4.94,
 5.66,
 6.05,
 7.49,
 6.06,
 6.23,
 6.65,
 4.92,
 5.04,
 4.72,
 4.4,
 5.17,
 4.36,
 4.97,
 6.23,
 5.03,
 4.21,
 3.37,
 3.28,
 2.82,
 1.13,
 0.31,
 1.49,
 1.33,
 0.32,
 1.01,
 0.12,
 0.92,
 1.63,
 3.79,
 4.82,
 4.67,
 3.53,
 2.38,
 1.92,
 0.97,
 2.47,
 3.01,
 2.31,
 2.51,
 3.97,
 3.44,
 4.13,
 3.44,
 3.51,
 3.2,
 3.47,
 5.06,
 5.95,
 4.28,
 3.87,
 5.67,
 4.88,
 5.48,
 6.46,
 5.0,
 3.73,
 3.27,
 3.33,
 3.92,
 2.68,
 1.3,
 0.32,
 1.23,
 1.26,
 2.74,
 2.65,
 1.19,
 1.71,
 4.18,
 3.47,
 2.13,
 1.02,
 2.46,
 3.46,
 2.59,
 2.17,
 1.95,
 2.38,
 1.17,
 1.88,
 4.03,
 4.76,
 6.38,
 7.53,
 8.28,
 9.26,
 7.68,
 6.99,
 6.71,
 7.64,
 8.23,
 8.66,
 8.78,
 7.8,
 8.08,
 7.01,
 7.73,
 8.6,
 8.49,
 9.76,
 8.92,
 8.11,
 7.76,
 7.67,
 8.79,
 9.0,
 8.22,
 6.34,
 8.1,
 7.23,
 7.83,
 7.48,
 8.16,
 8.5,
 7.8,
 8.83,
 8.65])

ravg=data.rolling(600,min_periods=1).apply(np.mean,raw=True)
ravg2=data.rolling(600,min_periods=1).apply(np.mean,raw=False)

print('%.35f' % ravg.iloc[-1])
print('%.35f' % ravg2.iloc[-1])

```
#### Problem description

#### Expected Output
I expect the print statement in the end to return 9.34999999999999964472863211994990706

#### Output of ``pd.Series.apply()``
When setting raw=True, I get the following output:
```
9.34999999999999964472863211994990706
```

When setting raw=False, I get the following output:
```
9.35000000000001030286966852145269513
```

Is the difference intentional or a bug?

Kind regards,
Nick

