ENH: Adapt window size of rolling statistics for first values
Now rolling functions like _rolling_mean_ give NaN's if the window size is outside the input series. I would like to have an extra option that the window size shrinkes if the window is outside the input series. Or to put it different: use an expanding mean for the first values and the rolling mean for all the intermediate values. Below an example of such a code and my workaround. 

``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#%% create data
df = pd.DataFrame()
data = np.cumsum(np.random.random(10)-0.5)
df['data'] = data

#%% do the calculations
# now:
window_size = 5
df['rolling_mean'] = pd.rolling_mean(df['data'], window=window_size)

# or: 
df['expanding']= pd.expanding_mean(df['data'],)

# what I wont
df['whatIwant'] = df['rolling_mean'].fillna(df['expanding'])

#%% create picture
fig, ax = plt.subplots()
ax.plot(df.index, df['data'], lw=5, label='data')
ax.plot(df.index, df['rolling_mean'], lw=5, label='rolling mean')
ax.plot(df.index, df['expanding'], lw=5, label='expanding')
ax.plot(df.index, df['whatIwant'], lw=2, label='what I want')
ax.legend()
```

This workaround works quite good, and is sort of clear. But if _rolling_mean_ is used in combination with _center=True_ this becomes more complicated. This workaround is also not very usefull when some data is missing. I think pandas can be improved by adding this option into the rolling_statistics functions. 

Disclaimer: This is the first issue ever that I add. 
