groupby.var result  is different than np.var
With the groupby.var, I don't have the same result with numpy.
I use the variance formula, it seems that numpy is correct.
In the groupby.var, I think that instead of dividing by N (the number of observations), it is divided by N-1.

```python
# data
df = pd.DataFrame({'id':['aa','aa','aa','aa','dd','dd'],'a':[2,2,3,4,2,2], 'b':[1,2,3,4,5,6]})

# code pandas
df[['id','b']].groupby('id').var()

#[out]:
           b
id          
aa  1.666667
dd  0.500000

# with numpy
for i in df['id'].drop_duplicates().tolist():
    nb_var = np.var(df[df.id==i]['b'])
    print(i, nb_var)

#[out]:
aa 1.25
dd 0.25
```
  