qcut not working properly with groupby
For some reason, the quantile bands aren't what you would expect when using a groupby...

---

print pandas.**version**
0.8.1

frame = DataFrame({'data1': np.random.randn(1000), 'data2': np.random.randn(1000)})

factor = qcut(frame.data1, 4)

frame.data2.groupby(factor)
Out[43]: <pandas.core.groupby.SeriesGroupBy at 0x5a61e90>

frame.data2.groupby(factor).describe()
Out[44]: 
data1  
(-0.00303, 0.649]   count    250.000000
                    mean       0.007775
                    std        0.999344
                    min       -2.502555
                    25%       -0.738085
                    50%       -0.010441
                    75%        0.658221
                    max        3.101737
(-0.748, -0.00303]  count    250.000000
                    mean       0.043912
                    std        1.000687
                    min       -3.025111
                    25%       -0.585176
                    50%        0.087992
                    75%        0.670224
                    max        3.043880
(0.649, 3.0124]     count    250.000000
                    mean      -0.068339
                    std        1.000569
                    min       -2.767514
                    25%       -0.842156
                    50%       -0.048075
                    75%        0.623365
                    max        3.043415
[-3.127, -0.748]    count    250.000000
                    mean      -0.063501
                    std        1.090136
                    min       -2.706057
                    25%       -0.776094
                    50%       -0.056513
                    75%        0.729134
                    max        3.377690

---

The "mean" and "median" functions seem to be behaving properly:

frame.data2.groupby(factor).mean()
Out[45]: 
data1
[-3.127, -0.748]     -0.063501
(-0.748, -0.00303]    0.043912
(-0.00303, 0.649]     0.007775
(0.649, 3.0124]      -0.068339

frame.data2.groupby(factor).median()
Out[46]: 
data1
[-3.127, -0.748]     -0.056513
(-0.748, -0.00303]    0.087992
(-0.00303, 0.649]    -0.010441
(0.649, 3.0124]      -0.048075

BUT _count_ is broken. this makes the median and mean functions inconsistent with the describe function:

frame.data2.groupby(factor).count()
Out[47]: 
data1
(-0.00303, 0.649]     250
(-0.748, -0.00303]    250
(0.649, 3.0124]       250
[-3.127, -0.748]      250
