PERF: nanops
```
from asv_bench.benchmarks.stat_ops import *
self = FrameOps()
self.setup("skew", "float", 0)

%timeit self.time_op("skew", "float", 0)
3.22 ms ± 235 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- master
2.06 ms ± 71.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- PR

%timeit self.time_op("skew", "int", 0)
3.81 ms ± 294 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- master
2.3 ms ± 79.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- PR

%timeit self.time_op("kurt", "float", 0)
2.79 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- master
2.2 ms ± 85.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)  # <- PR

%timeit self.time_op("kurt", "int", 0)
3.29 ms ± 131 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- master
2.35 ms ± 69.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- PR

%timeit self.time_op("prod", "float", 0)
2.9 ms ± 116 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- master
2.23 ms ± 40.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- PR

%timeit self.time_op("sum", "float", 0)
2.9 ms ± 194 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- master
2.14 ms ± 130 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # <- PR
```
