寻求对Dask，Numpy和Pandas基准测试结果的解释

Question

我正在尝试基准dask vs pandas的性能。

def make_pandas(n):
    df = pd.DataFrame(np.random.randint(10, size=(n, 3)))
    return df

def make_dask(n):
    df = da.from_array(np.random.randint(10, size=(n, 3)), chunks=10)
    return df

def make_numpy(n):
    return np.random.randint(10, size=(n, 3))

def sum_pandas(x): return x[0].sum()
def sum_dask(x): return x[1].sum()
def sum_numpy(x): return x[2].sum()

perfplot.show(
    setup=lambda n: [make_pandas(n), make_dask(n), make_numpy(n)],
    kernels=[sum_pandas, sum_dask, sum_numpy],
    n_range=[2**k for k in range(2, 15)],
    equality_check=False,
    xlabel='len(df)')

有人可以解释一下结果：

dask v numpy v pandas-10个dask块