计算列之间加权乘积之和的最快方法是什么?

我尝试改进解决方案-删除重塑和更改 arrange :

a = df.dot(np.arange(1, len(df.columns)+1))
print (a)
0   -0.501247
1    0.602741
2    2.046290
3    0.385219
4   -6.835748
dtype: float64

numpy :

a = pd.Series(np.dot(df.values, np.arange(1, len(df.columns)+1)), index=df.index)
print (a)
0   -0.501247
1    0.602741
2    2.046290
3    0.385219
4   -6.835748
dtype: float64

选项1

据我所知,最快的是使用 df.dot

df.dot((np.arange(df.shape[1]) + 1).reshape(-1, 1))

          0
0 -0.501247
1  0.602741
2  2.046290
3  0.385219
4 -6.835748

选项2

元素级产品和 sum

(df * (np.arange(df.shape[1]) + 1)).sum(1)

0   -0.501246
1    0.602742
2    2.046292
3    0.385219
4   -6.835747

表演

小型( 5 x 3 )

10000 loops, best of 3: 131 µs per loop  # dot
1000 loops, best of 3: 531 µs per loop   # element-wise prod + sum

大型( 100000 x 1000 )

10 loops, best of 3: 36.4 ms per loop   # dot
1 loop, best of 3: 1.18 s per loop      # element-wise prod + sum

有关实施背后的魔力的信息 pandas / numpy 的 dot Why is matrix multiplication faster with numpy than with ctypes in Python? .