python - 如何计算 Pandas 自定义权重的移动平均值？

Question

我有一个包含两列的数据框，a: [1,2,3,4,5]; b:[1,0.4,0.3,0.5,0.2]。我怎样才能创建一个c列:

c[0] = 1  
c[i] = c[i-1]*b[i]+a[i]*(1-b[i]) 

这样c:[1,1.6,2.58,3.29,4.658]

计算:

1 = 1
1*0.4+2*0.6 = 1.6
1.6*0.3+3*0.7 = 2.58
2.58*0.5+4*0.5 = 3.29
3.29*0.2+5*0.8 = 4.658

？

Answer 1

我看不到一种方法来矢量化你的递归算法。但是，您可以使用 numba 来优化当前逻辑。这应该比常规循环更好。

from numba import jit

df = pd.DataFrame({'a': [1,2,3,4,5],
                   'b': [1,0.4,0.3,0.5,0.2]})

@jit(nopython=True)
def foo(a, b):
    c = np.zeros(a.shape)
    c[0] = 1
    for i in range(1, c.shape[0]):
        c[i] = c[i-1] * b[i] + a[i] * (1-b[i])
    return c

df['c'] = foo(df['a'].values, df['b'].values)

print(df)

   a    b      c
0  1  1.0  1.000
1  2  0.4  1.600
2  3  0.3  2.580
3  4  0.5  3.290
4  5  0.2  4.658