python - 将每列中的值分配为该列的总和

Question

我有 DataFrame，我正在尝试将每列中的所有值分配为该列的总和。

x = pd.DataFrame(data = [[1,2],[3,4],[5,6],[7,8],[9,10]],index=[1,2,3,4,5],columns=['a','b'])
x 
   a   b
1  1   2
2  3   4
3  5   6
4  7   8
5  9  10

输出应该是

   a    b
1  25   30
2  25   30
3  25   30
4  25   30
5  25   30

我想使用 x.apply(f, axis=0)，但我不知道如何在 lambda 函数中定义一个将列转换为所有列值之和的函数。以下行引发 SyntaxError: can't assign to lambda

f = lambda x : x[:]= x.sum()

Answer 1

另一个更快的 numpy 解决方案 numpy.tile :

print (pd.DataFrame(np.tile(x.sum().values, (len(x.index),1)), 
                    columns=x.columns, 
                    index=x.index))
    a   b
1  25  30
2  25  30
3  25  30
4  25  30
5  25  30

另一种解决方案 numpy.repeat :

h = pd.DataFrame(x.sum().values[np.newaxis,:].repeat(len(x.index), axis=0),
                 columns=x.columns,
                 index=x.index)

print (h)
    a   b
1  25  30
2  25  30
3  25  30
4  25  30
5  25  30


In [431]: %timeit df = pd.DataFrame([x.sum()] * len(x))
1000 loops, best of 3: 786 µs per loop

In [432]: %timeit (pd.DataFrame(np.tile(x.sum().values, (len(x.index),1)), columns=x.columns, index=x.index))
1000 loops, best of 3: 192 µs per loop

In [460]: %timeit pd.DataFrame(x.sum().values[np.newaxis,:].repeat(len(x.index), axis=0),columns=x.columns, index=x.index)
The slowest run took 8.65 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 184 µs per loop