Python 如何将选定数据转换为相同长度（形状）

我将多个.csv文件作为一个具有相同形状的熊猫数据框读取。对于某些索引，某些值为零，因此我想选择具有相同形状的每个索引的值，并将相同索引的值置为零，然后删除零以成为相同形状：

a = pd.DataFrame(pd.read_csv("path_a",index_col=0))
b = pd.DataFrame(pd.read_csv("path_b",index_col=0))
c = pd.DataFrame(pd.read_csv("path_c",index_col=0))
print a,"\n",b,"\n",c
L = np.array(a.shape)
X = L[0]
d = a.index.values
a = np.array(a)
b = np.array(b)
c = np.array(c)
for i in range (0,X):
    xdata  = a[i]
    xdata1 = b[i]
    xdata2 = c[i]
    xdata  = np.where(xdata2==0,0,xdata)
    xdata1 = np.where(xdata2==0,0,xdata1)
    xdata1 = np.where(xdata==0,0,xdata1)
    xdata2 = np.where(xdata==0,0,xdata2)
    xdata  = np.where(xdata1==0,0,xdata)
    xdata2 = np.where(xdata1==0,0,xdata2)
    indexX  = np.argwhere(xdata==0)
    index1X = np.argwhere(xdata1==0)
    index2X = np.argwhere(xdata2==0)
    xdata  = np.delete(xdata,indexX)
    xdata1 = np.delete(xdata1,index1X)
    xdata2 = np.delete(xdata2,index2X)
    print d[i],"\n",xdata,"\n",xdata1,"\n",xdata2

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这段代码可以工作，但这是一种尝试性的方法，当数据量很大时，它就没有效率。你能给我建议一种更有效的方法，以及如何根据最小长度索引选择数据吗？

一个想法是将所有3个数组都乘以，然后测试它是否为非

0

，也可以使用列表中的3个数组循环

L1

。然后还更改了逻辑-选择与掩码不匹配的值，而不是

np.argwhere

和

np.delete

：

L = np.array(a.shape)
X = L[0]
d = a.index.values
a = np.array(a)
b = np.array(b)
c = np.array(c)
m = (a * b * c) != 0
L1 = [a,b,c]

for i in range (0,X):
    for arr in L1:
        xdata  = arr[i][m[i]]
        print (xdata)

如果使用pandas 0.24+，则转换为numpy数组的更好方法是使用：

编辑：

---

谢谢，但是如果我想将扩展数据用作输入数据，我如何区分它们？@water77-一个可能的解决方案是为每个索引值创建numpy数组，请选中编辑后的答案。@water77-谢谢，很乐意提供帮助。如果适合你，别忘了接受答案！：）

L = np.array(a.shape)
X = L[0]
d = a.index.values
a = np.array(a)
b = np.array(b)
c = np.array(c)
m = (a * b * c) != 0
L1 = [a,b,c]

for i in range (0,X):
    for arr in L1:
        xdata  = arr[i][m[i]]
        print (xdata)

L = np.array(a.shape)
X = L[0]
d = a.index.to_numpy()
a = a.to_numpy()
b = b.to_numpy()
c = c.to_numpy()
m = (a * b * c) != 0
L1 = [a,b,c]

for i in range (0,X):
    for arr in L1:
        xdata  = arr[i][m[i]]
        print (xdata)

L = np.array(a.shape)
X = L[0]
d = a.index.to_numpy()
a = a.to_numpy()
b = b.to_numpy()
c = c.to_numpy()
m = (a * b * c) != 0
L1 = [a,b,c]

for i in range (0,X):
    out = []
    for arr in L1:
        xdata  = arr[i][m[i]]
        out.append(xdata)
    data = np.vstack((out))
    print (data)

[]
[[ 3.   4. ]
 [15.8 16.4]
 [ 4.7  5.8]]
[[0.2 0.5 0.2 1.3 1.6 2.7]
 [1.5 1.6 1.6 1.6 1.6 1.7]
 [0.2 0.5 0.2 1.3 1.6 2.7]]
[]
[]
[[ 3.1  6.7  5.3 15.1 17.2 18.2 18.7]
 [ 1.8  1.8  1.7  1.8  1.8  1.9  1.9]
 [ 3.1  6.7  5.3 15.1 17.2 18.2 18.7]]
[[0.4 0.5 0.5 0.4 1.2 1.3]
 [1.8 1.8 1.7 1.8 1.9 1.5]
 [0.4 0.5 0.5 0.4 1.2 1.3]]