python - 从多个列表列表创建数据框

Question

我需要从 10 个列表中构建一个数据框。我是手动做的，但是需要时间。有什么更好的方法吗？

我尝试过手动完成。效果很好(#1)我尝试了代码(#2)以获得更好的性能，但它只返回最后一列。

1

    import pandas as pd
    import numpy as np
    a1T=[([7,8,9]),([10,11,12]),([13,14,15])]
    a2T=[([1,2,3]),([5,0,2]),([3,4,5])]
    print (a1T)
    #Output[[7, 8, 9], [10, 11, 12], [13, 14, 15]]
    vis1=np.array (a1T)
    vis_1_1=vis1.T
    tmp2=np.array (a2T)
    tmp_2_1=tmp2.T
    X=np.column_stack([vis_1_1, tmp_2_1])
    dataset_all = pd.DataFrame({"Visab1":X[:,0], "Visab2":X[:,1], "Visab3":X[:,2], "Temp1":X[:,3], "Temp2":X[:,4], "Temp3":X[:,5]})
    print (dataset_all)
    Output: Visab1  Visab2  Visab3  Temp1  Temp2  Temp3
0       7      10      13      1      5      3
1       8      11      14      2      0      4
2       9      12      15      3      2      5

> Actually I have varying number of columns in dataframe (500-1500), thats why I need auto generated column names. Extra index (1, 2, 3) after name Visab_, Temp_ and so on - constant for every case.  See code below.
For better perfomance I tried

code<br>
#2

n=3 # This is varying parameter. The parameter affects the number of columns in the table. 
m=2 # This is constant for every case. here is 2, because we have "Visab", "Temp"
mlist=('Visab', 'Temp')
nlist=[range(1, n)]
for j in range (1,n):
    for i in range (1,m):
    col=i+(j-1)*n
    dataset_all=pd.DataFrame({mlist[j]+str(i):X[:, col]})

I expect output like

Visab1 Visab2 Visab3 Temp1 Temp2 Temp3
0 7 10 13 1 5 3
1 8 11 14 2 0 4
2 9 12 15 3 2 5

but there is not any result (only error expected an indented block)

Answer 1

好的，那么列数 n 就是每个列表中子列表的数量，对吗？您可以使用 len 来测量:

len(a1T)
#Output
3

我将简化上面的答案，这样您就不需要 X 并添加自动列名创建:

my_lists = [a1T,a2T]
my_names = ["Visab","Temp"]

dfs=[]
for one_list,name in zip(my_lists,my_names):
  n_columns = len(one_list)
  col_names=[name+"_"+str(n) for n in range(n_columns)]
  df = pd.DataFrame(one_list).T
  df.columns = col_names
  dfs.append(df)

dataset_all = pd.concat(dfs,axis=1)

#Output
    Visab_0     Visab_1     Visab_2     Temp_0  Temp_1  Temp_2
0     7           10          13          1        5       3
1     8           11          14          2        0       4
2     9           12          15          3        2       5