다음 데이터 프레임을 목록으로 변환하고자합니다.

Question

Sl No  Vertical Verticale Code Org Work Location \ 
0  1.0    IT    5   New Delhi 
1  2.0    IT    5   Raipur 
2  3.0 Infrastructure    7  Coimbatore 
3  4.0   Telecom    3   Chennai 
4  5.0   Telecom    3   Ahmedabad 
5  6.0    IT    5   Chennai 
6  7.0    IT    5   Chennai 
7  8.0  IT Products    6   Bangalore 
8  9.0    IT    5   Chennai 
9  10.0    IT    5   Chennai 
10 11.0   Telecom    3   Bangalore 
11 12.0    IT    5   Mysore 
12 13.0  IT Products    6  Navi Mumbai 
13 14.0   Telecom    3   Bangalore 
14 15.0 Infrastructure    7   Chennai 
15 16.0    IT    5   Chennai 
16 17.0    IT    5   Chennai 
17 18.0 Infrastructure    7  Coimbatore 
18 19.0   Telecom    3   Chennai 
19 20.0   Telecom    3   Bangalore 
20 21.0   Telecom    3   Bengalore 
21 22.0    IT    5   Chennai 

Answer 1

은 평면 목록 또는 중첩 목록을 원하는지 여부에 따라 다릅니다.

중첩 된 목록

df.values.tolist() 

[[0, 1.0, 'IT', '5', 'New', 'Delhi', nan, nan], 
[1, 2.0, 'IT', '5', 'Raipur', nan, nan, nan], 
[2, 3.0, 'Infrastructure', '7', 'Coimbatore', nan, nan, nan], 
[3, 4.0, 'Telecom', '3', 'Chennai', nan, nan, nan], 
[4, 5.0, 'Telecom', '3', 'Ahmedabad', nan, nan, nan], 
[5, 6.0, 'IT', '5', 'Chennai', nan, nan, nan], 
[6, 7.0, 'IT', '5', 'Chennai', nan, nan, nan], 
[7, 8.0, 'IT', 'Products', '6', 'Bangalore', nan, nan], 
[8, 9.0, 'IT', '5', 'Chennai', nan, nan, nan], 
[9, 10.0, 'IT', '5', 'Chennai', nan, nan, nan], 
[10, 11.0, 'Telecom', '3', 'Bangalore', nan, nan, nan], 
[11, 12.0, 'IT', '5', 'Mysore', nan, nan, nan], 
[12, 13.0, 'IT', 'Products', '6', 'Navi', 'Mumbai', nan], 
[13, 14.0, 'Telecom', '3', 'Bangalore', nan, nan, nan], 
[14, 15.0, 'Infrastructure', '7', 'Chennai', nan, nan, nan], 
[15, 16.0, 'IT', '5', 'Chennai', nan, nan, nan], 
[16, 17.0, 'IT', '5', 'Chennai', nan, nan, nan], 
[17, 18.0, 'Infrastructure', '7', 'Coimbatore', nan, nan, nan], 
[18, 19.0, 'Telecom', '3', 'Chennai', nan, nan, nan], 
[19, 20.0, 'Telecom', '3', 'Bangalore', nan, nan, nan], 
[20, 21.0, 'Telecom', '3', 'Bengalore', nan, nan, nan], 
[21, 22.0, 'IT', '5', 'Chennai', nan, nan, nan]] 

단순 목록

df.values.ravel().tolist() 

[0, 
1.0, 
'IT', 
'5', 
'New', 
'Delhi', 
nan, 
nan, 
1, 
2.0, 
'IT', 
'5', 
'Raipur', 
nan, 
nan, 
nan, 
2, 
3.0, 
'Infrastructure', 
'7', 
'Coimbatore', 
nan, 
nan, 
nan, 
3, 
4.0, 
'Telecom', 
'3', 
'Chennai', 
nan, 
nan, 
nan, 
4, 
5.0, 
'Telecom', 
'3', 
'Ahmedabad', 
nan, 
nan, 
nan, 
5, 
6.0, 
'IT', 
'5', 
'Chennai', 
nan, 
nan, 
nan, 
6, 
7.0, 
'IT', 
'5', 
'Chennai', 
nan, 
nan, 
nan, 
7, 
8.0, 
'IT', 
'Products', 
'6', 
'Bangalore', 
nan, 
nan, 
8, 
9.0, 
'IT', 
'5', 
'Chennai', 
nan, 
nan, 
nan, 
9, 
10.0, 
'IT', 
'5', 
'Chennai', 
nan, 
nan, 
nan, 
10, 
11.0, 
'Telecom', 
'3', 
'Bangalore', 
nan, 
nan, 
nan, 
11, 
12.0, 
'IT', 
'5', 
'Mysore', 
nan, 
nan, 
nan, 
12, 
13.0, 
'IT', 
'Products', 
'6', 
'Navi', 
'Mumbai', 
nan, 
13, 
14.0, 
'Telecom', 
'3', 
'Bangalore', 
nan, 
nan, 
nan, 
14, 
15.0, 
'Infrastructure', 
'7', 
'Chennai', 
nan, 
nan, 
nan, 
15, 
16.0, 
'IT', 
'5', 
'Chennai', 
nan, 
nan, 
nan, 
16, 
17.0, 
'IT', 
'5', 
'Chennai', 
nan, 
nan, 
nan, 
17, 
18.0, 
'Infrastructure', 
'7', 
'Coimbatore', 
nan, 
nan, 
nan, 
18, 
19.0, 
'Telecom', 
'3', 
'Chennai', 
nan, 
nan, 
nan, 
19, 
20.0, 
'Telecom', 
'3', 
'Bangalore', 
nan, 
nan, 
nan, 
20, 
21.0, 
'Telecom', 
'3', 
'Bengalore', 
nan, 
nan, 
nan, 
21, 
22.0, 
'IT', 
'5', 
'Chennai', 
nan, 
nan, 
nan]