通过Pandas数据框为多列归一化每一列

时间:2018-07-09 19:33:51

标签: python-3.x pandas numpy dataframe

我有一个包含许多列的数据框文件。我想规范化每一列。以下是我的数据框文件的一部分。

0   4   6   8
-0.276  4403    4403    4403
-0.138  4640    4640    4640
0   0   0   0
0.138   12  0   12
0.276   0   0   0
0.414   0   0   0
0.552   0   0   0
0.69    0   0   0
0.828   0   12  0
0.966   0   0   0
1.104   0   12  0
1.242   0   0   0
1.38    0   0   0
1.518   0   0   12
1.656   0   0   0
1.794   0   0   0
1.932   0   0   0
2.07    0   0   0
2.208   0   0   0
2.346   0   0   0
2.484   0   0   0
2.622   0   12  0
2.76    0   0   0
2.898   0   0   0
3.036   0   0   0
3.174   0   0   0
3.312   0   0   0
3.45    0   0   0
3.588   0   0   0
3.726   0   0   0
3.864   12  0   0
4.002   0   0   0
4.14    0   0   0
4.278   12  0   0
4.416   0   0   0
4.554   0   12  0
4.692   0   0   0
4.83    0   0   0
4.968   0   0   0
5.106   0   0   0
5.244   0   0   0
5.382   12  0   0
5.52    0   0   0
5.658   0   0   0
5.796   127 60  60
5.934   357 275 317
6.072   1882    2144    1838
6.21    6726    6609    7915
6.348   9398    11180   12737
6.486   12784   18389   21361
6.624   15863   20111   24469
6.762   6739    10202   11897
6.9 1684    1921    2735
7.038   249 376 476
7.176   47  103 70
7.314   0   26  82
7.452   17  0   18
7.59    0   0   0
7.728   0   0   0
7.866   0   0   0
8.004   0   0   0
8.142   0   0   18
8.28    0   0   0
8.418   0   0   0
8.556   0   0   0
8.694   0   0   0
8.832   0   0   0
8.97    0   0   0
9.108   0   0   12
9.246   0   0   0
9.384   0   0   0
9.522   0   0   0
9.66    0   0   0
9.798   0   0   0
9.936   0   0   0
10.074  0   0   0
10.212  0   0   0
10.35   0   12  0
10.488  0   0   0
10.626  0   0   0
10.764  0   0   12
10.902  0   0   0
11.04   0   0   0
11.178  0   0   0
11.316  0   0   0
11.454  0   0   0
11.592  0   0   0
11.73   0   0   0
11.868  0   0   0
12.006  0   0   0
12.144  0   0   0
12.282  0   0   0
12.42   0   0   0
12.558  0   0   0
12.696  12  0   0
12.834  0   0   0
12.972  0   0   0
13.11   0   0   0
13.248  0   0   0
13.386  12  0   0
13.524  0   0   12
13.662  0   12  0
13.8    0   0   0
13.938  0   0   0
14.076  0   0   0
14.214  0   0   0
14.352  0   0   0
14.49   0   0   0
14.628  12  0   0
14.766  0   0   0
14.904  12  0   0
15.042  0   0   0
15.18   0   0   12
15.318  0   0   0
15.456  0   0   0
15.594  0   0   0
15.732  0   0   0
15.87   0   0   0
16.008  0   0   18
16.146  0   0   0
16.284  0   0   0
16.422  0   0   0
16.56   12  0   0
16.698  0   0   0
16.836  0   0   0
16.974  0   0   0
17.112  0   0   0
17.25   0   0   0
17.388  0   0   0
17.526  0   0   0
17.664  0   12  0
17.802  0   0   0
17.94   0   0   0
18.078  0   0   0
18.216  0   0   0
18.354  0   0   0
18.492  0   0   12
18.63   12  0   0
18.768  0   0   0
18.906  0   0   0
19.044  0   0   0
19.182  0   0   0
19.32   0   0   0
19.458  0   0   0
19.596  0   0   0
19.734  0   0   0
19.872  0   0   0
20.01   0   0   0
20.148  0   12  0
20.286  12  0   0
20.424  0   12  0
20.562  0   0   0
20.7    0   0   0
20.838  0   0   0
20.976  0   0   0
21.114  0   0   0
21.252  0   0   0
21.39   0   12  0
21.528  0   0   0
21.666  0   0   0
21.804  12  0   0
21.942  0   0   0
22.08   0   0   0
22.218  0   0   0
22.356  0   0   0
22.494  0   0   0
22.632  0   0   0
22.77   0   0   0
22.908  0   0   0
23.046  0   0   0
23.184  0   0   0
23.322  0   0   0
23.46   12  0   12
23.598  0   12  0
23.736  0   0   0
23.874  0   0   0
24.012  0   0   0
24.15   0   0   0
24.288  0   0   0
24.426  0   0   0
24.564  0   0   0
24.702  0   0   0
24.84   0   0   0
24.978  0   0   0
25.116  0   0   0
25.254  0   0   0
25.392  0   0   0
25.53   0   0   12
25.668  0   0   0
25.806  12  0   0
25.944  12  0   0
26.082  0   0   0
26.22   0   0   0
26.358  0   12  0
26.496  0   0   0
26.634  0   0   0
26.772  0   0   0
26.91   0   0   0
27.048  13  0   0
27.186  0   0   12
27.324  0   0   0
27.462  0   0   13

到目前为止,我已经可以通过以下代码规范化一列(名为“ 4”):

import pandas as pd
import numpy as np

pos_df = pd.read_csv('my_data.csv',header='infer')
column1=pos_df['4']

Norm_column1=pos_df['4']/np.max(pos_df['4'])

但是我想对所有列进行归一化。有更好的批处理吗?

谢谢!

0 个答案:

没有答案