[python] DataFrame 열의 순서를 변경하는 방법은 무엇입니까?

한 가지 쉬운 방법은 필요에 따라 재정렬 된 열 목록을 사용하여 데이터 프레임을 다시 할당하는 것입니다.

이것이 당신이 지금 가진 것입니다 :

In [6]: df
Out[6]:
          0         1         2         3         4      mean
0  0.445598  0.173835  0.343415  0.682252  0.582616  0.445543
1  0.881592  0.696942  0.702232  0.696724  0.373551  0.670208
2  0.662527  0.955193  0.131016  0.609548  0.804694  0.632596
3  0.260919  0.783467  0.593433  0.033426  0.512019  0.436653
4  0.131842  0.799367  0.182828  0.683330  0.019485  0.363371
5  0.498784  0.873495  0.383811  0.699289  0.480447  0.587165
6  0.388771  0.395757  0.745237  0.628406  0.784473  0.588529
7  0.147986  0.459451  0.310961  0.706435  0.100914  0.345149
8  0.394947  0.863494  0.585030  0.565944  0.356561  0.553195
9  0.689260  0.865243  0.136481  0.386582  0.730399  0.561593

In [7]: cols = df.columns.tolist()

In [8]: cols
Out[8]: [0L, 1L, 2L, 3L, 4L, 'mean']

cols원하는 방식으로 재 배열 하십시오. 이것이 마지막 요소를 첫 번째 위치로 옮긴 방법입니다.

In [12]: cols = cols[-1:] + cols[:-1]

In [13]: cols
Out[13]: ['mean', 0L, 1L, 2L, 3L, 4L]

그런 다음 데이터 프레임을 다음과 같이 재정렬하십시오.

In [16]: df = df[cols]  #    OR    df = df.ix[:, cols]

In [17]: df
Out[17]:
       mean         0         1         2         3         4
0  0.445543  0.445598  0.173835  0.343415  0.682252  0.582616
1  0.670208  0.881592  0.696942  0.702232  0.696724  0.373551
2  0.632596  0.662527  0.955193  0.131016  0.609548  0.804694
3  0.436653  0.260919  0.783467  0.593433  0.033426  0.512019
4  0.363371  0.131842  0.799367  0.182828  0.683330  0.019485
5  0.587165  0.498784  0.873495  0.383811  0.699289  0.480447
6  0.588529  0.388771  0.395757  0.745237  0.628406  0.784473
7  0.345149  0.147986  0.459451  0.310961  0.706435  0.100914
8  0.553195  0.394947  0.863494  0.585030  0.565944  0.356561
9  0.561593  0.689260  0.865243  0.136481  0.386582  0.730399

답변

df = df[['mean', '0', '1', '2', '3']]

cols = list(df.columns.values)

['0', '1', '2', '3', 'mean']

In [39]: df
Out[39]:
          0         1         2         3         4  mean
0  0.172742  0.915661  0.043387  0.712833  0.190717     1
1  0.128186  0.424771  0.590779  0.771080  0.617472     1
2  0.125709  0.085894  0.989798  0.829491  0.155563     1
3  0.742578  0.104061  0.299708  0.616751  0.951802     1
4  0.721118  0.528156  0.421360  0.105886  0.322311     1
5  0.900878  0.082047  0.224656  0.195162  0.736652     1
6  0.897832  0.558108  0.318016  0.586563  0.507564     1
7  0.027178  0.375183  0.930248  0.921786  0.337060     1
8  0.763028  0.182905  0.931756  0.110675  0.423398     1
9  0.848996  0.310562  0.140873  0.304561  0.417808     1

In [40]: df = df[['mean', 4,3,2,1]]

In [41]: df
Out[41]:
   mean         4         3         2         1
0     1  0.190717  0.712833  0.043387  0.915661
1     1  0.617472  0.771080  0.590779  0.424771
2     1  0.155563  0.829491  0.989798  0.085894
3     1  0.951802  0.616751  0.299708  0.104061
4     1  0.322311  0.105886  0.421360  0.528156
5     1  0.736652  0.195162  0.224656  0.082047
6     1  0.507564  0.586563  0.318016  0.558108
7     1  0.337060  0.921786  0.930248  0.375183
8     1  0.423398  0.110675  0.931756  0.182905
9     1  0.417808  0.304561  0.140873  0.310562

df.insert(0, 'mean', df.mean(1))

df = df.reindex(columns=['mean',0,1,2,3,4])

df = df.reindex(columns=sorted(df.columns))
df = df.reindex(columns=(['opened'] + list([a for a in df.columns if a != 'opened']) ))

cols = ['mean']  + [col for col in df if col != 'mean']
df = df[cols]

cols = [df.columns[-1]] + [col for col in df if col != df.columns[-1]]
df = df[cols]

inserted_cols = ['a', 'b', 'c']
cols = ([col for col in inserted_cols if col in df]
        + [col for col in df if col not in inserted_cols])
df = df[cols]

import numpy as np
import pandas as pd
df = pd.DataFrame()
column_names = ['x','y','z','mean']
for col in column_names:
    df[col] = np.random.randint(0,100, size=10000)

df = df[ ['mean'] + [ col for col in df.columns if col != 'mean' ] ]

df = df[['mean', 'x', 'y', 'z']]

col = df.pop("mean")
df = df.insert(0, col.name, col)

df.set_index(df.columns[-1], inplace=True)
df.reset_index(inplace=True)

cols = list(df)
cols = [cols[-1]] + cols[:-1]
df = df[cols]

order = [1,2,3,0] # setting column's order
df = df[[df.columns[i] for i in order]]