我有以下DataFrame(df):

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(10, 5))

我通过分配添加更多列:

df['mean'] = df.mean(1)

如何将列的意思移到前面,即将其设置为第一列,而其他列的顺序保持不变?


当前回答

如果列名太长,无法键入,则可以通过整数列表指定新顺序,其中包含以下位置:

数据:

          0         1         2         3         4      mean
0  0.397312  0.361846  0.719802  0.575223  0.449205  0.500678
1  0.287256  0.522337  0.992154  0.584221  0.042739  0.485741
2  0.884812  0.464172  0.149296  0.167698  0.793634  0.491923
3  0.656891  0.500179  0.046006  0.862769  0.651065  0.543382
4  0.673702  0.223489  0.438760  0.468954  0.308509  0.422683
5  0.764020  0.093050  0.100932  0.572475  0.416471  0.389390
6  0.259181  0.248186  0.626101  0.556980  0.559413  0.449972
7  0.400591  0.075461  0.096072  0.308755  0.157078  0.207592
8  0.639745  0.368987  0.340573  0.997547  0.011892  0.471749
9  0.050582  0.714160  0.168839  0.899230  0.359690  0.438500

通用示例:

new_order = [3,2,1,4,5,0]
print(df[df.columns[new_order]])  

          3         2         1         4      mean         0
0  0.575223  0.719802  0.361846  0.449205  0.500678  0.397312
1  0.584221  0.992154  0.522337  0.042739  0.485741  0.287256
2  0.167698  0.149296  0.464172  0.793634  0.491923  0.884812
3  0.862769  0.046006  0.500179  0.651065  0.543382  0.656891
4  0.468954  0.438760  0.223489  0.308509  0.422683  0.673702
5  0.572475  0.100932  0.093050  0.416471  0.389390  0.764020
6  0.556980  0.626101  0.248186  0.559413  0.449972  0.259181
7  0.308755  0.096072  0.075461  0.157078  0.207592  0.400591
8  0.997547  0.340573  0.368987  0.011892  0.471749  0.639745
9  0.899230  0.168839  0.714160  0.359690  0.438500  0.050582

虽然看起来我只是以不同的顺序显式键入列名,但列“mean”的事实应该清楚地表明,new_order与实际位置相关,而不是列名。

对于OP问题的具体情况:

new_order = [-1,0,1,2,3,4]
df = df[df.columns[new_order]]
print(df)

       mean         0         1         2         3         4
0  0.500678  0.397312  0.361846  0.719802  0.575223  0.449205
1  0.485741  0.287256  0.522337  0.992154  0.584221  0.042739
2  0.491923  0.884812  0.464172  0.149296  0.167698  0.793634
3  0.543382  0.656891  0.500179  0.046006  0.862769  0.651065
4  0.422683  0.673702  0.223489  0.438760  0.468954  0.308509
5  0.389390  0.764020  0.093050  0.100932  0.572475  0.416471
6  0.449972  0.259181  0.248186  0.626101  0.556980  0.559413
7  0.207592  0.400591  0.075461  0.096072  0.308755  0.157078
8  0.471749  0.639745  0.368987  0.340573  0.997547  0.011892
9  0.438500  0.050582  0.714160  0.168839  0.899230  0.359690

这种方法的主要问题是多次调用同一代码将每次产生不同的结果,因此需要小心:)

其他回答

下面是一个超级简单的方法示例。如果您要从excel复制标题,请使用.split('\t')

df = df['FILE_NAME DISPLAY_PATH SHAREPOINT_PATH RETAILER LAST_UPDATE'.split()]

您可以执行以下操作(从Aman的答案中借用零件):

cols = df.columns.tolist()
cols.insert(0, cols.pop(-1))

cols
>>>['mean', 0L, 1L, 2L, 3L, 4L]

df = df[cols]

一种简单的方法是用列列表重新分配数据帧,根据需要重新排列。

这就是你现在拥有的:

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']

按任意方式重新排列列。这是我将最后一个元素移动到第一个位置的方式:

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

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

然后重新排序数据帧,如下所示:

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

要根据其他列的名称将现有列设置为右侧/左侧,请执行以下操作:

def df_move_column(df, col_to_move, col_left_of_destiny="", right_of_col_bool=True):
    cols = list(df.columns.values)
    index_max = len(cols) - 1

    if not right_of_col_bool:
        # set left of a column "c", is like putting right of column previous to "c"
        # ... except if left of 1st column, then recursive call to set rest right to it
        aux = cols.index(col_left_of_destiny)
        if not aux:
            for g in [x for x in cols[::-1] if x != col_to_move]:
                df = df_move_column(
                        df, 
                        col_to_move=g, 
                        col_left_of_destiny=col_to_move
                        )
            return df
        col_left_of_destiny = cols[aux - 1]

    index_old = cols.index(col_to_move)
    index_new = 0
    if len(col_left_of_destiny):
        index_new = cols.index(col_left_of_destiny) + 1

    if index_old == index_new:
        return df

    if index_new < index_old:
        index_new = np.min([index_new, index_max])
        cols = (
            cols[:index_new]
            + [cols[index_old]]
            + cols[index_new:index_old]
            + cols[index_old + 1 :]
        )
    else:
        cols = (
            cols[:index_old]
            + cols[index_old + 1 : index_new]
            + [cols[index_old]]
            + cols[index_new:]
        )

    df = df[cols]
    return df

E.g.

cols = list("ABCD")
df2 = pd.DataFrame(np.arange(4)[np.newaxis, :], columns=cols)
for k in cols:
    print(30 * "-")
    for g in [x for x in cols if x != k]:
        df_new = df_move_column(df2, k, g)
        print(f"{k} after {g}:  {df_new.columns.values}")
for k in cols:
    print(30 * "-")
    for g in [x for x in cols if x != k]:
        df_new = df_move_column(df2, k, g, right_of_col_bool=False)
        print(f"{k} before {g}:  {df_new.columns.values}")

输出:

我尝试了创建一个order函数,您可以使用Stata的order命令对列进行重新排序/移动。最好创建一个py文件(其名称可能是order.py),并将其保存在目录中并调用它的函数

def order(dataframe,cols,f_or_l=None,before=None, after=None):

#만든이: 김완석, Stata로 뚝딱뚝딱 저자, blog.naver.com/sanzo213 운영
# 갖다 쓰시거나 수정을 하셔도 되지만 출처는 꼭 밝혀주세요
# cols옵션 및 befor/after옵션에 튜플이 가능하게끔 수정했으며, 오류문구 수정함(2021.07.12,1)
# 칼럼이 멀티인덱스인 상태에서 reset_index()메소드 사용했을 시 적용안되는 걸 수정함(2021.07.12,2) 

import pandas as pd
if (type(cols)==str) or (type(cols)==int) or (type(cols)==float) or (type(cols)==bool) or type(cols)==tuple:    
    cols=[cols]
    
dd=list(dataframe.columns)
for i in cols:
    i
    dd.remove(i) #cols요소를 제거함
    
if (f_or_l==None) & ((before==None) & (after==None)):
    print('f_or_l옵션을 쓰시거나 아니면 before옵션/after옵션 쓰셔야되요')
    
if ((f_or_l=='first') or (f_or_l=='last')) & ~((before==None) & (after==None)):
    print('f_or_l옵션 사용시 before after 옵션 사용불가입니다.')
    
if (f_or_l=='first') & (before==None) & (after==None):
    new_order=cols+dd
    dataframe=dataframe[new_order]
    return dataframe

if (f_or_l=='last') & (before==None) & (after==None):   
    new_order=dd+cols
    dataframe=dataframe[new_order]
    return dataframe
    
if (before!=None) & (after!=None):
    print('before옵션 after옵션 둘다 쓸 수 없습니다.')
    

if (before!=None) & (after==None) & (f_or_l==None):

    if not((type(before)==str) or (type(before)==int) or (type(before)==float) or
       (type(before)==bool) or ((type(before)!=list)) or 
       ((type(before)==tuple))):
        print('before옵션은 칼럼 하나만 입력가능하며 리스트 형태로도 입력하지 마세요.')
    
    else:
        b=dd[:dd.index(before)]
        a=dd[dd.index(before):]
        
        new_order=b+cols+a
        dataframe=dataframe[new_order]  
        return dataframe
    
if (after!=None) & (before==None) & (f_or_l==None):

    if not((type(after)==str) or (type(after)==int) or (type(after)==float) or
       (type(after)==bool) or ((type(after)!=list)) or 
       ((type(after)==tuple))):
            
        print('after옵션은 칼럼 하나만 입력가능하며 리스트 형태로도 입력하지 마세요.')  

    else:
        b=dd[:dd.index(after)+1]
        a=dd[dd.index(after)+1:]
        
        new_order=b+cols+a
        dataframe=dataframe[new_order]
        return dataframe

下面的python代码是我制作的order函数的一个示例。我希望您可以使用我的order函数轻松地对列进行重新排序:)

# module

import pandas as pd
import numpy as np
from order import order # call order function from order.py file

# make a dataset

columns='a b c d e f g h i j k'.split()
dic={}

n=-1
for i in columns:
    
    n+=1
    dic[i]=list(range(1+n,10+1+n))
data=pd.DataFrame(dic)
print(data)

# use order function (1) : order column e in the first

data2=order(data,'e',f_or_l='first')
print(data2)

# use order function (2): order column e in the last , "data" dataframe

print(order(data,'e',f_or_l='last'))


# use order function (3) : order column i before column c in "data" dataframe

print(order(data,'i',before='c'))


# use order function (4) : order column g after column b in "data" dataframe

print(order(data,'g',after='b'))

# use order function (4) : order columns ['c', 'd', 'e'] after column i in "data" dataframe

print(order(data,['c', 'd', 'e'],after='i'))