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

一种简单的方法是使用set(),特别是当您有一长串列并且不想手动处理它们时:

cols = list(set(df.columns.tolist()) - set(['mean']))
cols.insert(0, 'mean')
df = df[cols]

其他回答

您可以使用可用于两个轴的重新索引:

df
#           0         1         2         3         4      mean
# 0  0.943825  0.202490  0.071908  0.452985  0.678397  0.469921
# 1  0.745569  0.103029  0.268984  0.663710  0.037813  0.363821
# 2  0.693016  0.621525  0.031589  0.956703  0.118434  0.484254
# 3  0.284922  0.527293  0.791596  0.243768  0.629102  0.495336
# 4  0.354870  0.113014  0.326395  0.656415  0.172445  0.324628
# 5  0.815584  0.532382  0.195437  0.829670  0.019001  0.478415
# 6  0.944587  0.068690  0.811771  0.006846  0.698785  0.506136
# 7  0.595077  0.437571  0.023520  0.772187  0.862554  0.538182
# 8  0.700771  0.413958  0.097996  0.355228  0.656919  0.444974
# 9  0.263138  0.906283  0.121386  0.624336  0.859904  0.555009

df.reindex(['mean', *range(5)], axis=1)

#        mean         0         1         2         3         4
# 0  0.469921  0.943825  0.202490  0.071908  0.452985  0.678397
# 1  0.363821  0.745569  0.103029  0.268984  0.663710  0.037813
# 2  0.484254  0.693016  0.621525  0.031589  0.956703  0.118434
# 3  0.495336  0.284922  0.527293  0.791596  0.243768  0.629102
# 4  0.324628  0.354870  0.113014  0.326395  0.656415  0.172445
# 5  0.478415  0.815584  0.532382  0.195437  0.829670  0.019001
# 6  0.506136  0.944587  0.068690  0.811771  0.006846  0.698785
# 7  0.538182  0.595077  0.437571  0.023520  0.772187  0.862554
# 8  0.444974  0.700771  0.413958  0.097996  0.355228  0.656919
# 9  0.555009  0.263138  0.906283  0.121386  0.624336  0.859904

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'))

将任意列移动到任意位置:

import pandas as pd
df = pd.DataFrame({"A": [1,2,3], 
                   "B": [2,4,8], 
                   "C": [5,5,5]})

cols = df.columns.tolist()
column_to_move = "C"
new_position = 1

cols.insert(new_position, cols.pop(cols.index(column_to_move)))
df = df[cols]

只需键入要更改的列名,然后为新位置设置索引。

def change_column_order(df, col_name, index):
    cols = df.columns.tolist()
    cols.remove(col_name)
    cols.insert(index, col_name)
    return df[cols]

对于您的情况,这将是:

df = change_column_order(df, 'mean', 0)