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：）

我有一个在panda中重新排序列名的非常具体的用例。有时我在基于现有列的数据帧中创建一个新列。默认情况下，panda将在末尾插入我的新列，但我希望新列插入到它派生的现有列旁边。

def rearrange_list(input_list, input_item_to_move, input_item_insert_here):
    '''
    Helper function to re-arrange the order of items in a list.
    Useful for moving column in pandas dataframe.

    Inputs:
        input_list - list
        input_item_to_move - item in list to move
        input_item_insert_here - item in list, insert before 

    returns:
        output_list
    '''
    # make copy for output, make sure it's a list
    output_list = list(input_list)

    # index of item to move
    idx_move = output_list.index(input_item_to_move)

    # pop off the item to move
    itm_move = output_list.pop(idx_move)

    # index of item to insert here
    idx_insert = output_list.index(input_item_insert_here)

    # insert item to move into here
    output_list.insert(idx_insert, itm_move)

    return output_list


import pandas as pd

# step 1: create sample dataframe
df = pd.DataFrame({
    'motorcycle': ['motorcycle1', 'motorcycle2', 'motorcycle3'],
    'initial_odometer': [101, 500, 322],
    'final_odometer': [201, 515, 463],
    'other_col_1': ['blah', 'blah', 'blah'],
    'other_col_2': ['blah', 'blah', 'blah']
})
print('Step 1: create sample dataframe')
display(df)
print()

# step 2: add new column that is difference between final and initial
df['change_odometer'] = df['final_odometer']-df['initial_odometer']
print('Step 2: add new column')
display(df)
print()

# step 3: rearrange columns
ls_cols = df.columns
ls_cols = rearrange_list(ls_cols, 'change_odometer', 'final_odometer')
df=df[ls_cols]
print('Step 3: rearrange columns')
display(df)

这里有一个函数可以对任意数量的列执行此操作。

def mean_first(df):
    ncols = df.shape[1]        # Get the number of columns
    index = list(range(ncols)) # Create an index to reorder the columns
    index.insert(0,ncols)      # This puts the last column at the front
    return(df.assign(mean=df.mean(1)).iloc[:,index]) # new df with last column (mean) first

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)

对我来说，一个非常简单的解决方案是在df.columns上使用.rendex：

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

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：