在您的情况下，

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

会做你想做的事。

在我的情况下（一般形式）：

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

与上面的答案类似，还有一种方法可以使用deque（）及其rotate（）方法。rotate方法获取列表中的最后一个元素并将其插入开头：

from collections import deque

columns = deque(df.columns.tolist())
columns.rotate()

df = df[columns]

在您的情况下，

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

会做你想做的事。

在我的情况下（一般形式）：

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

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：

假设您有列为A、B、C的df。

最简单的方法是：

df = df.reindex(['B','C','A'], axis=1)