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

您需要按所需的顺序创建一个新的列列表，然后使用df=df[cols]以新的顺序重新排列列。

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

您也可以使用更通用的方法。在本例中，最后一列（由-1表示）作为第一列插入。

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

如果DataFrame中存在列，也可以使用此方法按所需顺序重新排序列。

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]

简单地说，

df = df[['mean'] + df.columns[:-1].tolist()]

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：

DataFrame.sort_index（axis=1）非常干净。请在此处检查文档。然后凹入

仅仅是翻转往往会有帮助。

df[df.columns[::-1]]

或者只是洗牌看看。

import random
cols = list(df.columns)
random.shuffle(cols)
df[cols]