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

其他回答

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

这个问题以前已经回答过,但reindex_axis现在已被弃用,因此我建议使用:

df = df.reindex(sorted(df.columns), axis=1)

对于那些想要指定他们想要的顺序而不是仅仅对它们进行排序的人来说,下面列出了解决方案:

df = df.reindex(['the','order','you','want'], axis=1)

现在,如何对列名列表排序真的不是熊猫问题,而是Python列表操作问题。有很多方法可以做到这一点,我认为这个答案有一个非常简洁的方法。

使用T怎么样?

df = df.T.reindex(['mean', 0, 1, 2, 3, 4]).T

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)

简单地说,

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