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

另一种选择是使用set_index()方法,后跟reset_index()。请注意,我们首先pop()将要移动到数据帧前面的列,以便在重置索引时避免名称冲突:

df.set_index(df.pop('column_name'), inplace=True)
df.reset_index(inplace=True)

有关详细信息,请参阅How to change the order of dataframe columns in panda。

其他回答

大多数答案都不够概括,panda reindex_axis方法有点乏味,因此我提供了一个简单的函数,可以使用字典将任意数量的列移动到任意位置,其中key=列名,value=要移动到的位置。如果数据帧很大,请将True传递给“big_data”,那么函数将返回有序的列列表。您可以使用此列表来分割数据。

def order_column(df, columns, big_data = False):

    """Re-Orders dataFrame column(s)
       Parameters : 
       df      -- dataframe
       columns -- a dictionary:
                  key   = current column position/index or column name
                  value = position to move it to  
       big_data -- boolean 
                  True = returns only the ordered columns as a list
                          the user user can then slice the data using this
                          ordered column
                  False = default - return a copy of the dataframe
    """
    ordered_col = df.columns.tolist()

    for key, value in columns.items():

        ordered_col.remove(key)
        ordered_col.insert(value, key)

    if big_data:

        return ordered_col

    return df[ordered_col]

# e.g.
df = pd.DataFrame({'chicken wings': np.random.rand(10, 1).flatten(), 'taco': np.random.rand(10,1).flatten(),
                          'coffee': np.random.rand(10, 1).flatten()})
df['mean'] = df.mean(1)

df = order_column(df, {'mean': 0, 'coffee':1 })

>>>

col = order_column(df, {'mean': 0, 'coffee':1 }, True)

col
>>>
['mean', 'coffee', 'chicken wings', 'taco']

# you could grab it by doing this

df = df[col]

我认为这个函数更简单。您只需在开始或结束处或同时指定列的子集:

def reorder_df_columns(df, start=None, end=None):
    """
        This function reorder columns of a DataFrame.
        It takes columns given in the list `start` and move them to the left.
        Its also takes columns in `end` and move them to the right.
    """
    if start is None:
        start = []
    if end is None:
        end = []
    assert isinstance(start, list) and isinstance(end, list)
    cols = list(df.columns)
    for c in start:
        if c not in cols:
            start.remove(c)
    for c in end:
        if c not in cols or c in start:
            end.remove(c)
    for c in start + end:
        cols.remove(c)
    cols = start + cols + end
    return df[cols]

简单地说,

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

我自己也遇到了一个类似的问题,只是想补充一下我已经解决的问题。我喜欢用于更改列顺序的reindex_axis()方法。这是有效的:

df = df.reindex_axis(['mean'] + list(df.columns[:-1]), axis=1)

另一种基于@Jorge评论的方法:

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

虽然reindex_axis在微基准测试中似乎比reindex稍快,但我认为我更喜欢后者,因为它的直接性。

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