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
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
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]
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
我想到了和Dmitriy Work一样的答案，显然是最简单的：
df["mean"] = df.mean(1)
l = list(np.arange(0,len(df.columns) -1 ))
l.insert(0,-1)
df.iloc[:,l]
这里有一个非常简单的答案（只有一行）。
在将“n”列添加到df中之后，可以执行以下操作。
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10, 5))
df['mean'] = df.mean(1)
df
0 1 2 3 4 mean
0 0.929616 0.316376 0.183919 0.204560 0.567725 0.440439
1 0.595545 0.964515 0.653177 0.748907 0.653570 0.723143
2 0.747715 0.961307 0.008388 0.106444 0.298704 0.424512
3 0.656411 0.809813 0.872176 0.964648 0.723685 0.805347
4 0.642475 0.717454 0.467599 0.325585 0.439645 0.518551
5 0.729689 0.994015 0.676874 0.790823 0.170914 0.672463
6 0.026849 0.800370 0.903723 0.024676 0.491747 0.449473
7 0.526255 0.596366 0.051958 0.895090 0.728266 0.559587
8 0.818350 0.500223 0.810189 0.095969 0.218950 0.488736
9 0.258719 0.468106 0.459373 0.709510 0.178053 0.414752
### here you can add below line and it should work
# Don't forget the two (()) 'brackets' around columns names.Otherwise, it'll give you an error.
df = df[list(('mean',0, 1, 2,3,4))]
df
mean 0 1 2 3 4
0 0.440439 0.929616 0.316376 0.183919 0.204560 0.567725
1 0.723143 0.595545 0.964515 0.653177 0.748907 0.653570
2 0.424512 0.747715 0.961307 0.008388 0.106444 0.298704
3 0.805347 0.656411 0.809813 0.872176 0.964648 0.723685
4 0.518551 0.642475 0.717454 0.467599 0.325585 0.439645
5 0.672463 0.729689 0.994015 0.676874 0.790823 0.170914
6 0.449473 0.026849 0.800370 0.903723 0.024676 0.491747
7 0.559587 0.526255 0.596366 0.051958 0.895090 0.728266
8 0.488736 0.818350 0.500223 0.810189 0.095969 0.218950
9 0.414752 0.258719 0.468106 0.459373 0.709510 0.178053
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：
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