pandas 移动数据框列并更改列顺序
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/50660949/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Moving a dataframe column and changing column order
提问by Stacey
I have a dataframe called dfwhich has the following columns header of data:
我有一个名为的数据框df，它具有以下列数据标题：
date A B C D E F G H I
07/03/2016 2.08 1 NaN NaN 1029 2 2.65 4861688 -0.0388
08/03/2016 2.20 1 NaN NaN 1089 2 2.20 5770819 -0.0447
: :
09/03/2016 2.14 1 NaN NaN 1059 2 2.01 5547959 -0.0514
10/03/2016 2.25 1 NaN NaN 1089 2 1.95 4064482 -0.0520
Is there a way to change the order of the columns so that column F is moved to a position that is after column H. The resulting dfwould look like:
有没有办法更改列的顺序，以便将 F 列移动到 H 列之后的位置。结果df如下所示：
date A B C D E F G H F I
07/03/2016 2.08 1 NaN NaN 1029 2 2.65 4861688 2 -0.0388
08/03/2016 2.20 1 NaN NaN 1089 2 2.20 5770819 2 -0.0447
: :
09/03/2016 2.14 1 NaN NaN 1059 2 2.01 5547959 2 -0.0514
10/03/2016 2.25 1 NaN NaN 1089 2 1.95 4064482 2 -0.0520
采纳答案by asapo kL
Use this :
用这个 ：
df = df[['date','A','B','C','D','E','F','G','H','F','I']]
--- Edit
- - 编辑
columnsName = list(df.columns)
F, H = columnsName.index('F'), columnsName.index('H')
columnsName[F], columnsName[H] = columnsName[H],columnsName[F]
df = df[columnsName]
回答by cs95
Use df.insertwith df.columns.get_locto dynamically determine the position of insertion.
使用df.insertwithdf.columns.get_loc动态确定插入的位置。
col = df['F'] # df.pop('F') # if you want it removed
df.insert(df.columns.get_loc('H') + 1, col.name, col, allow_duplicates=True)
df
date A B C D E F G H F I
0 07/03/2016 2.08 1 NaN NaN 1029 2 2.65 4861688 2 -0.0388
1 08/03/2016 2.20 1 NaN NaN 1089 2 2.20 5770819 2 -0.0447
...
回答by k.ko3n
Not for the author of this question, but perhaps for others.
不是针对这个问题的作者，而是针对其他人。
list = df.columns.tolist() # list the columns in the df
list.insert(8, list.pop(list.index('F'))) # Assign new position (i.e. 8) for "F"
df = df.reindex(columns= list) # Now move 'F' to ist new position
回答by jpp
This is one way via pd.DataFrame.iloc, which uses integer-location based indexing for selecting by position.
这是 via 的一种方式pd.DataFrame.iloc，它使用基于整数位置的索引来按位置进行选择。
It's also a gentle reminder that pandasinteger indexing is based on numpy.
这也是一个温和的提醒，pandas整数索引是基于numpy.
import pandas as pd
import numpy as np
df = pd.DataFrame(columns=list('ABCDEFGHI'))
cols = np.insert(np.arange(df.shape[1]),
df.columns.get_loc('H')+1,
df.columns.get_loc('F'))
res = df.iloc[:, cols]
print(res)
Empty DataFrame
Columns: [A, B, C, D, E, F, G, H, F, I]
Index: []
回答by llllllllll
You can use:
您可以使用：
df.reindex(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'F', 'I'], axis=1)
