根据 pandas 中的索引连接多个列 [英] concatenate multiple columns based on index in pandas
本文介绍了根据 pandas 中的索引连接多个列的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
作为此帖子的后续操作 ,我想根据它们的索引连接多个列,但是遇到一些问题.在此示例中,我得到了与map函数相关的Attribute错误.与执行列的等效串联的代码一样,可以帮助您解决此错误.
As a follow up to this post, I would like to concatenate a number of columns based on their index but I am encountering some problems. In this example I get an Attribute error related to the map function. Help around this error would be appreciated as would code that does the equivalent concatenation of columns.
#data
df = DataFrame({'A':['a','b','c'], 'B':['d','e','f'], 'C':['concat','me','yo'], 'D':['me','too','tambien']})
#row function to concat rows with index greater than 2
def cnc(row):
temp = []
for x in range(2,(len(row))):
if row[x] != None:
temp.append(row[x])
return map(concat, temp)
#apply function per row
new = df.apply(cnc,axis=1)
#Expected Output
new
concat me
me too
yo tambien
谢谢, 扎克cp
推荐答案
这样的事情怎么样?
>>> from pandas import *
>>> df = DataFrame({'A':['a','b','c'], 'B':['d','e','f'], 'C':['concat','me','yo'], 'D':['me','too','tambien']})
>>> df
A B C D
0 a d concat me
1 b e me too
2 c f yo tambien
>>> df.columns[2:]
Index([C, D], dtype=object)
>>> df[df.columns[2:]]
C D
0 concat me
1 me too
2 yo tambien
>>> [' '.join(row) for row in df[df.columns[2:]].values]
['concat me', 'me too', 'yo tambien']
>>> df["new"] = [' '.join(row) for row in df[df.columns[2:]].values]
>>> df
A B C D new
0 a d concat me concat me
1 b e me too me too
2 c f yo tambien yo tambien
如果周围有None
个对象,也可以进行处理.例如:
If you have None
objects floating around, you could handle that too. For example:
>>> df["C"][1] = None
>>> df
A B C D
0 a d concat me
1 b e None too
2 c f yo tambien
>>> rows = df[df.columns[2:]].values
接近英语:
>>> new = [' '.join(word for word in row if word is not None) for row in rows]
>>> new
['concat me', 'too', 'yo tambien']
使用filter
:
>>> new = [' '.join(filter(None, row)) for row in rows]
>>> new
['concat me', 'too', 'yo tambien']
等您可以一行完成它,但我认为将其分开会更清楚.
etc. You could do it in one line but I think it's clearer to separate it.
这篇关于根据 pandas 中的索引连接多个列的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!
查看全文