concatenate multiple columns based on index in pandas
pandas, python
Solution
How about something like this?
>>> 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
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
In near-English:
>>> new = [' '.join(word for word in row if word is not None) for row in rows]
>>> new
['concat me', 'too', 'yo tambien']
Using `filter`:
>>> new = [' '.join(filter(None, row)) for row in rows]
>>> new
['concat me', 'too', 'yo tambien']
etc. You could do it in one line but I think it's clearer to separate it.
Problem
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 ``` thanks, zach cp