[英]How to iterate over columns and connacenate two columns into one
I have a dataframe:我有一个 dataframe:
Border #1 [from] Border #1 [to] Border #2 [from] Border #2 [to]
index
0 BE BE_AL PL SK
1 BE BE_AL PL SK
And I want to connect every two columns into one (I have many more columns), the desired result:我想将每两列连接成一列(我还有更多列),期望的结果:
Border #1 Border #2
index
0 BE_BE_AL PL_SK
1 BE_BE_AL PL_SK
For one column I could do:对于一列,我可以这样做:
df['Border#1']=df['Border #1 [from]']+'_'+df['Border #1 [to]']
but how can I do it for multiple columns?但我怎样才能为多列做到这一点?
Create MutliIndex
by split by [
with space, so possible select both levels by DataFrame.xs
and join by +
:创建MutliIndex
通过[
用空格分割,所以可能 select 两个级别都可以通过DataFrame.xs
和加入+
:
df.columns = df.columns.str.strip(']').str.split('\s+\[', expand=True)
print (df)
Border #1 Border #2
from to from to
0 BE BE_AL PL SK
1 BE BE_AL PL SK
print (df.columns)
MultiIndex([('Border #1', 'from'),
('Border #1', 'to'),
('Border #2', 'from'),
('Border #2', 'to')],
)
df = df.xs('from', axis=1, level=1) +'_'+ df.xs('to', axis=1, level=1)
print (df)
Border #1 Border #2
0 BE_BE_AL PL_SK
1 BE_BE_AL PL_SK
You can group the columns and craft a new dataframe您可以对列进行分组并制作新的 dataframe
groups = df.columns.str.replace(' \[.+\]', '', regex=True)
df2 = pd.concat({g: d.apply('_'.join, axis=1)
for g,d in df.groupby(groups, axis=1)}, axis=1)
output: output:
Border #1 Border #2
index
0 BE_BE_AL PL_SK
1 BE_BE_AL PL_SK
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