Merged Pandas dataframe columns side by side-CodePudding

If I merge on two dataframes as shown

df1=pd.DataFrame({"Name": ["Alex","Ben","Cat","Dog"],'A':[1,2,3,3],'B':[2,3,4,4]})
df2=pd.DataFrame({"Name": ["Alex","Ben","Cat","Dog"],'A':[2,3,4,4],'B':[1,2,3,3]})

df_merged = pd.merge(df1,df2, on = "Name", suffixes= ["L","R"])

this is what my merged dataframe df_merged is like

Name    AL  BL  AR  BR
0   Alex 1  1   2   2   
1   Ben  2  3   3   2
2   Cat  3  4   4   3
3   Dog  3  4   4   3

But is there a way to get df_merged to look like

Name    AL AR BL BR
0   Alex 1  2  1  2 
1   Ben  2  3  3  2
2   Cat  3  4  4  3
3   Dog  3  4  4  3

where the columns of the dataframes are next to one other.

This is just an interest for cosmetic reasons rather than anything else. I couldn't find an example for it anywhere.

CodePudding user response：

You need change order of columns, here is dynamic approach:

#get all columns names in both df, removed Name used for key in merge
same = df1.columns.intersection(df2.columns, sort=False).difference(['Name'])

#generate columns in order
cols = [f'{a}{b}' for a in same for b in ['L','R']]

#get different like `cols` from merged df and add cols in order
order = df_merged.columns.difference(cols, sort=False).union(cols, sort=False)

#changed order by list
df = df_merged[order]
print (df)
   Name  AL  AR  BL  BR
0  Alex   1   2   2   1
1   Ben   2   3   3   2
2   Cat   3   4   4   3
3   Dog   3   4   4   3

Manually approach:

df = df_merged[['Name', 'AL', 'AR', 'BL', 'BR']]