Add columns to existing dataframe with a loop

June 20, 2023

I try to build a dataframe with the first columns of several other dataframes with a loop. All of them have same index.

df1 = pd.DataFrame(np.random.randint(0,100,size=(3, 2)), columns=list('AB'), index=('class1', 'class2', 'class3'))
df2 = pd.DataFrame(np.random.randint(0,100,size=(3, 2)), columns=list('CD'), index=('class1', 'class2', 'class3')) 
df3 = pd.DataFrame(np.random.randint(0,100,size=(3, 2)), columns=list('EF'), index=('class1', 'class2', 'class3'))

df = pd.DataFrame( index=('class1', 'class2', 'class3')) 

for f in [df1, df2, df3]:
    first_col = f.iloc[:,0]
    df[f] = first_col.values

The expected output is a matrix with same formatting as below:

         A   C   E
class1   2  18  62
class2  46  46  11
class3  57  73  92

But this code did not work.

The question mirror this query, but the answers tried (below) did not work.
How to add a new column to an existing DataFrame?

df.set_index([first_col], append=True)

df.assign(f=first_col.values)

>Solution :

Your solution is possible if change name of new columns in ouput DataFrame:

df = pd.DataFrame(index=('class1', 'class2', 'class3')) 

r = [df1, df2, df3] 
for f in r: 
    first_col = f.iloc[:,0] 
    df[f.columns[0]] = first_col
print (df)
         A   C   E
class1  10  25  85
class2  45  57  48
class3  59  99  87

Better is use concat with list comprehension for select first column:

r=[df1, df2, df3] 

df = pd.concat([f.iloc[:, 0] for f in r], axis=1)
print (df)
         A   C   E
class1   2  18  62
class2  46  46  11
class3  57  73  92