[FIXED] concat several pandas dataframes

Issue

df1 = pd.DataFrame({'a':['id1','id2','id3'],'b':['W','W','W'],'c1':[1,2,3]})
df2 = pd.DataFrame({'a':['id1','id2','id3'],'b':['W','W','W'],'c2':[4,5,6]})
df3 = pd.DataFrame({'a':['id1','id4','id5'],'b':['Q','Q','Q'],'c1':[7,8,9]})

I'm trying to concatenate df1 df2 and df3 into one dataframe:

a    b   c1   c2
id1  W   1    4
id2  W   2    5
id3  W   3    6
id1  Q   7    NA
id4  Q   8    NA
id5  Q   9    NA

I tried:

l = [d.set_index(['a','b']) for d in [df1,df2,df3]]
pd.concat(l, axis=1)

but the output isn't what I expected:

        c1   c2   c1
a   b               
id1 W  1.0  4.0  NaN
id2 W  2.0  5.0  NaN
id3 W  3.0  6.0  NaN
id1 Q  NaN  NaN  7.0
id4 Q  NaN  NaN  8.0
id5 Q  NaN  NaN  9.0

Solution

You can join MultiIndex Series created by DataFrame.stack:

l = [d.set_index(['a','b']).stack() for d in [df1,df2,df3]]
df = pd.concat(l).unstack().sort_index(level=[1,0], ascending=[False, True])
print (df)
        c1   c2
a   b          
id1 W  1.0  4.0
id2 W  2.0  5.0
id3 W  3.0  6.0
id1 Q  7.0  NaN
id4 Q  8.0  NaN
id5 Q  9.0  NaN

If there is only 3 columns DataFrames use DataFrame.squeeze or select first column by iloc[:, 0] for list of Series:

l = [d.set_index(['a','b']).squeeze() for d in [df1,df2,df3]]
keys = [x.name for x in l]
df = (pd.concat(l, axis=0, keys=keys)
        .unstack(0)
        .sort_index(level=[1,0], ascending=[False, True]))
print (df)
        c1   c2
a   b          
id1 W  1.0  4.0
id2 W  2.0  5.0
id3 W  3.0  6.0
id1 Q  7.0  NaN
id4 Q  8.0  NaN
id5 Q  9.0  NaN

---

l = [d.set_index(['a','b']).iloc[:, 0] for d in [df1,df2,df3]]
keys = [x.name for x in l]
df = (pd.concat(l, axis=0, keys=keys)
        .unstack(0)
        .sort_index(level=[1,0], ascending=[False, True]))

Another idea is chain multiple DataFrames in list by DataFrame.combine_first:

from functools import reduce

dfs = [d.set_index(['a','b']) for d in [df1,df2,df3]]
df = (reduce(lambda x, y: x.combine_first(y), dfs)
        .sort_index(level=[1,0], ascending=[False, True]))
print (df)
        c1   c2
a   b          
id1 W  1.0  4.0
id2 W  2.0  5.0
id3 W  3.0  6.0
id1 Q  7.0  NaN
id4 Q  8.0  NaN
id5 Q  9.0  NaN

Answered By - jezrael