Both join() and concat() way could solve the problem. However, there is one warning I have to mention: Reset the index before you join() or concat() if you trying to deal with some data frame by selecting some rows from another DataFrame.
One example below shows some interesting behavior of join and concat:
dat1 = pd.DataFrame({'dat1': range(4)})
dat2 = pd.DataFrame({'dat2': range(4,8)})
dat1.index = [1,3,5,7]
dat2.index = [2,4,6,8]
# way1 join 2 DataFrames
print(dat1.join(dat2))
# output
dat1 dat2
1 0 NaN
3 1 NaN
5 2 NaN
7 3 NaN
# way2 concat 2 DataFrames
print(pd.concat([dat1,dat2],axis=1))
#output
dat1 dat2
1 0.0 NaN
2 NaN 4.0
3 1.0 NaN
4 NaN 5.0
5 2.0 NaN
6 NaN 6.0
7 3.0 NaN
8 NaN 7.0
#reset index
dat1 = dat1.reset_index(drop=True)
dat2 = dat2.reset_index(drop=True)
#both 2 ways to get the same result
print(dat1.join(dat2))
dat1 dat2
0 0 4
1 1 5
2 2 6
3 3 7
print(pd.concat([dat1,dat2],axis=1))
dat1 dat2
0 0 4
1 1 5
2 2 6
3 3 7