Append column to pandas dataframe

This is probably easy, but I have the following data:

In data frame 1:

index dat1
0     9
1     5

In data frame 2:

index dat2
0     7
1     6

I want a data frame with the following form:

index dat1  dat2
0     9     7
1     5     6

I've tried using the append method, but I get a cross join (i.e. cartesian product).

What's the right way to do this?

It seems in general you're just looking for a join:

> dat1 = pd.DataFrame({'dat1': [9,5]})
> dat2 = pd.DataFrame({'dat2': [7,6]})
> dat1.join(dat2)
   dat1  dat2
0     9     7
1     5     6

You can also use:

dat1 = pd.concat([dat1, dat2], axis=1)

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