Having trouble using loc with multiple indexes in pandas

I appreciate your help. So I have these two dfs. Notice how df2 has two indexes:

import pandas as pd


data1 = {
    "col 1": ['a', 'b', 'c'],
    "col 2": ['x', 'y', 'z']
}

index1 = ['a', 'b', 'c']
index2 = ['x', 'y', 'z']

data2 = {
  "col 1": [420, 380, 390],
  "col 2": [50, 40, 45]
}

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2, index=[index1, index2])

df1:

  col 1 col 2
0     a     x
1     b     y
2     c     z

df2:

     col 1  col 2
a x    420     50
b y    380     40
c z    390     45

Now I'm trying to make a call where df1 has a third column that takes the first (left-most) index from df2 to the column without turning any of df2's indexes into a column. I've been reading online and the most I could find was the df.index() tool, with the argument being which index specifically used. It's not working for me. Here's my call:

df2['col 3'] = df1.loc[df1['col 1'] == df2.index(0)]

There's an error involved, which has left me stumped.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-25-2f901269f295> in <module>
----> 1 df2['col 3'] = df1.loc[df1['col 1'] == df2.index(0)]

TypeError: 'MultiIndex' object is not callable

What can I do to fix this? Thanks!

The error message is indicating that you cannot call the index attribute of a pandas DataFrame using parentheses like a function. Instead, you need to use square brackets to access a specific level of the multi-index.

In your case, you can access the first level of the multi-index using df2.index.levels[0]. Then, you can use the get_level_values method to get the values of the first level of the multi-index for each row in df2. Finally, you can assign the resulting array to a new column of df1.

Here's an updated version of your code that should do what you're looking for:

import pandas as pd

data1 = {
    "col 1": ['a', 'b', 'c'],
    "col 2": ['x', 'y', 'z']
}

index1 = ['a', 'b', 'c']
index2 = ['x', 'y', 'z']

data2 = {
  "col 1": [420, 380, 390],
  "col 2": [50, 40, 45]
}

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2, index=[index1, index2])

df1['col 3'] = df2.index.get_level_values(0)

Output:

  col 1 col 2 col 3
0     a     x     a
1     b     y     b
2     c     z     c

As you can see, a new column named col 3 has been added to df1, with the values from the first level of the multi-index from df2.