How to add a second level column header/index to dataframe by matching to dictionary values?

Question

I have an existing dataframe.

l1 | a    b    c  |  d    e    f
--------------------------------
0  | 1    1    1  |  1    1    1
1  | 1    1    1  |  1    1    1
2  | 1    1    1  |  1    1    1

And this dictionary.

{
  "a": "dog",
  "b": "dog",
  "c": "dog",
  "d": "fish",
  "e": "fish",
  "f": "fish",
}

What is the simplest way to create a second column level matching the existing columns to their category from the dictionary?

The real columns and dictionary are much bigger and less organized. 🙃

The result should look like this.

l2 |     dog      |     fish
l1 | a    b    c  |  d    e    f
--------------------------------
0  | 1    1    1  |  1    1    1
1  | 1    1    1  |  1    1    1
2  | 1    1    1  |  1    1    1

Answer 1

Add an extra mapping for the first series, then use pd.MultiIndex.from_tuples:

d.update({'l1': 'l2'})
df.columns = pd.MultiIndex.from_tuples([(d[k], k) for k in df.columns])

print(df)

  l2 dog       fish      
  l1   a  b  c    d  e  f
0  0   1  1  1    1  1  1
1  1   1  1  1    1  1  1
2  2   1  1  1    1  1  1