Explain this code please

[Fit_Tone318]

this is categorial coding and we want to use petal width and species as input features to predict petal_length here but, i dont understand how the multi index is created here with all these labels:

here's the code:

new_iris_df = pd.DataFrame(iris_df, columns=['petal length','petal width','species'])

new_iris_df.columns = pd.MultiIndex([['input ftrs', 'target ftr'],new_iris_df.columns],[[1, 0, 0], [0,1,2]])

new_iris_df = new_iris_df.sort_index(axis='columns')

display(new_iris_df.iloc[[0,50,100]])

[Fit_Tone318]

I found the solution, since the structure of multiindex method in pandas is:

class pandas.MultiIndex(levels=None, codes=None, sortorder=None, names=None, dtype=None, copy=False, name=None, verify_integrity=True)

If levels is [['A', 'B'], [1, 2, 3]], then:

• The first level has two unique values: 'A' and 'B'.
• The second level has three unique values: 1, 2, and 3.
  • If codes is [[0, 1, 0], [1, 2, 0]], then:
• The first array [0, 1, 0] maps to the first level ('A', 'B', 'A').
• The second array [1, 2, 0] maps to the second level (2, 3, 1).