I have a complex dataframe with multiple columns. All of them being MultiIndex based. At some point I wanted to be quite specific when it comes to estimating some metrics so I started experimenting with the .groupby method. I can manage to do the basics: 1) computing the aggregation method on the whole dataframe or 2) computing it for one specific column. However, I am interested in computing the aggreagtion method by indicating some of the names within the first column levels. This is quite easy to do when there is just a single level within the columns. In order to be understood, I created the following MRO that reproduces my idea and the errors I am getting:

import numpy as np
import pandas as pd


columns = pd.MultiIndex.from_tuples(
    [
        ("Dimensions", "x"),
        ("Dimensions", "y"),
        ("Dimensions", "z"),
        ("Coefficient", ""),
        ("Comments", ""),
    ],
    names=["Category", "Details"],
)

df = pd.DataFrame(index=range(11), columns=columns)
df[("Dimensions", "x")] = np.random.randint(1, 100, size=11)
df[("Dimensions", "y")] = np.random.randint(1, 100, size=11)
df[("Dimensions", "z")] = np.random.randint(1, 100, size=11)
df[("Coefficient", "")] = np.random.randint(1, 50, size=11)  # Coefficient como entero aleatorio
df[("Comments", "")] = np.random.choice(["Good", "Average", "Bad"], size=11)
df["Comments"] = df["Comments"].astype("category")

# Basic metrics
print(df.groupby("Comments").mean())  # It works
print(df.groupby("Comments")["Dimensions"].mean())  # It works

# Selecting multiple columns within a MultiIndex based one. Different ideas I tried:
df.groupby("Comments")["Dimensions", "Coefficient"].mean()  # It does not work
df.groupby("Comments")[["Dimensions", "Coefficient"]].mean()  # It does not work
df.groupby("Comments").agg({"Dimensions": "mean", "Coefficient": "mean"})  # It does not work