Is it possible to take one portion of a multi-index from the rows of a dataframe and apply it as columns

I have a dataframe with multi-indexed rows and a single index columns. "foo" is just the first EntityType, I will have others which will make this dataframe longer and harder to view.

I'd like to take the aggregation method index column and move it to the columns so I have a table like this

Is there an easy command to do this? Something like Melt or reset index? I'm just not even sure where to being googling to get the answer.

Here is an example of how to do it with Pandas pivot, as suggested in the comments, and reindex:

import random

import pandas as pd

# Toy dataframe (same as yours after reseting the index)
df = pd.DataFrame(
    {
        "Entity Type": ["foo" for _ in range(20)],
        "Quantile Range": ["q99_q100", "q90_q99", "q70_q90", "q00_q70"] * 5,
        "AggregationMethod": ["max", "avg", "std", "total", "count"] * 4,
        "bar": [random.uniform(1, 999) for _ in range(20)],
        "baz": [random.uniform(1, 999) for _ in range(20)],
    }
)

df = df.pivot(
    index=["Entity Type", "Quantile Range"],
    columns="AggregationMethod",
    values=["bar", "baz"],
).reindex(["max", "avg", "std", "total"], level=1, axis=1)

print(df)
# Output

                                   bar
AggregationMethod                  max         avg         std       total   
Entity Type Quantile Range
foo         q00_q70         141.752307  822.270987  199.740853  595.444166  \
            q70_q90         383.574450  410.730888  838.562828  545.299705   
            q90_q99         339.588340  606.983173  935.142608  407.674059   
            q99_q100        161.833517  932.267262  157.149458  618.105967   

                                   baz
AggregationMethod                  max         avg         std       total  
Entity Type Quantile Range
foo         q00_q70          17.986766  298.760555  389.559554   49.925246  
            q70_q90         888.435092  695.713473  502.429534  209.356226  
            q90_q99         715.425998  209.749918  136.480141  525.729657  
            q99_q100        705.721265  956.273655  684.883477   39.114393 

Posting my comment as an answer:

You can use .unstack for this, which does as the name suggests, removing the level of the MultiIndex to create a multi column index:

df.unstack()

This unstacks the last level by default, but you can specify this if you want to either with level="AggregationMethod", level=-1 (the default) or level=2.