Memory leak in pandas multiIndex (with minmum reproducible example)

Eariler today I posted this question. I now have a MRE that can reproduce the issue.

In short, this piece of code seems to be using much more memory than it should (the idea is to average some number of time-traces into a certain number of bins. The traces are arranged in a matrix using a pd.MultiIndex).

import numpy as np
import pandas as pd

#Len of each trace
trace_len =2500

#Number of Bins
bin_num = 300

# Traces matrix dimensions
L1 = 70
L2 = 100

index = pd.MultiIndex.from_product([range(L1), range(L2)])
traces = np.random.random((L1*L2, trace_len))
traces_df = pd.DataFrame(traces, index=index)

#Lets make 300 random bins
bins = [index.to_frame().sample(frac=1, replace=True) for _ in range(bin_num)]
bins = [pd.MultiIndex.from_frame(bin) for bin in bins]

def bin_single(traces: pd.DataFrame, bin_idx:pd.Index) -> np.array:
    """ Cumulative sum of all shots that are both in traces and bin_idx"""
    bin_idx = bin_idx.intersection(traces.index)
    binned = traces.reindex(bin_idx, copy=False)
    return binned.sum(axis=0, skipna=False).to_numpy()

output = np.empty((bin_num, trace_len))
for n, bin in enumerate(bins):
    output[n] = bin_single(traces_df, bin)

print(output.nbytes)

This is the memory allocation over time:

The issue cannot be due to lazy allocation of output, since that array is only 6Mb, as reported by output.nbytes, while the overall memory allocation grows by more than 200Mb over the for loop.

I think the problem might be hidden in the pd.MultiIndex usage, since this very similar program that does not use MultiIndex does not show the memory increase:

import numpy as np
import pandas as pd

#Len of each trace
trace_len =2500

#Number of Bins
bin_num = 300

# Traces matrix dimensions
L1 = 70
L2 = 100

#index = pd.MultiIndex.from_product([range(L1), range(L2)])
traces = np.random.random((L1*L2, trace_len))
traces_df = pd.DataFrame(traces)
index = traces_df.index

#Lets make 300 random bins
bins = [index.to_frame().sample(frac=1, replace=True) for _ in range(bin_num)]
bins = [pd.MultiIndex.from_frame(bin) for bin in bins]

def bin_single(traces: pd.DataFrame, bin_idx:pd.Index) -> np.array:
    """ Cumulative sum of all shots that are both in traces and bin_idx"""
    bin_idx = bin_idx.intersection(traces.index)
    binned = traces.reindex(bin_idx, copy=False)
    return binned.sum(axis=0, skipna=False).to_numpy()

output = np.empty((bin_num, trace_len))
for n, bin in enumerate(bins):
    output[n] = bin_single(traces_df, bin)

print(output.nbytes)

I tend to think that there might be a bug somewhere in pd.MultiIndex, but maybe I'm just overlooking something.

Thanks a lot!