[Python] Julia: equivalent of Pythons selection by multiindex level (especially columns)

I am new to Julia and Julia DataFrames. My understanding is that DataFrames do not support MultiIndexing, which generally does not pose much problems, but translating some pythonic habits to Julia poses difficulties. I wonder how one could load and subselect features by columns, as in the example below.

import numpy as np
import pandas as pd

#generating sample data
nsmpls = 10
smpls = [f'smpl{j}' for j in range(nsmpls)]

nfeats = 5
feats = [f'feat{j}' for j in range(nfeats)]

data = np.random.rand(nfeats, nsmpls)

countries = ['France'] * 2 + ['UK'] * 3 + ['US'] * 5

df = pd.DataFrame(data, index=feats, columns=pd.MultiIndex.from_tuples(zip(countries, smpls)))
df.to_csv('./data.tsv', sep='\t')

#---------------------------------------------------------------------
#loading dataset
df = pd.read_csv('./data.tsv', sep='\t', index_col=0, header=[0,1])

#extracting subset
dg = df.xs('France', level=0, axis=1)
print(dg.shape)

#iterating
for country, group in df.groupby(level=0, axis=1):
print('#samples: {}'.format(group.shape[1]))

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