In the 2nd example, the column gender is reverse-lexicographically sorted (male then female).
df = DataFrame(
{
"gender": ["male", "male", "female", "male", "female", "male"],
"education": ["low", "medium", "high", "low", "high", "low"],
"country": ["US", "FR", "US", "FR", "FR", "FR"],
}
)
gb = df.groupby(["country", "gender"], as_index=True, sort=False)
result = gb.value_counts(sort=False)
print(result)
# country gender education
# US male low 1
# FR male medium 1
# US female high 1
# FR male low 2
# female high 1
# dtype: int64
gb2 = df.groupby(["country", "gender"], as_index=True, sort=False)["education"]
result2 = gb2.value_counts(sort=False)
print(result2)
# country gender education
# US male low 1
# FR male low 2
# medium 1
# US female high 1
# FR female high 1
# Name: education, dtype: int64
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