sklearn train_test_split on pandas stratify by multiple columns : <p>I'm a relatively new user to sklearn and have run into some unexpected behavior in train_test_split from sklearn.model_selection. I have a pandas dataframe that I would like to split into a training and test set. I would like to stratify my data by at least 2, but ideally 4 columns in my dataframe. </p>

<p>There were no warnings from sklearn when I tried to do this, however I found later that there were repeated rows in my final data set. I created a sample test to show this behavior:</p>

<pre><code>from sklearn.model_selection import train_test_split
a = np.array([i for i in range(1000000)])
b = [i%10 for i in a]
c = [i%5 for i in a]
df = pd.DataFrame({'a':a, 'b':b, 'c':c})
</code></pre>

<p>It seems to work as expected if I stratify by either column:</p>

<pre><code>train, test = train_test_split(df, test_size=0.2, random_state=0, stratify=df[['b']])
print(len(train.a.values))  # prints 800000
print(len(set(train.a.values)))  # prints 800000

train, test = train_test_split(df, test_size=0.2, random_state=0, stratify=df[['c']])
print(len(train.a.values))  # prints 800000
print(len(set(train.a.values)))  # prints 800000
</code></pre>

<p>But when I try to stratify by both columns, I get repeated values:</p>

<pre><code>train, test = train_test_split(df, test_size=0.2, random_state=0, stratify=df[['b', 'c']])
print(len(train.a.values))  # prints 800000
print(len(set(train.a.values)))  # prints 640000
</code></pre>
