Let’s say you work as a medical researcher.

You are given a dataframe of patient data containing the <code>age</code> of the patient and two columns, <code>smoking</code> and <code>cancer</code>, indicating if the patient is a smoker or has cancer, respectively.

Write a function, <code>stratified_split</code>, that splits the dataframe into train and test sets while preserving the approximate ratios for the values in a specified column (given by a <code>col</code> variable). Do not return the training set. Instead, return the number of columns in the training set that are in the <code>&#34;no&#34;</code> class of <code>col</code>

Note: Do not use <code>scikit-learn</code>.

Example:

Input:

<pre><code class="language-python"><pre tabindex="0" class="chroma"><code>
print(df)
...
 age smoking cancer
0 25 yes yes
1 32 no no
2 10 yes no
3 40 yes no
4 75 no no
5 80 yes no
6 60 yes no
7 60 no yes
8 40 yes yes
9 80 yes no
</code></pre></code></pre>

Output:

<pre><code class="language-python"><pre tabindex="0" class="chroma"><code>def stratified_split(df, train_ratio=0.7, col=&#39;cancer&#39;) -&gt; 5
</code></pre></code></pre>

The resulting dataframe was:

<pre><code class="language-python"><pre tabindex="0" class="chroma"><code> age smoking cancer
1 32 no no
3 40 yes no
4 75 no no
6 60 yes no
7 60 no yes
8 40 yes yes
9 80 yes no
</code></pre></code></pre>

Divided a data set into a training and testing set.

Stratified Split

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