Let’s say you’re given a dataframe of standardized test scores from high schoolers from grades 9 to 12 called df_grades.

Given the dataset, write code function in Pandas called bucket_test_scores to return the cumulative percentage of students that received scores within the buckets of &lt;50, &lt;75, &lt;90, &lt;100.

Example:

Input:

<pre tabindex="0" class="chroma"><code>print(df_grades)
</code></pre>

<table>
<thead>
<tr>
<th>user_id</th>
<th>grade</th>
<th>test score</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>10</td>
<td>85</td>
</tr>

<tr>
<td>2</td>
<td>10</td>
<td>60</td>
</tr>

<tr>
<td>3</td>
<td>11</td>
<td>90</td>
</tr>

<tr>
<td>4</td>
<td>10</td>
<td>30</td>
</tr>

<tr>
<td>5</td>
<td>11</td>
<td>99</td>
</tr>
</tbody>
</table>
Output:

<pre tabindex="0" class="chroma"><code>def bucket_test_scores(df_grades) -&gt;
</code></pre>

<table>
<thead>
<tr>
<th>grade</th>
<th>test score</th>
<th>percentage</th>
</tr>
</thead>

<tbody>
<tr>
<td>10</td>
<td>&lt;50</td>
<td>33%</td>
</tr>

<tr>
<td>10</td>
<td>&lt;75</td>
<td>66%</td>
</tr>

<tr>
<td>10</td>
<td>&lt;90</td>
<td>100%</td>
</tr>

<tr>
<td>10</td>
<td>&lt;100</td>
<td>100%</td>
</tr>

<tr>
<td>11</td>
<td>&lt;50</td>
<td>0%</td>
</tr>

<tr>
<td>11</td>
<td>&lt;75</td>
<td>0%</td>
</tr>

<tr>
<td>11</td>
<td>&lt;90</td>
<td>50%</td>
</tr>

<tr>
<td>11</td>
<td>&lt;100</td>
<td>100%</td>
</tr>
</tbody>
</table>

Write a function to return the cumulative percentage of students that received scores within certain buckets.

Bucket Test Scores

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