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Product Metrics

Python

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You’re given a dataframe <code>df_rain</code> containing rainfall data. The dataframe has two columns: day of the week and rainfall in inches.

Write a function <code>median_rainfall</code> to find the median amount of rainfall for the days on which it rained.

Note: You may assume it rained on at least one of the days.

Input:

<pre tabindex="0" class="chroma"><code>import pandas as pd

rainfall = {&#34;Day&#34; : [&#34;Monday&#34;, &#34;Tuesday&#34;, &#34;Wednesday&#34;, &#34;Thursday&#34;, &#34;Friday&#34;], &#34;Inches&#34; : [0, 1.2, 0, 0.8, 1]}

df_rain = pd.DataFrame(rainfall)
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def median_rainfall(df_rain) -&gt; 1
</code></pre>

Write a function to find the median amount of rainfall for the days on which it rained.

Rain on Rainy Days

Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.

Write a query to randomly sample a row from this table.

Input:

<code>big_table</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>

Write a query to randomly sample a row from a big table.

Random SQL Sample

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

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