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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 you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

Decreasing Comments

How would you investigate a decline in the average number of comments per user?

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

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