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<p>A climate research organization has a time-series dataframe with daily temperature readings for different cities. This dataframe has three columns: <strong><code>date</code></strong>, <strong><code>city</code></strong>, and <strong><code>temperature</code></strong>.</p>

<p>Due to data recording issues, the temperature reading for some days might be missing. The organization requires the temperature readings for each day, so they ask you to interpolate the missing data.</p>

<p>Write a Python function using Pandas that uses a <strong>linear interpolation</strong> to estimate the missing data and fill out the dataframe.</p>

<p><strong><em>Notes:</em></strong></p>

<ul>
<li><em>When estimating the missing values for a city, the interpolation should only consider data from the same city.</em></li>
<li><em>Temperature recording issues are rare, so you can assume that there is no missing data two days in a row.</em></li>
<li><em>You can also assume that both the first and the last dates in your dataframe hold valid temperature data.</em></li>
</ul>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<table>
<thead>
<tr>
<th align="center">date</th>
<th align="center">city</th>
<th align="center">temperature</th>
</tr>
</thead>

<tbody>
<tr>
<td align="center">2023-01-01</td>
<td align="center">London</td>
<td align="center">10</td>
</tr>

<tr>
<td align="center">2023-01-02</td>
<td align="center">London</td>
<td align="center">NaN</td>
</tr>

<tr>
<td align="center">2023-01-03</td>
<td align="center">London</td>
<td align="center">12</td>
</tr>

<tr>
<td align="center">2023-01-04</td>
<td align="center">London</td>
<td align="center">NaN</td>
</tr>

<tr>
<td align="center">2023-01-05</td>
<td align="center">London</td>
<td align="center">14</td>
</tr>

<tr>
<td align="center">2023-01-01</td>
<td align="center">Berlin</td>
<td align="center">-2</td>
</tr>

<tr>
<td align="center">2023-01-02</td>
<td align="center">Berlin</td>
<td align="center">-1</td>
</tr>

<tr>
<td align="center">2023-01-03</td>
<td align="center">Berlin</td>
<td align="center">NaN</td>
</tr>

<tr>
<td align="center">2023-01-04</td>
<td align="center">Berlin</td>
<td align="center">1</td>
</tr>

<tr>
<td align="center">2023-01-05</td>
<td align="center">Berlin</td>
<td align="center">2</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th align="center">date</th>
<th align="center">city</th>
<th align="center">temperature</th>
</tr>
</thead>

<tbody>
<tr>
<td align="center">2023-01-01</td>
<td align="center">London</td>
<td align="center">10</td>
</tr>

<tr>
<td align="center">2023-01-02</td>
<td align="center">London</td>
<td align="center">11</td>
</tr>

<tr>
<td align="center">2023-01-03</td>
<td align="center">London</td>
<td align="center">12</td>
</tr>

<tr>
<td align="center">2023-01-04</td>
<td align="center">London</td>
<td align="center">13</td>
</tr>

<tr>
<td align="center">2023-01-05</td>
<td align="center">London</td>
<td align="center">14</td>
</tr>

<tr>
<td align="center">2023-01-01</td>
<td align="center">Berlin</td>
<td align="center">-2</td>
</tr>

<tr>
<td align="center">2023-01-02</td>
<td align="center">Berlin</td>
<td align="center">-1</td>
</tr>

<tr>
<td align="center">2023-01-03</td>
<td align="center">Berlin</td>
<td align="center">0</td>
</tr>

<tr>
<td align="center">2023-01-04</td>
<td align="center">Berlin</td>
<td align="center">1</td>
</tr>

<tr>
<td align="center">2023-01-05</td>
<td align="center">Berlin</td>
<td align="center">2</td>
</tr>
</tbody>
</table>


Interpolating Missing Temperatures

<p>Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.</p>

<p>What factors could have biased this result, and what would you look into?</p>


<p>What could potentially bias the average boarding time in favor of an airline that only flies domestically?</p>

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

<p>You are testing hundreds of hypotheses with many <em>t</em>-tests. What considerations should be made?</p>


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

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?

Question	Topic	Difficulty	Ask Chance
Interpolating Missing Temperatures	SQL	Medium	Very High
Boarding Times Bias	Statistics	Medium	Medium
Hundreds of Hypotheses	Statistics	Medium	Medium

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