Second Highest Salary
<p>Table: <code>Employee</code></p>

<pre>
+-------------+------+
| Column Name | Type |
+-------------+------+
| id          | int  |
| salary      | int  |
+-------------+------+
id is the primary key (column with unique values) for this table.
Each row of this table contains information about the salary of an employee.
</pre>

<p>&nbsp;</p>

<p>Write a solution to find&nbsp;the second highest <strong>distinct</strong> salary from the <code>Employee</code> table. If there is no second highest salary,&nbsp;return&nbsp;<code>null (return&nbsp;None in Pandas)</code>.</p>

<p>The result format is in the following example.</p>

<p>&nbsp;</p>
<p><strong class="example">Example 1:</strong></p>

<pre>
<strong>Input:</strong> 
Employee table:
+----+--------+
| id | salary |
+----+--------+
| 1  | 100    |
| 2  | 200    |
| 3  | 300    |
+----+--------+
<strong>Output:</strong> 
+---------------------+
| SecondHighestSalary |
+---------------------+
| 200                 |
+---------------------+
</pre>

<p><strong class="example">Example 2:</strong></p>

<pre>
<strong>Input:</strong> 
Employee table:
+----+--------+
| id | salary |
+----+--------+
| 1  | 100    |
+----+--------+
<strong>Output:</strong> 
+---------------------+
| SecondHighestSalary |
+---------------------+
| null                |
+---------------------+
</pre>
Database
import pandas as pd


def second_highest_salary(employee: pd.DataFrame) -> pd.DataFrame:
    # Drop any duplicate salary values to avoid counting duplicates as separate salary ranks
    unique_salaries = employee["salary"].drop_duplicates()

    # Sort the unique salaries in descending order and get the second highest salary
    second_highest = (
        unique_salaries.nlargest(2).iloc[-1] if len(unique_salaries) >= 2 else None
    )

    # If the second highest salary doesn't exist (e.g., there are fewer than two unique salaries), return None
    if second_highest is None:
        return pd.DataFrame({"SecondHighestSalary": [None]})

    # Create a DataFrame with the second highest salary
    result_df = pd.DataFrame({"SecondHighestSalary": [second_highest]})

    return result_df
