Create a Client class that interacts with an SQLDatabase Server (provided below). The Client sends a SQLQuery to the SQLDatabase and receives a Dataframe from the SQLDatabase. The class should contain the default __dunders__ and potentially introduce some custom __dunders__. The SQLDatabase Server: import pandas as pd import sqlite3 class Server:     def __init__(self, database):         self.conn = sqlite3.connect(database)     def __enter__(self):         return self     def __exit__(self, exc_type, exc_val, exc_tb):         self.conn.close()     def __repr__(self):         return f"Server({self.conn})"         def execute_query(self, query):         cursor = self.conn.cursor()         cursor.execute(query)         columns = [desc[0] for desc in cursor.description]         results = cursor.fetchall()         df = pd.DataFrame(results, columns=columns)         cursor.close()         return df

Microsoft Visual C#
7th Edition
ISBN:9781337102100
Author:Joyce, Farrell.
Publisher:Joyce, Farrell.
Chapter14: Files And Streams
Section: Chapter Questions
Problem 2CP: In Chapter 11, you created the most recent version of the MarshallsRevenue program, which prompts...
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Create a Client class that interacts with an SQLDatabase Server (provided below). The Client sends a SQLQuery to the SQLDatabase and receives a Dataframe from the SQLDatabase. The class should contain the default __dunders__ and potentially introduce some custom __dunders__.

The SQLDatabase Server:

import pandas as pd

import sqlite3

class Server:

    def __init__(self, database):

        self.conn = sqlite3.connect(database)

    def __enter__(self):

        return self

    def __exit__(self, exc_type, exc_val, exc_tb):

        self.conn.close()

    def __repr__(self):

        return f"Server({self.conn})"    

    def execute_query(self, query):

        cursor = self.conn.cursor()

        cursor.execute(query)

        columns = [desc[0] for desc in cursor.description]

        results = cursor.fetchall()

        df = pd.DataFrame(results, columns=columns)

        cursor.close()

        return df

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