Querying SQL Databases with Pandas

In order to efficiently store and manipulate data retrieved from SQL queries, it is necessary to convert the results into Pandas data structures.

Problem Statement:

A user seeks guidance on converting SQL query results into Pandas data structures. A sample query has been provided, and the user has indicated difficulty in understanding the return type of the query.

Solution:

To convert SQL query results to a Pandas DataFrame, the following steps can be taken:

1. Import the necessary libraries:

import pandas as pd
from sqlalchemy import create_engine

2. Create the SQL connection:

engine = create_engine('Your_SQL_Database_Url')
connection = engine.connect()

3. Execute the SQL query and retrieve results:

query = 'Your_SQL_Query'
results = connection.execute(query)

4. Convert results to a Pandas DataFrame:

df = pd.DataFrame(results.fetchall())
df.columns = results.keys()

Additional Considerations:

• Identifying Query Result Type: To determine the return type of the SQL query, you can use the fetchall() method to retrieve all rows of the result set. The output will be a list of tuples, with each tuple representing a row of data.
• Parsing Column Types: If desired, you can further parse the column types of the Pandas DataFrame by examining the description property of the query results.