How to Convert SQL Query Results into a Pandas DataFrame?

Converting SQL Query Results to pandas DataFrames

In this scenario, you aim to retrieve data from an SQL database using a query and store it in a pandas data structure. The question revolves around the data type of the query result and how to convert it to a DataFrame.

Understanding the Query Result

The sample query you provided performs various summations on fields from the table "daily_report_cooked." The result is a collection of rows, each containing the aggregate values for a particular campaign.

Converting to a pandas DataFrame

To convert the query result to a DataFrame, follow these steps:

1. Create a pandas DataFrame: Construct an empty DataFrame using the DataFrame class without any arguments.
2. Fetch the query results: Use the fetchall() method of the resoverall object to obtain a list of tuples representing the rows of the query result.
3. Populate the DataFrame: Assign the list of tuples to the DataFrame's data property.
4. Set column names: Retrieve the column names from the resoverall object using the keys() method, and assign them to the DataFrame's columns property.

The following code snippet demonstrates the conversion process:

from pandas import DataFrame
df = DataFrame(resoverall.fetchall())
df.columns = resoverall.keys()

Now, the variable df represents a pandas DataFrame containing the query results. It provides a structured representation of the data, enabling further analysis and manipulation using pandas' extensive functionality.

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