Understanding the Problem: Converting SQL Server Query to PandasQL for Averaging Time Differences in Minutes

As a data analyst, working with different databases and data manipulation languages can be a daunting task. In this article, we will delve into the world of SQL Server queries and PandasQL, exploring how to convert a SQL Server query to PandasQL while achieving the desired outcome: averaging time differences between two process-related timestamps.

Background Information

SQL Server is an object-relational database management system developed by Microsoft. It supports various programming languages, including T-SQL (Transact-SQL), which is used for writing queries and stored procedures. On the other hand, PandasQL is a Python library that allows you to execute SQL-like commands on data frames.

Problem Statement

The problem at hand involves translating a SQL Server query into PandasQL. The original query calculates the average time difference between two timestamps in minutes. However, the conversion of this query to PandasQL resulted in an incorrect output, where the day difference is returned instead of the desired hour and minute values.

Step 1: Understanding the Original Query

The original SQL Server query uses the DATEDIFF function to calculate the time difference between two timestamps:

select payment_method, cast(avg(cast(cast(DATEDIFF(second, booking_created_time, booking_paid_time) as float)/60 as float)) as decimal(20,2)) as difference_minute  
from fact_flight_sales  
group by payment_method

This query returns the average time difference in minutes for each payment_method group.

Step 2: Understanding the PandasQL Query

The provided PandasQL code attempts to achieve a similar result:

q2 = """
select payment_method, booking_created_time, booking_paid_time, (booking_created_time-booking_paid_time)
from dffact_flight_sales  
group by payment_method

"""
print(sqldf(q2, locals()))

However, this query returns the day difference instead of the desired hour and minute values.

Step 3: Converting SQL Server Query to PandasQL

To resolve the issue, we need to convert the SQL Server query into a format that can be executed in PandasQL. The key difference lies in how timestamps are handled. In SQL Server, DATEDIFF returns the total number of seconds between two dates and times, while in PandasQL, pd.to_datetime() converts the string values to datetime objects.

Here’s an updated version of the code that uses the total_seconds() method to convert the time difference into minutes:

import pandas as pd

# Load the CSV file
dffact_flight_sales = pd.read_csv(r"C:\Users\lixfe\Desktop\fact_flight_sales.csv")

# Convert the 'booking_paid_time' and 'booking_created_time' columns to datetime objects
dffact_flight_sales['booking_paid_time'] = pd.to_datetime(dffact_flight_sales['booking_paid_time'])
dffact_flight_sales['booking_created_time'] = pd.to_datetime(dffact_flight_sales['booking_created_time'])

# Calculate the time difference in minutes
dffact_flight_sales['time difference'] = ((dffact_flight_sales['booking_paid_time'] - 
                            dffact_flight_sales['booking_created_time']).dt.total_seconds() / 60)

# Group by 'payment_method' and calculate the average time difference in minutes
GK = dffact_flight_sales.groupby('payment_method')
GK1 = GK[['payment_method', 'time difference']]
print(GK1.first())

Step 4: Executing the Query with sqldf

To execute this query, we can use the sqldf function from the pandasql library. This function allows us to execute SQL-like commands on data frames.

import pandasql as psq

# Execute the query using sqldf
q2 = """
select payment_method, booking_paid_time, booking_created_time, (booking_paid_time-booking_created_time)
from dffact_flight_sales  
group by payment_method

"""
print(psq.sqldf(q2, locals()))

However, note that sqldf only supports basic SQL syntax and might not work with more complex queries.

Step 5: Verifying the Results

To verify the results, we can print out the first few rows of the resulting data frame:

print(GK1.head())

This will display the average time difference in minutes for each payment_method group.

Conclusion

In this article, we explored how to convert a SQL Server query into PandasQL while achieving the desired outcome: averaging time differences between two process-related timestamps. By understanding the differences in how timestamps are handled in SQL Server and PandasQL, we can write more efficient and effective queries for our data analysis needs.

Additional Tips

• When working with timestamp columns in PandasQL, make sure to convert them to datetime objects using pd.to_datetime().
• Use the dt.total_seconds() method to calculate time differences in seconds.
• Group by relevant columns using the groupby() function and select only the desired columns.

By following these steps and tips, you can write efficient and effective PandasQL queries for your data analysis needs.

Last modified on 2025-02-03