![]() ![]() ![]() Step 2: Use a window function to compute average distance-per-cost at year-month level.Step 1: At a given day, compute distance-per-cost and create year-month based on that day.The following are steps we need to take to solve this problem: Order your results by the earliest request date first. Also, assume that all dates are unique in the dataset. You should also count both success and failed request_status as the distance and cost values are populated for all ride requests. The output should include the year-month (YYYY-MM) and the average difference in distance-per-dollar for said year-month as an absolute value rounded to the 2nd decimal. Distance-per-dollar is defined as the distance traveled divided by the cost of the ride. For each date, find the difference between the distance-per-dollar for that date and the average distance-per-dollar for that year-month. SQL Problem: Distance Per Dollar You’re given a dataset of Uber rides with the traveling distance ("distance_to_travel") and cost ("monetary_cost") for each ride. ![]() I will use a common SQL interview problem to demonstrate the similarity between Subquery and CTE. In this article, I will explain the similarities and differences between Subquery and CTE. Both Subquery and CTE (Common Table Expression) are useful tools we can use to write a complex SQL query to implement data analysis, in a similar way as other data science tools, such as Pandas in Python and dplyr in R. Not only SQL can be used to query a database, it can also be used for data analysis. We can write a SQL query to select, filter, transform, insert, update, and delete the underlying data in the database. SQL is the programming language we need to interact with them. Many companies store their data in a relational database system, such as MySQL, PostgreSQL, MS SQL Server, SQLite. SQL is an essential skill for data science professionals. Photo by Mike Benna on Unsplash Background ![]()
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