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SQL Window Functions on Data Science Interviews Asked By Airbnb, Netflix, Twitter, and Uber
Window functions are a group of functions that perform calculations on a set of lines relative to your current line. They are considered advanced sql and are often asked in data science interviews. It is also used at work to solve many different types of problems. Let’s summarize the 4 different types of window functions and cover why and when you should use them.
4 Types of Window Functions
1. Regular aggregate functions
o These are aggregates such as AVG, MIN/MAX, COUNT, SUM
o You want to use it to aggregate your data and group it in another column like month or year
2. Ranking functions
or ROW_NUMBER, RANK, RANK_DENSE
o These are functions that help you rank your data. You can rank your entire dataset or rank it by groups such as month or country
o Very useful for creating rank indexes within groups
3. Generate statistics
o This is great if you need to do simple statistics like NTILE (percentiles, quartiles, median)
o You can use it for your entire dataset or a group
4. Time series data management
o A common window function especially if you need to calculate trends such as a month-to-month rolling average or a growth metric
o LAG and LEAD are two functions that allow you to do this.
1. Regular aggregate function
Regular aggregate functions are functions like average, count, sum, min/max applied to columns. The goal is to use the aggregate function when you want to apply aggregations to different dataset groups, such as month.
This is similar to the type of calculation that an aggregate function can do that you’d find in a SELECT clause, but unlike regular aggregate functions, window functions don’t group multiple rows into a single output row. , they are grouped together or keep their own identities, depending on how you look for them.
Let’s look at an example of an avg() window function implemented to answer a data analytics question. You can view the question and write the code in the link below:
This is a perfect example of using a window function and then applying an avg() to a month group. Here we try to calculate the average distance per dollar of the month. This is difficult to do in SQL without this window function. Here we apply the avg() window function in the 3rd column where we find the average value for the month-year for each month-year of the dataset. We can use this metric to calculate the difference between the month average and the date average for each request date in the table.
The code to implement the window function looks like this:
AVG(a.dist_to_cost) OVER(PARTITION BY a.request_mnth) AS avg_dist_to_cost
to_char(request_date::date, ‘YYYY-MM’) AS request_mnth,
(travel_distance/money_cost) AS dist_to_cost
FROM uber_request_logs) a
ORDER BY request_date
2. Ranking Activities
Ranking functions are an important tool for a data scientist. You are constantly ranking and indexing your data to better understand which rows perform best in your dataset. The SQL window functions provide you with 3 ranking utilities — RANK(), DENSE_RANK(), ROW_NUMBER() — depending on your exact use case. These functions help you list your data in order and in groups based on your preferences.
Let’s look at a rank window function example to see how we can rank data within groups using SQL window functions. Follow the interactive method at this link: platform.stratascratch.com/coding-question?id=9898&python=
Here we want to find the highest salary in the department. We can’t just find the top 3 salaries without the window function because it will only give us the top 3 salaries of all departments, so we have to rank the salaries of the departments individually. This is done by rank() and divided by department. From there it’s very easy to filter for the top 3 in all departments
Here is the code to output this table. You can copy and paste the SQL editor in the link above and see the same output.
RANK() OVER (PARTITION OF a.department
ORDER BY a.salary DESC) AS rank_id
(SELECT department, salary
GROUP IN department, salary
ORDER OF department, salary) a
NTILE is a very useful function for those in data analytics, business analytics, and data science. Often times the deadline with statistical data, you probably need to create strong statistics such as quartile, quintile, median, decile in your daily work, and NTILE makes it easy to do it. outputs.
NTILE takes an argument of the number of bins (or generally how many buckets you want to divide your data into), and then creates this number of bins by dividing your data into a large number of bins. You set how to order and divide the data, if you want more groups.
NTILE(100) For example
In this example, we will learn how to use NTILE to categorize our data into percentages. You can follow along interactively at the link here: platform.stratascratch.com/coding-question?id=10303&python=
What you’re trying to do here is identify the top 5 percent of claims based on the single-point algorithm outputs. But you can’t find the top 5% and make an order because you want to find the top 5% in the state. So one way to do this is to use the NTILE() ranking function and then PARTITION the state. You can use a filter in the WHERE clause to get the top 5%.
Here is the code to output the entire table above. You can copy and paste it from the link above.
NTILE(100) OVER(PARTITION BY state
ORDER BY fraud_score DESC) AS percentile
FROM fraud_score) a
WHERE percentage <=5
4. Time series data management
LAG and LEAD are two window functions that are useful for dealing with time series data. The only difference between LAG and LEAD is whether you want to retrieve from previous rows or following rows, almost like sampling from previous data or future data.
You can use LAG and LEAD to calculate month-over-month growth or rolling averages. As a data scientist and business analyst, you often deal with time series data and create time metrics.
In this example, we want to find the percentage growth per year, which is a common question that data scientists and business analysts answer every day. The problem statement, data, and SQL editor are available at the following link if you want to try coding the solution yourself: platform.stratascratch.com/coding-question?id=9637&python=
The trick with this problem is that the data is set up — you have to use the value of the previous row in your metric. But SQL wasn’t built to do that. SQL is built to calculate anything you want as long as the values are in the same row. So we can use the lag () or lead () window function that will take the previous or next rows and put them in your current row which is what this query does.
Here is the code to output the entire table above. You can copy and paste the code in the SQL editor in the link above:
round(((current_year_host – prev_year_host)/(cast(prev_year_host AS numeric)))*100) estimated_growth
LAG(current_year_host, 1) OVER (ORDER BY year) AS prev_year_host
(SELECT extract (year
FROM host_since::date) AS year,
WHERE host_since IS NOT NULL
GROUP BY extract(year
ORDER BY year) t1) t2
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