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Handling Time Series Window Functions in Data Science Interviews
Data scientists manage time series data on a daily basis, and being able to manipulate and analyze that data is a necessary part of the job. The SQL window functions allow you to do just that, and it’s a common data science interview question. So let’s talk about what time series data is, when to use it, and how to implement features to help manage time series data.
What is time series data?
Time series data are variables in your data that have a time component. This means that each value of this attribute has either a date or time value, sometimes both. Here are some examples of time series data:
• The daily stock price of companies because each stock price is associated with a specific day
• The average daily value of the stock index over the past years because each value is assigned to a specific day
• Unique visits to a website during a month
• Registrations on the platform every day
• Monthly sales and income
• Daily logins for an application
LAG and LEAD window functions
When dealing with time series data, a common calculation is to calculate growth or averages over time. This means you need to take the future date or past date and its associated values.
Two WINDOW functions that allow you to do this are LAG and LEAD, which are very useful for dealing with time-related data. The main difference between LAG and LEAD is that LAG fetches data from the previous rows while LEAD is the opposite, fetches data from the following rows.
We can use either function to compare month-on-month growth, for example. As a data analytics professional, you will most likely work with time-related data, and if you can use LAG or LEAD efficiently, you will be a very productive data scientist.
A data science interview question that requires a window function
Let’s go through an advanced data science sql interview question that deals with this window function. You’ll find that window functions are often part of interview questions, but you’ll also see them a lot in your day-to-day work, so it’s important to know how to use them.
Let’s go through an Airbnb question called Airbnb Growth. If you want to follow along interactively, you can do so here.
The point is to estimate Airbnb’s growth each year using the number of registered hosts as a growth metric. The growth rate is calculated by taking ((number of registered hosts in the current year – number of registered hosts in the previous year) / number of registered hosts in the previous year) * 100.
Output of the year, number of hosts for the current year, number of hosts for the previous year and the growth rate. Round the growth rate to the nearest percentage and sort the result in ascending order by year.
Approach Step 1: Count the current year’s host
The first step is to count hosts by year, so we’ll need to extract the year from the date values.
SELECT extract (year
FROM host_since::date) AS year,
count(id) current_year_host
FROM airbnb_search_details
WHERE host_since IS NOT NULL
GROUP BY extract (year
FROM host_since::date)
ORDER BY YEAR
Approach Step 2: Count the previous year’s host.
This is where you will use the LAG window function. Here you will create a view where we have the year, the number of hosts for the current year, and then the number of hosts for the previous year. Use a delay function for last year’s count and take last year’s value and put it in the same row as this year’s count. This way you will have 3 columns in your view: year, current year host count and last year host count. The LAG feature allows you to easily pull last year’s host count in your row. This makes it easy for you to implement any metric, such as a growth rate, because you have all the values you need in one row for SQL to easily calculate a metric. Here is the code:
SELECT year,
host_current_year,
LAG(current_year_host, 1) OVER (ORDER BY year) AS prev_year_host
FROM
(SELECT statement (year
FROM host_since::date) AS year,
count(id) current_year_host
FROM airbnb_search_details
WHERE host_since IS NOT NULL
GROUP BY extract (year
FROM host_since::date)
ORDER BY year) t1) t2
Approach 3: Implement the growth metric
As mentioned above, it is much easier to implement a metric like the following when all the values are in one row. This is why you perform the LAG function. Implement the growth rate calculation round(((current_year_host – prev_year_host)/(cast(prev_year_host AS numeric)))*100) estimated_growth
SELECT year,
host_current_year,
prev_year_host,
round(((host_current_year – host_previous_year)/(cast(host_previous_year AS numeric)))*100) estimated_growth
FROM
(SELECT year,
host_current_year,
LAG(current_year_host, 1) OVER (ORDER BY year) AS prev_year_host
FROM
(SELECT statement (year
FROM host_since::date) AS year,
count(id) current_year_host
FROM airbnb_search_details
WHERE host_since IS NOT NULL
GROUP BY extract (year
FROM host_since::date)
ORDER BY year) t1) t2
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