Can I Pay For A Year Of Netflix latest 2023

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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 will perform calculations on a set of rows related to your current row. They are considered advanced SQL and are often asked for in data science interviews. It is also used a lot at work to solve many types of problems. Let’s summarize the 4 different types of window functions and explain why and when to use them.

4 kinds of window functions

1. Regular aggregation functions

o These are aggregates like AVG, MIN/MAX, COUNT, SUM

o You will want to use these to aggregate your data and group it into another column like month or year

2. Classification functions

o ROW_NUMBER, RANK, RANK_DENSE

o These are functions that help you classify your data. You can either categorize all your data or categorize it by groups, for example by month or by country

o Extremely useful for generating ranking indexes within groups

3. Generation of statistics

o They are great if you need to generate simple statistics like NTILE (percentiles, quartiles, medians)

o You can use it for your whole dataset or by group

4. Processing time series data

o A very common window function, especially if you need to calculate trends like a month-over-month moving average or a measure of growth

o LAG and LEAD are the two functions that allow you to do this.

1. Regular aggregation function

Regular aggregate functions are functions like average, count, sum, min/max that are applied to columns. The purpose is to apply the aggregate function if you want to apply aggregations to different groups in the dataset, such as month.

This is similar to the type of calculation that can be performed with an aggregate function that you will find in the SELECT clause, but unlike regular aggregate functions, window functions do not aggregate multiple rows into a single row of output, they are grouped together or retain their own identity, depending on how you find them.

Avg() Example:

Let’s look at an example of an avg() window function implemented to answer a data analysis question. You can see the question and write the code in the link below:

platform.stratascratch.com/coding-question?id=10302&python=

This is a perfect example of using a window function and then applying an avg() to a group of months. Here we are trying to calculate the average distance per dollar per month. It’s hard to do in SQL without this window function. Here we applied the window function avg() to the 3rd column where we found the average value for month-year for each month-year in 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 would look like this:

SELECT a.request_date,

a.dist_to_cost,

AVG(a.dist_to_cost) OVER(PARTITION BY a.request_mnth) AS avg_dist_to_cost

OF

(TO SELECT *,

to_char(request_date::date, ‘YYYY-MM’) AS request_mnth,

(distance_to_travel/monetary_cost) AS dist_to_cost

FROM uber_request_logs) a

ORDER BY request_date

2. Classification functions

Ranking functions are an important utility for a data scientist. You are always ranking and indexing your data to better understand which rows are the best in your data set. SQL window functions give you 3 rank utilities — RANK(), DENSE_RANK(), ROW_NUMBER() — depending on your exact use case. These functions will help you list your data in order and in groups according to what you desire.

Rank() Example:

Let’s look at an example rank window function to see how we can rank data within groups using SQL window functions. Follow interactively with this link: platform.stratascratch.com/coding-question?id=9898&python=

Here we want to find the best salaries per department. We can’t just find the top 3 salaries without a window feature, because that will just give us the top 3 salaries across all departments, so we need to rank the salaries by department individually. This is done by rank() and partitioned by department. From there it is very easy to filter the top 3 in all departments

Here is the code to generate this table. You can copy and paste into the SQL editor in the link above and see the same output.

SELECT Department,

salary,

RANK() OVER (PARTITION BY a.department

ORDER BY a.salary DESC) AS rank_id

OF

(SELECT department, salary

FROM twitter_employee

GROUP BY department, salary

ORDER BY department, salary) a

ORDER BY department,

DESC salary

3. NTILE

NTILE is a very useful function for those who work in data analysis, business analysis and data science. Often, when you have statistical data at maturity, you probably need to create robust statistics such as quartile, quintile, median, decile in your daily work, and NTILE makes it easy to generate these outputs.

NTILE takes an argument of the number of bins (or basically the number of buckets you want to divide your data into) and then creates that number of bins by dividing your data into that number of bins. You define how the data is sorted and partitioned, if you want additional groupings.

NTILE(100) Example

In this example, we’ll learn how to use NTILE to classify our data into percentiles. You can follow the link interactively here: platform.stratascratch.com/coding-question?id=10303&python=

What you’re trying to do here is identify the top 5% of claims based on an algorithm-generated score. But you can’t just find the top 5% and order by because you want to find the top 5% by state. So one way to do this is to use a sort function NTILE() then PARTITION by state. You can then apply a filter in the WHERE clause to get the top 5%.

Here is the code to display the entire table above. You can copy and paste it in the link above.

SELECT policy_num,

State,

claim_cost,

score_fraud,

percentile

OF

(TO SELECT *,

NTILE(100) ON(PARTITION BY state

ORDER BY fraud_score DESC) AS percentile

FROM fraud_score) a

WHERE percentile <=5

4. Processing time series data

LAG and LEAD are two useful window functions for dealing with time series data. The only difference between LAG and LEAD is whether you want to extract previous rows or next rows, almost like sampling from previous data or future data.

You can use LAG and LEAD to calculate monthly growth or moving averages. As a data scientist and business analyst, you are always dealing with time series data and creating these temporal metrics.

LAG() Example:

In this example, we want to find the year-over-year growth percentage, a very common question that data scientists and business analysts answer on a daily basis. The problem statement, data and SQL editor can be found in the following link if you want to try coding the solution on your own: platform.stratascratch.com/coding-question?id=9637&python=

What’s tricky with this problem is that the data is set up – you need to use the value from the previous row in your metric. But SQL is not designed for that. SQL is designed to calculate anything you want as long as the values ​​are on the same line. So we can use window function lag() or lead() which will take previous or next rows and place them in your current row, which is what this question does.

Here is the code to display the entire table above. You can copy and paste the code into the SQL editor in the link above:

SELECT the year,

host_of_the_current_year,

prev_year_host,

round(((current_year_host – prev_year_host)/(cast(prev_year_host AS numeric)))*100) estimate_growth

OF

(SELECT year,

host_of_the_current_year,

LAG(current_year_host, 1) OVER (ORDER BY year) AS prev_year_host

OF

(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) t1) t2

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