SQL Random Sampling per Group: An Introduction with Code Examples

Random sampling is a common statistical technique used to select a random subset of data from a larger population. In SQL, random sampling can be useful in a variety of scenarios, such as when working with large datasets that are too time-consuming or expensive to analyze in their entirety.

When working with group data, it is often necessary to sample randomly within each group, rather than across the entire dataset. This technique is known as "random sampling per group". In this article, we will discuss the basic concepts of random sampling per group and provide code examples in SQL to help you get started.

Before we dive into the code examples, let's take a closer look at the basic concepts of random sampling per group.

# Basic Concepts of Random Sampling per Group

In SQL, a "group" refers to a set of rows in a table that share the same values in one or more columns. For example, you might have a table with customer data, where each row represents a single customer and the columns include information such as their name, address, and purchase history. In this case, you could group the data by region, so that each group consists of customers from a particular region.

Random sampling per group involves selecting a random subset of data from each group, rather than selecting a random subset of data from the entire dataset. This is useful when you want to analyze data at the group level, or when you want to get a general sense of the distribution of data within each group.

# Code Examples in SQL

There are several different ways to implement random sampling per group in SQL, depending on the database management system (DBMS) you are using. In this section, we will provide code examples for two of the most popular DBMSs: PostgreSQL and MySQL.

## PostgreSQL

In PostgreSQL, you can use the `percentage`

argument in the `ntile`

function to randomly sample data per group. The `ntile`

function divides the data into a specified number of buckets, and the `percentage`

argument determines the size of the sample. For example, if you want to sample 10% of the data from each group, you would use a `percentage`

argument of 0.1.

Here is an example of how to randomly sample 10% of the data from each region in a customer data table:

```
SELECT *
FROM (
SELECT *,
NTILE(10) OVER (PARTITION BY region) as bucket
FROM customer_data
) sub
WHERE random() < 0.1
```

In this example, the `ntile`

function is used to divide the data into 10 buckets (or "tiles") per region. The `random`

function is then used to randomly select a subset of data from each bucket, based on the `percentage`

argument.

## MySQL

In MySQL, you can use the `RAND()`

function in combination with the `GROUP BY`

clause to randomly sample data per group. The `RAND()`

function generates a random number between 0 and 1, and you can use this number to determine which rows to include in the sample.

Here is an example of how to randomly sample 10% of the data from each region in a customer data table:

```
SELECT *
FROM customer_data
WHERE RAND() < 0.1
GROUP BY region
```

In this example, the `RAND()`

function is used to generate a random number for each row in the `customer_data`

table. The `WHERE

# Adjacent Topics to Random Sampling per Group in SQL

In addition to random sampling per group, there are several other related topics that are useful to understand when working with data in SQL. These include:

## Group By Clause

The `GROUP BY`

clause is used in SQL to group rows in a table based on the values in one or more columns. The `GROUP BY`

clause is typically used in combination with aggregate functions, such as `SUM`

, `AVG`

, or `COUNT`

, to perform calculations on the grouped data.

For example, you could use the `GROUP BY`

clause to group customer data by region and then use an aggregate function to calculate the total sales for each region:

```
SELECT region, SUM(sales) as total_sales
FROM customer_data
GROUP BY region
```

## Window Functions

Window functions are a type of function in SQL that allow you to perform calculations across a set of rows that are related to the current row in some way. Window functions are often used in combination with the `OVER`

clause to specify the set of rows to include in the calculation.

Window functions are a powerful tool for data analysis and can be used for tasks such as calculating running totals, ranking data, or calculating percentiles.

For example, you could use a window function to calculate the cumulative sum of sales for each customer in a table:

```
SELECT customer_id, sales, SUM(sales) OVER (ORDER BY customer_id) as cumulative_sales
FROM sales_data
```

## Stratified Sampling

Stratified sampling is a type of random sampling that involves dividing the data into distinct groups or "strata" and then randomly selecting a sample from each stratum. The goal of stratified sampling is to ensure that the sample is representative of the population as a whole, by including data from all relevant strata.

For example, you might have a table of customer data with columns for age, income, and region. To perform stratified sampling, you could divide the data into strata based on age, income, and region and then randomly sample data from each stratum:

```
SELECT *
FROM (
SELECT *,
NTILE(10) OVER (PARTITION BY age, income, region) as bucket
FROM customer_data
) sub
WHERE random() < 0.1
```

In this example, the data is divided into strata based on the values in the `age`

, `income`

, and `region`

columns. The `ntile`

function is then used to divide the data within each stratum into 10 buckets, and the `random`

function is used to randomly select data from each bucket.

In conclusion, random sampling per group is a useful technique for analyzing data in SQL, and there are several related topics, such as the `GROUP BY`

clause, window functions, and stratified sampling, that are also useful to understand. By combining these concepts, you can perform a wide range of data analysis tasks in SQL to gain insights into your data.

## Popular questions

- What is the purpose of random sampling per group in SQL?

The purpose of random sampling per group in SQL is to select a random subset of rows from a table for each distinct group of data. This is useful for a variety of tasks, such as data exploration, feature selection, or model validation. Random sampling per group allows you to select a representative sample of data for each group, rather than just a random sample of the overall data.

- How do you perform random sampling per group in SQL?

You can perform random sampling per group in SQL by using the `GROUP BY`

clause to group the data, and the `ORDER BY`

clause to randomly order the data within each group. You can then use the `LIMIT`

clause to select a specified number of rows from each group.

For example, to select 5 random rows from each group in a table, you could use the following SQL code:

```
SELECT *
FROM (
SELECT *,
ROW_NUMBER() OVER (PARTITION BY group_column ORDER BY RAND()) as row_num
FROM table_name
) sub
WHERE row_num <= 5
```

- What is the
`GROUP BY`

clause in SQL?

The `GROUP BY`

clause in SQL is used to group rows in a table based on the values in one or more columns. The `GROUP BY`

clause is typically used in combination with aggregate functions, such as `SUM`

, `AVG`

, or `COUNT`

, to perform calculations on the grouped data.

For example, you could use the `GROUP BY`

clause to group customer data by region and then use an aggregate function to calculate the total sales for each region:

```
SELECT region, SUM(sales) as total_sales
FROM customer_data
GROUP BY region
```

- What is the
`ROW_NUMBER`

function in SQL?

The `ROW_NUMBER`

function in SQL is a window function that assigns a unique number to each row in a result set. The `ROW_NUMBER`

function can be used in combination with the `PARTITION BY`

and `ORDER BY`

clauses to assign numbers to rows within a specified set of rows.

For example, you could use the `ROW_NUMBER`

function to assign a unique number to each row in a table, and then use the `WHERE`

clause to select a specified number of rows:

```
SELECT *
FROM (
SELECT *,
ROW_NUMBER() OVER (ORDER BY column_name) as row_num
FROM table_name
) sub
WHERE row_num <= 5
```

- What is the difference between random sampling per group and stratified sampling?

Random sampling per group and stratified sampling are both types of random sampling in SQL, but they differ in how the data is divided into samples. Random sampling per group involves selecting a random subset of rows for each distinct group of data, whereas stratified sampling involves dividing the data into distinct groups or "strata" and then randomly selecting a sample from each stratum. The goal of stratified sampling is to ensure that the sample is representative of the population as a whole, by including data from all relevant strata.

### Tag

Sampling