


How to Create a Pivot Table Showing Average House Prices by Neighborhood and Bedroom Count in PostgreSQL?
Creating a Pivot Table in PostgreSQL to Show Average House Prices
Problem: How can we generate a summary table showing average house prices, broken down by neighborhood and number of bedrooms?
Solution: This involves a two-step process using PostgreSQL's crosstab
function (from the tablefunc
extension).
- Calculate Average Prices by Neighborhood and Bedroom Count: First, we determine the average price for each combination of neighborhood and bedroom count.
SELECT neighborhood, bedrooms, AVG(price) AS average_price FROM listings GROUP BY neighborhood, bedrooms ORDER BY neighborhood, bedrooms;
- Use the
crosstab
Function: Next, we feed the results from step 1 into thecrosstab
function. This function transforms the data into a pivot table. The second argument specifies the bedroom counts we want as columns. Note that you'll need to install thetablefunc
extension if you haven't already (CREATE EXTENSION tablefunc;
).
SELECT * FROM crosstab( 'SELECT neighborhood, bedrooms, AVG(price)::int AS average_price FROM listings GROUP BY neighborhood, bedrooms ORDER BY neighborhood, bedrooms', $$SELECT unnest('{0,1,2,3}'::int[]) AS bedrooms$$ ) AS ct ("neighborhood" text, "0" int, "1" int, "2" int, "3" int);
This query produces a pivot table with neighborhoods as rows and the average price for 0, 1, 2, and 3 bedrooms as columns. Remember to adjust the '{0,1,2,3}'
array to reflect the actual bedroom counts present in your listings
table. The ::int
cast ensures the average price is treated as an integer; you may need to adjust this based on your price
column's data type.
The above is the detailed content of How to Create a Pivot Table Showing Average House Prices by Neighborhood and Bedroom Count in PostgreSQL?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



The article discusses using MySQL's ALTER TABLE statement to modify tables, including adding/dropping columns, renaming tables/columns, and changing column data types.

Article discusses configuring SSL/TLS encryption for MySQL, including certificate generation and verification. Main issue is using self-signed certificates' security implications.[Character count: 159]

Article discusses popular MySQL GUI tools like MySQL Workbench and phpMyAdmin, comparing their features and suitability for beginners and advanced users.[159 characters]

Article discusses strategies for handling large datasets in MySQL, including partitioning, sharding, indexing, and query optimization.

InnoDB's full-text search capabilities are very powerful, which can significantly improve database query efficiency and ability to process large amounts of text data. 1) InnoDB implements full-text search through inverted indexing, supporting basic and advanced search queries. 2) Use MATCH and AGAINST keywords to search, support Boolean mode and phrase search. 3) Optimization methods include using word segmentation technology, periodic rebuilding of indexes and adjusting cache size to improve performance and accuracy.

The article discusses dropping tables in MySQL using the DROP TABLE statement, emphasizing precautions and risks. It highlights that the action is irreversible without backups, detailing recovery methods and potential production environment hazards.

Article discusses using foreign keys to represent relationships in databases, focusing on best practices, data integrity, and common pitfalls to avoid.

The article discusses creating indexes on JSON columns in various databases like PostgreSQL, MySQL, and MongoDB to enhance query performance. It explains the syntax and benefits of indexing specific JSON paths, and lists supported database systems.
