Study on Tokyo Rent Using Python (3-1 of 3)
Result excerpt
Simultaneous posting on the blog: https://leoluistudio.com/blog/10/python%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E6%9D%B1%E4%BA%AC%E9%83%BD%E5%AE%B6%E8%B3%83%E3%81%AB%E3%81%A4%E3%81%84%E3%81%A6%E3%81%AE%E7%A0%94%E7%A9%B6-3%E3%81%AE1-%E7%B5%90%E6%9E%9C%E6%8A%9C/
Research goals and methods
Target
――I want to study the relationship between rent in Tokyo and related attributes. --Graph the results of the relevance and visualize it
Method
--Automatically collects rent data posted online in August 2020 by a program (data source: https://suumo.jp) --Analysis based on data from 82,812 buildings and 624,499 rooms
Excerpt of research results
Distribution of rent prices in Tokyo
X is the rent divided into groups (yen), Y is the number (k is 1,000)
Relationship between price and area (divided into 23 wards and city)
Blue dots are 23 wards, red dots are city areas, X is area (square meter), Y is rent (M is one million yen)
Distribution of average price for each ward (23 wards)
--X is a number (k is 1,000)
--Y is the 23 wards
--How to read the box plot:
--The leftmost line is the minimum value
--The left side of the box is the first quartile
--The line in the box is the median
--The left side of the box is the 3rd quartile
--The rightmost line is the maximum value
Direction and price heatmap for municipalities
--X is 8 directions
--Y is the 23 wards
--The brighter the color, the higher the price
--The darker the color (blue), the lower the price
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