Name | n3_chennan_CS_ershoufang_fang_com |
---|---|
Data | 40.78M (+ 0B) |
Tables | 3 (+ 0) |
Columns | 18 (+ 0) |
Table Rows | 220,668 (+ 0) |
Media | 0B (+ 0B) |
Files | 0 (+ 0) |
Last Commit | 2023-06-19 18:36:38 (+ 169 d) |
全国620个城市的94,139个二手房数据库。每个二手房信息中包含有标题,价格,单价,面积和优势。该二手房数据库中共有3个表。
From this China second-hand house database, there are 94,139 second-hand housing records from 620 cities in China. Each record consists of title, price, unit price, acreage and advantage. The whole China second-hand housing database has 3 tables in total.
Tables | Rows | Columns | Non-empty |
---|---|---|---|
ershoufang | 94,139 | youshi |
81.82%
|
title |
100%
|
||
jiage |
100%
|
||
danjia |
100%
|
||
mianji |
100%
|
||
chengshi | 620 | title |
100%
|
chengshi_x_ershoufang | 125,909 | chengshi_id |
100%
|
ershoufang_id |
100%
|
No media sets.
Time | Data | Tables | Columns | Rows | Media | Files |
---|---|---|---|---|---|---|
2023-06-19 (+ 169 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2023-01-01 (+ 95 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2022-09-27 (+ 271 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-12-30 (+ 115 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-09-05 (+ 150 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-04-08 (+ 68 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-01-29 (+ 120 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-10-01 (+ 38 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-08-23 (+ 141 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-04-04 (+ 196 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-09-21 (+ 197 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-03-07 (+ 43 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-01-23 (+ 44 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-12-09 (+ 29 d) | 40.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-11-09 (+ 27 d) | 40.78M (- 1M) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-10-13 (+ 29 d) | 41.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-09-13 (+ 34 d) | 41.78M (+ 0B) | 3 (+ 0) | 18 (+ 0) | 220,668 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-08-09 | 41.78M | 3 | 18 | 220,668 | 0B | 0 |
Contact us for pricing to download the latest commit / release of this database.
In the same time, you can also access this data set via API.
Select a membership plan and sign up. Return to this page, click Online Query to access the API query maker. Create and open an API call to acquire the data.
The scanning and profiling of data size increments are done separately from those of the number of rows.
Subscribe to be notified of major data releases and updates.
STAY IN THE LOOP.