Name | n3_chennan_CS_ershoufang_jiwu_com |
---|---|
Data | 784.36M (+ 0B) |
Tables | 7 (+ 0) |
Columns | 41 (+ 0) |
Table Rows | 2,539,623 (+ 0) |
Media | 0B (+ 0B) |
Files | 0 (+ 0) |
Last Commit | 2023-06-19 18:36:38 (+ 169 d) |
来自全国31个省,426个城市的的359,524二手房数据库,每个二手房记录中包含有总价,单价,房源描述,首付,贷款金额,支付利息,月供,均价,年代,装修,楼层,朝向,更新时间和地址。所有这些二手房被分为426大类别。除此之外,数据中还包含1,819,692张二手房图片。该全国二手房信息数据库中共有7个表。
From this China second-hand houses database, it contians 359,524 second-hand houses from 426 cities of 31 provinces in China. Each second-hand housing is comprised of total price, unit price, housing description, down payment, loan amount, interest payment, monthly payment, average price, years, decoration, floor, orientation, update time and address. All these second-hand housing are typed into 426 categories. Beside, it also has 1,819,692 images of these houses. The whole China second-hand houses database has 7 tables in total.
Tables | Rows | Columns | Non-empty |
---|---|---|---|
image | 1,819,692 | ershoufang_id |
100%
|
ershoufang | 359,524 | shoufu |
99.79%
|
daikuan_jine |
99.79%
|
||
zhifu_lixi |
99.79%
|
||
yuegong |
99.79%
|
||
junjia |
99.79%
|
||
niandai |
99.79%
|
||
zhuangxiu |
93.37%
|
||
louceng |
99.79%
|
||
chaoxing |
93.37%
|
||
gangxin_shijian |
99.79%
|
||
dizhi |
99.79%
|
||
fangyuan_miaoshu |
89.46%
|
||
danjia |
100%
|
||
title |
100%
|
||
zongjia |
100%
|
||
sheng | 31 | title |
100%
|
category_1 | 426 | chengshi_id |
100%
|
chengshi |
100%
|
||
category_1_x_ershoufang | 359,524 | category_1_id |
100%
|
ershoufang_id |
100%
|
||
chengshi | 426 | title |
100%
|
sheng_id |
100%
|
Size (Bytes) | Files |
---|---|
0P | 0 |
Time | Data | Tables | Columns | Rows | Media | Files |
---|---|---|---|---|---|---|
2023-06-19 (+ 169 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2023-01-01 (+ 95 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2022-09-27 (+ 271 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-12-30 (+ 115 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-09-05 (+ 150 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-04-08 (+ 68 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-01-29 (+ 120 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-10-01 (+ 38 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-08-23 (+ 141 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-04-04 (+ 196 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-09-21 (+ 197 d) | 784.36M (+ 0B) | 7 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-03-07 (+ 43 d) | 784.36M (+ 96K) | 7 (+ 1) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-01-23 (+ 44 d) | 784.27M (+ 0B) | 6 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-12-09 (+ 29 d) | 784.27M (+ 0B) | 6 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-11-09 (+ 27 d) | 784.27M (+ 597.80M) | 6 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-10-13 (+ 29 d) | 186.47M (+ 0B) | 6 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-09-13 (+ 34 d) | 186.47M (+ 0B) | 6 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-08-09 (+ 27 d) | 186.47M (+ 0B) | 6 (+ 0) | 41 (+ 0) | 2,539,623 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-07-12 | 186.47M | 6 | 41 | 2,539,623 | 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.