Name | n3_chennan_CS_huangyeyiyuan_yy_ylsw_net |
---|---|
Data | 72.75M (+ 0B) |
Tables | 3 (+ 0) |
Columns | 32 (+ 0) |
Table Rows | 197,942 (+ 0) |
Media | 0B (+ 0B) |
Files | 0 (+ 0) |
Last Commit | 2023-06-19 18:36:38 (+ 169 d) |
全国34个省份的98,954家医院信息数据库。每家医院包含有医院类型,等级,所获荣誉,联系电话,地址,邮编,名称,所在地区等信息。整个全国医院信息数据库共有3个表。
From this China hospitals database, it has 98,954 hospitals from 34 provinces in China. Each hospital consists of type, hospital level, awards, telephone, address, zip code, name, location, etc. The whole China hospitals data set has 3 tables in total.
Tables | Rows | Columns | Non-empty |
---|---|---|---|
yiyuan | 98,954 | lianxi_dianhua |
93.93%
|
title |
100%
|
||
leixing |
99.94%
|
||
dengji |
99.8%
|
||
yiyuan_jieshao |
6.6%
|
||
suohuo_rongyu |
99.88%
|
||
yiyuan_wangzhi |
4.83%
|
||
yiyuan_chuanzhen |
2.73%
|
||
yiyuan_dizhi |
99.88%
|
||
yiyuan_youbian |
99.78%
|
||
yiyuan_mingcheng |
99.88%
|
||
suozai_diqu |
99.88%
|
||
yuanzhang_xingming |
17.19%
|
||
jianyuan_nianfen |
23.29%
|
||
yiyuan_leixing |
99.88%
|
||
bingchuang_shuliang |
17.95%
|
||
nianmenzhenliang |
10.61%
|
||
shifou_yibao |
11.94%
|
||
shengfen_x_yiyuan | 98,954 | shengfen_id |
100%
|
yiyuan_id |
100%
|
||
shengfen | 34 | title |
100%
|
No media sets.
Time | Data | Tables | Columns | Rows | Media | Files |
---|---|---|---|---|---|---|
2023-06-19 (+ 169 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2023-01-01 (+ 95 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2022-09-27 (+ 271 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-12-30 (+ 115 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-09-05 (+ 150 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-04-08 (+ 68 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-01-29 (+ 120 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-10-01 (+ 38 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-08-23 (+ 141 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-04-04 (+ 196 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-09-21 (+ 197 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-03-07 (+ 43 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-01-23 (+ 44 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-12-09 (+ 29 d) | 72.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-11-09 (+ 27 d) | 72.75M (+ 5M) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-10-13 (+ 29 d) | 67.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-09-13 (+ 34 d) | 67.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-08-09 (+ 27 d) | 67.75M (+ 0B) | 3 (+ 0) | 32 (+ 0) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-07-12 (+ 31 d) | 67.75M (+ 1M) | 3 (+ 0) | 32 (- 1) | 197,942 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-06-10 | 66.75M | 3 | 33 | 197,942 | 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.