| Name | n3_lyz_data.stats.gov.cn_mon_area |
|---|---|
| Data | 55.44M (+ 0B) |
| Tables | 10 (+ 0) |
| Columns | 73 (+ 0) |
| Table Rows | 398,243 (+ 0) |
| Media | 0B (+ 0B) |
| Files | 0 (+ 0) |
| Last Commit | 2023-06-19 18:12:05 (+ 169 d) |
这是一个中国月度宏观统计数据库。该数据库包含来自中国31个地区的8,850条月度宏观统计数据及318,600个月度统计数据时间记录，这些数据按照居民生活、生产所需消费指数被分为6大主分类以及主分类下的16个次级分类和91个三级分类。每条月度统计数据中包含有名称，分类和描述。
31个地区：北京市，天津市，河北省，山西省，内蒙古自治区，辽宁省，吉林省，黑龙江省，上海市，江苏省，浙江省，安徽省，福建省，江西省，山东省，河南省，湖北省，湖南省，广东省，广西壮族自治区，海南省，重庆市，四川省，贵州省，云南省，西藏自治区，陕西省，甘肃省，青海省，宁夏回族自治区，新疆维吾尔自治区。
该中国各地区月度宏观统计数据库共有10个表。
This is a comprehensive database of monthly macroscopic statistics data with 8,850 statistics data records and 318,600 times records from 31 regions in China. Each datum record is comprised of name, category and description. These monthly macroscopic statistics data records are categorized into 6 types and further into 16 categories and 91 classifications. The whole China macroscopic statistics data by month database has 10 tables.
| Tables | Rows | Columns | Non-empty |
|---|---|---|---|
| category_2_mon_indicator | 16 | name |
100%
|
| category_mon_indicator_id |
100%
|
||
| category_2_mon_indicator_x_region | 155 | category_2_mon_indicator_id |
100%
|
| region_id |
100%
|
||
| category_3_mon_indicator | 91 | name |
100%
|
| category_2_mon_indicator_id |
100%
|
||
| category_3_mon_indicator_x_region | 2,821 | category_3_mon_indicator_id |
100%
|
| region_id |
100%
|
||
| category_mon_indicator | 6 | name |
100%
|
| data_month_indicator | 8,850 | name |
100%
|
| category_3_mon_indicator_x_region_id |
100%
|
||
| description |
59.46%
|
||
| data_month_indicator_2 | 1,829 | name |
100%
|
| category_2_mon_indicator_x_region_id |
100%
|
||
| description |
22.03%
|
||
| data_month_indicator_time | 318,600 | time |
100%
|
| value |
69.76%
|
||
| data_month_indicator_id |
100%
|
||
| data_month_indicator_time_2 | 65,844 | time |
100%
|
| value |
82.92%
|
||
| data_month_indicator_2_id |
100%
|
||
| region | 31 | region |
100%
|
No media sets.
| Time | Data | Tables | Columns | Rows | Media | Files |
|---|---|---|---|---|---|---|
| 2023-06-19 (+ 169 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2023-01-01 (+ 95 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2022-09-27 (+ 271 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2021-12-30 (+ 115 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2021-09-05 (+ 150 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2021-04-08 (+ 68 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2021-01-29 (+ 120 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2020-10-01 (+ 38 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2020-08-23 (+ 141 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2020-04-04 (+ 196 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2019-09-21 (+ 197 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2019-03-07 (+ 43 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2019-01-23 (+ 44 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2018-12-09 (+ 29 d) | 55.44M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2018-11-09 (+ 27 d) | 55.44M (- 0.22M) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2018-10-13 (+ 29 d) | 55.66M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2018-09-13 (+ 34 d) | 55.66M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2018-08-09 (+ 27 d) | 55.66M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2018-07-12 (+ 31 d) | 55.66M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 398,243 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
| 2018-06-10 | 55.66M | 10 | 73 | 398,243 | 0B | 0 |
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