Name | n3_lyz_data.stats.gov.cn_year_area |
---|---|
Data | 245.03M (+ 0B) |
Tables | 10 (+ 0) |
Columns | 73 (+ 0) |
Table Rows | 1,956,540 (+ 0) |
Media | 0B (+ 0B) |
Files | 0 (+ 0) |
Last Commit | 2023-06-19 18:12:05 (+ 169 d) |
这是一个中国各地区年度宏观统计数据库。该数据库包含来自中国31个地区的88,598条年度统计数据及1,769,040个年度统计数据时间记录,这些数据按照区域划分,生产,人口及居民生活等方向以及产业分别被分为13个,312个和28个分类。每条年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。
该中国各地区年度宏观统计数据库共有10个表。
There are 88,598 macroscopic statistics data by year records and 1,769,040 time records from 31 regions in China. Each record is comprised of name, category and description. These yearly macroscopic statistics data records are categorized into 13 types,312 categories and 28 classifications by regionalism, production, population resident life, etc. and industry in each. The whole China macroscopic statistics data by year data set has 10 tables.
Tables | Rows | Columns | Non-empty |
---|---|---|---|
category_2_year | 312 | name |
100%
|
category_year_id |
100%
|
||
category_2_year_x_region | 9,579 | category_2_year_id |
100%
|
region_id |
100%
|
||
category_3_year | 13 | name |
100%
|
category_2_year_id |
100%
|
||
category_3_year_x_region | 403 | category_3_year_id |
100%
|
region_id |
100%
|
||
category_year | 28 | name |
100%
|
data_year_indicator | 88,598 | name |
100%
|
description |
30.23%
|
||
category_2_year_x_region_id |
100%
|
||
data_year_indicator3 | 4,216 | name |
100%
|
description |
66.91%
|
||
category_3_year_x_region_id |
100%
|
||
data_year_indicator_time | 1,769,040 | time |
100%
|
value |
61.69%
|
||
data_year_indicator_id |
100%
|
||
data_year_indicator_time3 | 84,320 | time |
100%
|
value |
54.44%
|
||
data_year_indicator3_id |
100%
|
||
region | 31 | region |
100%
|
No media sets.
Time | Data | Tables | Columns | Rows | Media | Files |
---|---|---|---|---|---|---|
2023-06-19 (+ 169 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2023-01-01 (+ 95 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2022-09-27 (+ 271 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-12-30 (+ 115 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-09-05 (+ 150 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-04-08 (+ 68 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2021-01-29 (+ 120 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-10-01 (+ 38 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-08-23 (+ 141 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2020-04-04 (+ 196 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-09-21 (+ 197 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-03-07 (+ 43 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2019-01-23 (+ 44 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-12-09 (+ 29 d) | 245.03M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-11-09 (+ 27 d) | 245.03M (+ 134.06M) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-10-13 (+ 29 d) | 110.97M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-09-13 (+ 34 d) | 110.97M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-08-09 (+ 27 d) | 110.97M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-07-12 (+ 31 d) | 110.97M (+ 0B) | 10 (+ 0) | 73 (+ 0) | 1,956,540 (+ 0) | 0B (+ 0B) | 0 (+ 0) |
2018-06-10 | 110.97M | 10 | 73 | 1,956,540 | 0B | 0 |
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