07 Industrial Value Added
Interfaces implemented in this chapter are listed below; uncovered indicators are noted under “To be added” at the end.
Industrial value-added growth
Interface: macro_china_gyzjz
Source URL: https://data.eastmoney.com/cjsj/gyzjz.html
Description: East Money — China industrial value-added growth; data from 2008 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Month | object | - |
| YoY growth | float64 | Unit: % |
| Cumulative growth | float64 | Unit: % |
| Release date | object | - |
Example
import meshare as ms
macro_china_gyzjz_df = ms.macro_china_gyzjz()
print(macro_china_gyzjz_df)
Sample data
Month YoY growth Cumulative growth Release date
0 Feb 2008 15.4 15.4 2008-02-01
1 Mar 2008 17.8 16.4 2008-03-01
2 Apr 2008 15.7 16.3 2008-04-01
3 May 2008 16.0 16.3 2008-05-01
4 Jun 2008 16.0 16.3 2008-06-01
.. ... ... ... ...
165 Feb 2023 NaN 2.4 2023-02-01
166 Mar 2023 3.9 3.0 2023-03-01
167 Apr 2023 5.6 3.6 2023-04-01
168 May 2023 3.5 3.6 2023-05-01
169 Jun 2023 4.4 3.8 2023-06-01
[170 rows x 4 columns]
Industrial production YoY (above-scale)
Interface: macro_china_industrial_production_yoy
Source URL: https://datacenter.jin10.com/reportType/dc_chinese_industrial_production_yoy
Description: China above-scale industrial value-added YoY report; data from 19900301 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Item | object | - |
| Date | object | - |
| Actual | float64 | Unit: % |
| Forecast | float64 | Unit: % |
| Previous | float64 | Unit: % |
Example
import meshare as ms
macro_china_industrial_production_yoy_df = ms.macro_china_industrial_production_yoy()
print(macro_china_industrial_production_yoy_df)
Sample data
Item Date Actual Forecast Previous
0 China above-scale industrial value-added YoY report 1990-03-01 5.0 NaN NaN
1 China above-scale industrial value-added YoY report 1990-04-01 0.8 NaN 5.0
2 China above-scale industrial value-added YoY report 1990-05-01 1.7 NaN 0.8
3 China above-scale industrial value-added YoY report 1990-06-01 3.3 NaN 1.7
4 China above-scale industrial value-added YoY report 1990-07-01 5.0 NaN 3.3
.. ... ... ... ... ...
392 China above-scale industrial value-added YoY report 2023-11-15 4.6 4.4 4.5
393 China above-scale industrial value-added YoY report 2023-12-15 6.6 5.6 4.6
394 China above-scale industrial value-added YoY report 2024-01-17 6.8 6.6 6.6
395 China above-scale industrial value-added YoY report 2024-03-18 7.0 5.3 6.8
396 China above-scale industrial value-added YoY report 2024-04-16 NaN NaN 7.0
[397 rows x 5 columns]
Society-wide electricity consumption by category
Interface: macro_china_society_electricity
Source URL: http://finance.sina.com.cn/mac/#industry-6-0-31-1
Description: NBS — Society-wide electricity consumption by category
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Statistical time | object | - |
| Society-wide electricity | float64 | Unit: 10,000 kWh |
| Society-wide electricity YoY | float64 | Unit: % |
| All industries total | float64 | Unit: 10,000 kWh |
| All industries total YoY | float64 | Unit: % |
| Primary industry electricity | float64 | Unit: 10,000 kWh |
| Primary industry electricity YoY | float64 | Unit: % |
| Secondary industry electricity | float64 | Unit: 10,000 kWh |
| Secondary industry electricity YoY | float64 | Unit: % |
| Tertiary industry electricity | float64 | Unit: 10,000 kWh |
| Tertiary industry electricity YoY | float64 | Unit: % |
| Urban & rural residential total | float64 | Unit: 10,000 kWh |
| Urban & rural residential total YoY | float64 | Unit: % |
| Urban residential electricity | float64 | Unit: 10,000 kWh |
| Urban residential electricity YoY | float64 | Unit: % |
| Rural residential electricity | float64 | Unit: 10,000 kWh |
| Rural residential electricity YoY | float64 | Unit: % |
Example
import meshare as ms
macro_china_society_electricity_df = ms.macro_china_society_electricity()
print(macro_china_society_electricity_df)
Sample data
Statistical time Society-wide electricity Society-wide electricity YoY ... Urban residential electricity YoY Rural residential electricity Rural residential electricity YoY
0 2003.12 188912117.0 15.29 ... 16.12 8806708.0 5.79
1 2004.10 175828690.0 15.17 ... 8.74 7652223.0 8.99
2 2004.11 194584023.0 15.13 ... 8.95 8408040.0 10.48
3 2004.3 48045510.0 15.70 ... 9.85 2187609.0 11.68
4 2004.9 157131146.0 14.92 ... 8.58 6814158.0 9.80
.. ... ... ... ... ... ... ...
209 2023.6 430760000.0 5.00 ... NaN NaN NaN
210 2023.7 519650000.0 5.20 ... NaN NaN NaN
211 2023.8 608260000.0 5.00 ... NaN NaN NaN
212 2023.9 686370000.0 5.60 ... NaN NaN NaN
213 2024.2 153160000.0 11.00 ... NaN NaN NaN
[214 rows x 17 columns]
Daily coastal six major power plant coal inventory
Interface: macro_china_daily_energy
Source URL: https://datacenter.jin10.com/reportType/dc_qihuo_energy_report
Description: China daily coastal six major power plant coal inventory; data from 20160101 to present; no longer updated — historical data only
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Date | object | - |
| Coastal six major plants inventory | float64 | - |
| Daily consumption | float64 | - |
| Days of coal coverage | float64 | - |
Example
import meshare as ms
macro_china_daily_energy_df = ms.macro_china_daily_energy()
print(macro_china_daily_energy_df)
Sample data
Date Coastal six major plants inventory Daily consumption Days of coal coverage
0 2016-01-01 1167.60 64.20 18.19
1 2016-01-02 1162.90 63.40 18.34
2 2016-01-03 1160.80 62.60 18.54
3 2016-01-04 1185.30 57.60 20.58
4 2016-01-05 1150.20 57.20 20.11
... ... ... ...
1202 2019-05-17 1639.47 61.71 26.56
1203 2019-05-21 1591.92 62.67 25.40
1204 2019-05-22 1578.63 59.54 26.51
1205 2019-05-24 1671.83 60.65 27.56
1206 2019-06-21 1786.64 66.57 26.84
[1207 rows x 4 columns]
Energy index
Interface: macro_china_energy_index
Source URL: https://data.eastmoney.com/cjsj/hyzs_list_EMI00662539.html
Description: Energy index; data from 20111205 to present
Limits: Returns all historical data in a single call
Input
| Name | Type | Description |
|---|---|---|
| - | - | - |
Output
| Name | Type | Description |
|---|---|---|
| Date | object | - |
| Latest | int64 | - |
| Change % | float64 | Unit: % |
| 3M change % | float64 | Unit: % |
| 6M change % | float64 | Unit: % |
| 1Y change % | float64 | Unit: % |
| 2Y change % | float64 | Unit: % |
| 3Y change % | float64 | Unit: % |
Example
import meshare as ms
macro_china_energy_index_df = ms.macro_china_energy_index()
print(macro_china_energy_index_df)
Sample data
Date Latest Change % ... 1Y change % 2Y change % 3Y change %
0 2011-12-05 1003 NaN ... NaN NaN NaN
1 2011-12-12 995 -0.797607 ... NaN NaN NaN
2 2011-12-19 987 -0.804020 ... NaN NaN NaN
3 2011-12-26 983 -0.405268 ... NaN NaN NaN
4 2012-01-02 984 0.101729 ... NaN NaN NaN
... ... ... ... ... ... ...
2972 2022-03-29 1208 -0.247729 ... 48.220859 84.992343 48.768473
2973 2022-03-30 1206 -0.165563 ... 47.252747 88.437500 48.522167
2974 2022-03-31 1207 0.082919 ... 47.735618 90.378549 48.645320
2975 2022-04-01 1207 0.000000 ... 48.098160 91.283677 48.828607
2976 2022-04-02 1208 0.082850 ... 47.858017 91.442155 47.858017