## 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```