## 09 Price Analysis > Interfaces implemented in this chapter are listed below; uncovered indicators are noted under “To be added” at the end. #### Consumer Price Index (CPI) Interface: macro_china_cpi Source URL: http://data.eastmoney.com/cjsj/cpi.html Description: China Consumer Price Index; monthly data from 200801 to present Limits: Returns all historical data in a single call Input | Name | Type | Description | |-----|-----|-----| | - | - | - | Output | Name | Type | Description | |---------|---------|---------| | Month | object | - | | Nationwide-Current month | float64 | - | | Nationwide-YoY | float64 | Unit: % | | Nationwide-MoM | float64 | Unit: % | | Nationwide-Cumulative | float64 | - | | Urban-Current month | float64 | - | | Urban-YoY | float64 | Unit: % | | Urban-MoM | float64 | Unit: % | | Urban-Cumulative | float64 | - | | Rural-Current month | float64 | - | | Rural-YoY | float64 | Unit: % | | Rural-MoM | float64 | Unit: % | | Rural-Cumulative | float64 | - | Example ```python import meshare as ms macro_china_cpi_df = ms.macro_china_cpi() print(macro_china_cpi_df) ``` Sample data ``` Month Nationwide-Current month Nationwide-YoY ... Rural-YoY Rural-MoM Rural-Cumulative 0 Oct 2022 102.1000 2.1000 ... 2.5000 0.1 102.0000 1 Sep 2022 102.8000 2.8000 ... 3.1000 0.4 102.0000 2 Aug 2022 102.5000 2.5000 ... 2.7000 -0.1 101.8000 3 Jul 2022 102.7000 2.7000 ... 3.0000 0.5 101.7000 4 Jun 2022 102.5000 2.5000 ... 2.6000 0.0 101.5000 .. ... ... ... ... ... ... ... 173 May 2008 107.7163 7.7163 ... 8.5481 -0.3 108.7612 174 Apr 2008 108.4829 8.4829 ... 9.2737 0.1 108.8147 175 Mar 2008 108.3097 8.3097 ... 9.0330 -0.5 108.6618 176 Feb 2008 108.7443 8.7443 ... 9.2344 2.4 108.4812 177 Jan 2008 107.0781 7.0781 ... 7.7209 1.2 107.7209 ``` #### China CPI YoY report Interface: macro_china_cpi_yearly Source URL: https://datacenter.jin10.com/reportType/dc_chinese_cpi_yoy Description: China annual CPI data; from 19860201 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_cpi_yearly_df = ms.macro_china_cpi_yearly() print(macro_china_cpi_yearly_df) ``` Sample data ``` Item Date Actual Forecast Previous 0 China CPI YoY report 1986-02-01 7.1 NaN NaN 1 China CPI YoY report 1986-03-01 7.1 NaN 7.1 2 China CPI YoY report 1986-04-01 7.1 NaN 7.1 3 China CPI YoY report 1986-05-01 7.1 NaN 7.1 4 China CPI YoY report 1986-06-01 7.1 NaN 7.1 .. ... ... ... ... ... 454 China CPI YoY report 2023-12-09 -0.5 -0.1 -0.2 455 China CPI YoY report 2024-01-12 -0.3 -0.4 -0.5 456 China CPI YoY report 2024-02-08 -0.8 -0.5 -0.3 457 China CPI YoY report 2024-03-09 0.7 0.3 -0.8 458 China CPI YoY report 2024-04-11 NaN NaN 0.7 [459 rows x 5 columns] ``` #### China CPI MoM report Interface: macro_china_cpi_monthly Source URL: https://datacenter.jin10.com/reportType/dc_chinese_cpi_mom Description: China monthly CPI data; from 19960201 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_cpi_monthly_df = ms.macro_china_cpi_monthly() print(macro_china_cpi_monthly_df) ``` Sample data ``` Item Date Actual Forecast Previous 0 China CPI MoM report 1996-02-01 2.1 NaN NaN 1 China CPI MoM report 1996-03-01 2.3 NaN 2.1 2 China CPI MoM report 1996-04-01 0.6 NaN 2.3 3 China CPI MoM report 1996-05-01 0.7 NaN 0.6 4 China CPI MoM report 1996-06-01 -0.5 NaN 0.7 .. ... ... ... ... ... 334 China CPI MoM report 2023-12-09 -0.5 -0.1 -0.1 335 China CPI MoM report 2024-01-12 0.1 0.2 -0.5 336 China CPI MoM report 2024-02-08 0.3 0.4 0.1 337 China CPI MoM report 2024-03-09 1.0 0.7 0.3 338 China CPI MoM report 2024-04-11 NaN NaN 1.0 [339 rows x 5 columns] ``` #### Producer Price Index (PPI) Interface: macro_china_ppi Source URL: http://data.eastmoney.com/cjsj/ppi.html Description: Producer Price Index for industrial products; monthly data from 200601 to present Limits: Returns all historical data in a single call Input | Name | Type | Description | |-----|-----|-----| | - | - | - | Output | Name | Type | Description | |--------|---------|---------| | Month | object | - | | Current month | float64 | - | | Current month YoY | float64 | Unit: % | | Cumulative | float64 | - | Example ```python import meshare as ms macro_china_ppi_df = ms.macro_china_ppi() print(macro_china_ppi_df) ``` Sample data ``` Month Current month Current month YoY Cumulative 0 Oct 2022 98.700 -1.30 105.2000 1 Sep 2022 100.948 0.90 105.9397 2 Aug 2022 102.300 2.30 106.6000 3 Jul 2022 104.200 4.20 107.2000 4 Jun 2022 106.100 6.10 107.7000 .. ... ... ... ... 197 May 2006 102.430 2.43 102.5700 198 Apr 2006 101.870 1.87 102.6000 199 Mar 2006 102.490 2.49 102.9000 200 Feb 2006 103.010 3.01 103.0000 201 Jan 2006 103.050 3.05 103.0500 ``` #### China PPI YoY report Interface: macro_china_ppi_yearly Source URL: https://datacenter.jin10.com/reportType/dc_chinese_ppi_yoy Description: China annual PPI data; from 19950801 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_ppi_yearly_df = ms.macro_china_ppi_yearly() print(macro_china_ppi_yearly_df) ``` Sample data ``` Item Date Actual Forecast Previous 0 China PPI YoY report 1995-08-01 13.5 NaN NaN 1 China PPI YoY report 1995-09-01 13.0 NaN 13.5 2 China PPI YoY report 1995-10-01 12.9 NaN 13.0 3 China PPI YoY report 1995-11-01 12.5 NaN 12.9 4 China PPI YoY report 1995-12-01 11.1 NaN 12.5 .. ... ... ... ... ... 340 China PPI YoY report 2023-12-09 -3.0 -2.8 -2.6 341 China PPI YoY report 2024-01-12 -2.7 -2.6 -3.0 342 China PPI YoY report 2024-02-08 -2.5 -2.6 -2.7 343 China PPI YoY report 2024-03-09 -2.7 -2.5 -2.5 344 China PPI YoY report 2024-04-11 NaN NaN -2.7 [345 rows x 5 columns] ``` #### Corporate Goods Price Index (CGPI) Interface: macro_china_qyspjg Source URL: http://data.eastmoney.com/cjsj/qyspjg.html Description: East Money — economic data overview — China — Corporate Goods Price Index; from 20050101 to present Limits: Returns all historical data in a single call Input | Name | Type | Description | |-----|-----|-----| | - | - | - | Output | Name | Type | Description | |----------|---------|---------| | Month | object | - | | Overall index-Value | float64 | - | | Overall index-YoY | float64 | Unit: % | | Overall index-MoM | float64 | Unit: % | | Agricultural products-Value | float64 | - | | Agricultural products-YoY | float64 | Unit: % | | Agricultural products-MoM | float64 | Unit: % | | Mineral products-Value | float64 | - | | Mineral products-YoY | float64 | Unit: % | | Mineral products-MoM | float64 | Unit: % | | Coal/oil/electricity-Value | float64 | - | | Coal/oil/electricity-YoY | float64 | Unit: % | | Coal/oil/electricity-MoM | float64 | Unit: % | Example ```python import meshare as ms macro_china_qyspjg_df = ms.macro_china_qyspjg() print(macro_china_qyspjg_df) ``` Sample data ``` Month Overall index-Value Overall index-YoY ... Coal/oil/electricity-Value Coal/oil/electricity-YoY Coal/oil/electricity-MoM 0 Sep 2022 101.70 -5.746061 ... 111.80 -4.931973 -2.272727 1 Aug 2022 102.30 -4.925651 ... 114.40 -0.608167 -1.970865 2 Jul 2022 103.10 -4.448563 ... 116.70 1.214224 -3.233831 3 Jun 2022 105.20 -2.682701 ... 120.60 4.960836 0.249377 4 May 2022 105.10 -3.666361 ... 120.30 4.427083 -1.635323 .. ... ... ... ... ... ... ... 208 May 2005 103.16 -5.738304 ... 122.53 11.168572 1.625612 209 Apr 2005 102.81 -5.903350 ... 120.57 12.640135 0.920733 210 Mar 2005 103.48 -4.485878 ... 119.47 14.292548 0.538585 211 Feb 2005 104.82 -2.046538 ... 118.83 13.974679 2.590003 212 Jan 2005 104.67 -1.957662 ... 115.83 8.903723 -1.814021 ``` #### Vegetable basket wholesale price index Interface: macro_china_vegetable_basket Source URL: https://data.eastmoney.com/cjsj/hyzs_list_EMI00009275.html Description: Vegetable basket product wholesale price index; from 20050927 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_vegetable_basket_df = ms.macro_china_vegetable_basket() print(macro_china_vegetable_basket_df) ``` Sample data ``` Date Latest Change % ... 1Y change % 2Y change % 3Y change % 0 2005-09-27 123.80 NaN ... NaN NaN NaN 1 2005-09-28 123.00 -0.646204 ... NaN NaN NaN 2 2005-09-29 123.10 0.081301 ... NaN NaN NaN 3 2005-09-30 124.10 0.812348 ... NaN NaN NaN 4 2005-10-08 122.20 -1.531023 ... NaN NaN NaN ... ... ... ... ... ... ... 4100 2022-03-28 137.93 0.561388 ... 6.026597 6.608440 15.470908 4101 2022-03-29 138.45 0.377003 ... 6.952491 7.010357 15.712495 4102 2022-03-30 138.85 0.288913 ... 7.410846 7.978847 16.046803 4103 2022-03-31 139.38 0.381707 ... 8.180689 8.289954 16.489762 4104 2022-04-01 139.70 0.229588 ... 8.775208 8.622969 16.484616 ``` #### Agricultural product wholesale price index Interface: macro_china_agricultural_product Source URL: https://data.eastmoney.com/cjsj/hyzs_list_EMI00009274.html Description: Overall agricultural product wholesale price index; from 20050927 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_agricultural_product_df = ms.macro_china_agricultural_product() print(macro_china_agricultural_product_df) ``` Sample data ``` Date Latest Change % ... 1Y change % 2Y change % 3Y change % 0 2005-09-27 125.50 NaN ... NaN NaN NaN 1 2005-09-28 125.00 -0.398406 ... NaN NaN NaN 2 2005-09-29 125.00 0.000000 ... NaN NaN NaN 3 2005-09-30 125.80 0.640000 ... NaN NaN NaN 4 2005-10-08 124.20 -1.271860 ... NaN NaN NaN ... ... ... ... ... ... ... 4100 2022-03-28 134.83 0.499404 ... 6.349582 7.374373 15.259019 4101 2022-03-29 135.30 0.348587 ... 7.100451 7.748666 15.492958 4102 2022-03-30 135.03 -0.199557 ... 7.098668 8.127803 15.262484 4103 2022-03-31 136.10 0.792416 ... 8.265054 8.862582 16.175843 4104 2022-04-01 136.38 0.205731 ... 8.790683 9.156395 16.176846 ``` #### Agricultural and sideline products index Interface: macro_china_agricultural_index Source URL: https://data.eastmoney.com/cjsj/hyzs_list_EMI00662543.html Description: Agricultural and sideline products index; 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_agricultural_index_df = ms.macro_china_agricultural_index() print(macro_china_agricultural_index_df) ``` Sample data ``` Date Latest Change % ... 1Y change % 2Y change % 3Y change % 0 2011-12-05 995 NaN ... NaN NaN NaN 1 2011-12-12 986 -0.904523 ... NaN NaN NaN 2 2011-12-19 990 0.405680 ... NaN NaN NaN 3 2011-12-26 988 -0.202020 ... NaN NaN NaN 4 2012-01-02 993 0.506073 ... NaN NaN NaN ... ... ... ... ... ... ... 2971 2022-03-28 1316 -0.679245 ... 7.428571 24.385633 55.739645 2972 2022-03-29 1311 -0.379939 ... 7.635468 23.913043 55.331754 2973 2022-03-30 1305 -0.457666 ... 7.495881 22.881356 54.620853 2974 2022-03-31 1303 -0.153257 ... 8.222591 22.347418 54.383886 2975 2022-04-01 1289 -1.074444 ... 6.090535 21.489161 52.906287 ``` #### Retail Price Index (RPI) Interface: macro_china_retail_price_index Source URL: http://finance.sina.com.cn/mac/#price-12-0-31-1 Description: NBS — Retail Price Index Limits: Returns all historical data in a single call Input | Name | Type | Description | |----|----|----| | - | - | - | Output | Name | Type | Description | |----------|---------|----| | Statistical month | object | Year-month | | Consumer item | object | - | | Retail price index | float64 | - | Example ```python import meshare as ms macro_china_retail_price_index_df = ms.macro_china_retail_price_index() print(macro_china_retail_price_index_df) ``` Sample data ``` Statistical month Consumer item Retail price index 0 2020.8 Building materials and hardware/electrical 100.10 1 2020.8 Fuels 90.70 2 2020.8 Books, newspapers, magazines and electronic publications 101.70 3 2020.8 Chinese/Western medicines and healthcare products 100.30 4 2020.8 Gold, silver and jewelry 122.90 ... ... ... 3777 2002.1 Electromechanical products 94.10 3778 2002.1 Household appliances and AV equipment 94.20 3779 2002.1 Sports and entertainment products 98.80 3780 2002.1 Chinese/Western medicines and healthcare products 97.30 3781 2002.1 Books, newspapers, magazines and electronic publications 101.00 ``` #### Commodity price index Interface: macro_china_commodity_price_index Source URL: https://data.eastmoney.com/cjsj/hyzs_list_EMI00662535.html Description: Commodity price 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_commodity_price_index_df = ms.macro_china_commodity_price_index() print(macro_china_commodity_price_index_df) ``` Sample data ``` Date Latest Change % ... 1Y change % 2Y change % 3Y change % 0 2011-12-05 999 NaN ... NaN NaN NaN 1 2011-12-12 991 -0.800801 ... NaN NaN NaN 2 2011-12-19 990 -0.100908 ... NaN NaN NaN 3 2011-12-26 988 -0.202020 ... NaN NaN NaN 4 2012-01-02 992 0.404858 ... NaN NaN NaN ... ... ... ... ... ... ... 2975 2022-03-31 1213 0.000000 ... 21.543086 68.005540 40.556199 2976 2022-04-01 1212 -0.082440 ... 20.958084 69.037657 40.277778 2977 2022-04-02 1212 0.000000 ... 20.717131 70.224719 40.440324 2978 2022-04-03 1212 0.000000 ... 20.837488 70.464135 40.440324 2979 2022-04-04 1212 0.000000 ... 20.837488 70.704225 40.440324 ```