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# Economic Indicators
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AKShare provides comprehensive macroeconomic data with 226 functions covering economic indicators from China, United States, European Union, Japan, Australia, Canada, and the United Kingdom. This is the largest category in AKShare, reflecting the importance of macroeconomic analysis in financial markets.
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## Chinese Economic Data (77+ functions)
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### Core Economic Indicators
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#### Consumer Price Index (CPI)
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```python { .api }
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import akshare as ak
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def macro_china_cpi() -> pd.DataFrame:
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    """
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    Chinese Consumer Price Index data from East Money
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    Returns:
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        pd.DataFrame: CPI data with columns:
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            - 时间 (Time): Date
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            - 全国-同比 (National YoY): National CPI year-over-year
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            - 全国-环比 (National MoM): National CPI month-over-month  
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            - 城市-同比 (Urban YoY): Urban CPI year-over-year
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            - 农村-同比 (Rural YoY): Rural CPI year-over-year
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    """
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# Get national CPI data
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cpi_df = ak.macro_china_cpi()
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print(cpi_df.head())
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#         时间     全国-同比    全国-环比    城市-同比    农村-同比
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# 0  2024-11-01    102.3      100.2      102.1      102.8
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# 1  2024-10-01    102.0      100.4      101.8      102.5
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```
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#### Gross Domestic Product (GDP)
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```python { .api }
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import akshare as ak
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def macro_china_gdp() -> pd.DataFrame:
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    """
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    Chinese GDP quarterly data
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    Returns:
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        pd.DataFrame: GDP data with columns:
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            - 季度 (Quarter): Quarter period
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            - 国内生产总值-绝对值 (GDP Absolute): Absolute GDP value
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            - 国内生产总值-同比增长 (GDP YoY): Year-over-year growth
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            - 第一产业-绝对值 (Primary Industry): Primary sector GDP
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            - 第二产业-绝对值 (Secondary Industry): Secondary sector GDP  
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            - 第三产业-绝对值 (Tertiary Industry): Tertiary sector GDP
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    """
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# Get GDP data
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gdp_df = ak.macro_china_gdp()
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print(gdp_df.head())
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#      季度  国内生产总值-绝对值  国内生产总值-同比增长  第一产业-绝对值  第二产业-绝对值  第三产业-绝对值
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# 0  2024Q3         296298.4          4.6       80480.5      117647.2      98170.7
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```
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#### Purchasing Managers Index (PMI)
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```python { .api }
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import akshare as ak
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def macro_china_pmi() -> pd.DataFrame:
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    """
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    Chinese PMI (Manufacturing and Services)
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    Returns:
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        pd.DataFrame: PMI data with columns:
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            - 日期 (Date): Date
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            - 制造业PMI (Manufacturing PMI): Manufacturing PMI value
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            - 服务业PMI (Services PMI): Services PMI value
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            - 综合PMI (Composite PMI): Composite PMI value
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    """
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# Get PMI data
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pmi_df = ak.macro_china_pmi()
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print(pmi_df.head())
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#         日期  制造业PMI  服务业PMI  综合PMI
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# 0  2024-11-01     50.2     51.4    51.8
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# 1  2024-10-01     49.8     50.2    50.5
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```
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#### Money Supply
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```python { .api }
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import akshare as ak
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def macro_china_money_supply() -> pd.DataFrame:
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    """
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    Chinese money supply data (M0, M1, M2)
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    Returns:
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        pd.DataFrame: Money supply data with columns:
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            - 月份 (Month): Month
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            - 货币和准货币(M2) (M2): Broad money supply
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            - 货币(M1) (M1): Narrow money supply  
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            - 流通中现金(M0) (M0): Currency in circulation
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            - M2同比增长 (M2 YoY): M2 year-over-year growth
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            - M1同比增长 (M1 YoY): M1 year-over-year growth
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            - M0同比增长 (M0 YoY): M0 year-over-year growth
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    """
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# Get money supply data
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money_df = ak.macro_china_money_supply()
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```
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#### Trade Statistics  
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```python { .api }
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import akshare as ak
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def macro_china_trade() -> pd.DataFrame:
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    """
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    Chinese international trade data
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    Returns:
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        pd.DataFrame: Trade statistics with columns:
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            - 月份 (Month): Month
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            - 出口金额 (Exports): Export value in USD
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            - 进口金额 (Imports): Import value in USD
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            - 贸易差额 (Trade Balance): Trade balance
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            - 出口同比 (Export YoY): Export year-over-year growth
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            - 进口同比 (Import YoY): Import year-over-year growth
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    """
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# Get trade data
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trade_df = ak.macro_china_trade()
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```
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### Industrial and Production Indicators
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#### Industrial Production
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```python { .api }
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import akshare as ak
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def macro_china_industrial_production() -> pd.DataFrame:
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    """
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    Chinese industrial production index
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    Returns:
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        pd.DataFrame: Industrial production data
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    """
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# Get industrial production data
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industrial_df = ak.macro_china_industrial_production()
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```
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#### Fixed Asset Investment
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```python { .api }
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import akshare as ak
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def macro_china_fixed_asset_investment() -> pd.DataFrame:
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    """
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    Chinese fixed asset investment data
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    Returns:
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        pd.DataFrame: Investment statistics by sector
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    """
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# Get investment data
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investment_df = ak.macro_china_fixed_asset_investment()
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```
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### Financial and Monetary Indicators
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#### Interest Rates
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```python { .api }
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import akshare as ak
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def macro_china_interest_rate() -> pd.DataFrame:
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    """
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    Chinese central bank interest rates
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    Returns:
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        pd.DataFrame: Interest rate data including:
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            - 存款基准利率 (Deposit Rate)
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            - 贷款基准利率 (Loan Rate)
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            - 公开市场操作利率 (OMO Rate)
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    """
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# Get interest rate data
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rate_df = ak.macro_china_interest_rate()
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```
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#### Reserve Requirement Ratio
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```python { .api }
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import akshare as ak
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def macro_china_rrr() -> pd.DataFrame:
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    """
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    Chinese bank reserve requirement ratio
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    Returns:
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        pd.DataFrame: RRR adjustment history
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    """
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# Get RRR data
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rrr_df = ak.macro_china_rrr()
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```
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## US Economic Data (49+ functions)
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### Employment Indicators
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#### Non-farm Payrolls
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```python { .api }
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import akshare as ak
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def macro_usa_non_farm() -> pd.DataFrame:
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    """
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    US Non-farm Payroll employment data
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    Returns:
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        pd.DataFrame: Employment data with columns:
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            - 日期 (Date): Release date
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            - 非农就业人口 (Non-farm Employment): Employment change
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            - 失业率 (Unemployment Rate): Unemployment rate
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    """
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# Get non-farm payroll data
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nonfarm_df = ak.macro_usa_non_farm()
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print(nonfarm_df.head())
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#         日期  非农就业人口   失业率
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# 0  2024-11-01     12000    4.1
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# 1  2024-10-04      254000   4.0
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```
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#### Unemployment Rate
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```python { .api }
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import akshare as ak
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def macro_usa_unemployment_rate() -> pd.DataFrame:
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    """
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    US unemployment rate statistics
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    Returns:
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        pd.DataFrame: Unemployment data by demographics
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    """
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# Get detailed unemployment data
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unemployment_df = ak.macro_usa_unemployment_rate()
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```
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### Economic Growth
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#### GDP Data
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```python { .api }
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import akshare as ak
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def macro_usa_gdp() -> pd.DataFrame:
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    """
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    US GDP quarterly data
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    Returns:
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        pd.DataFrame: GDP data with columns:
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            - 日期 (Date): Quarter
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            - GDP环比折年率 (GDP QoQ Annualized): GDP growth rate
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            - GDP同比 (GDP YoY): GDP year-over-year
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    """
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# Get US GDP data
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us_gdp_df = ak.macro_usa_gdp()
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```
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### Inflation Indicators
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#### Consumer Price Index
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```python { .api }
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import akshare as ak
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def macro_usa_cpi() -> pd.DataFrame:
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    """
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    US Consumer Price Index
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    Returns:
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        pd.DataFrame: CPI data with core and headline measures
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    """
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# Get US CPI data
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us_cpi_df = ak.macro_usa_cpi()
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```
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#### Producer Price Index
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```python { .api }
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import akshare as ak
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def macro_usa_ppi() -> pd.DataFrame:
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    """
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    US Producer Price Index
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    Returns:
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        pd.DataFrame: PPI data for producer inflation
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    """
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# Get US PPI data  
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us_ppi_df = ak.macro_usa_ppi()
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```
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### Manufacturing and Business
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#### ISM Manufacturing PMI
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```python { .api }
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import akshare as ak
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def macro_usa_ism_pmi() -> pd.DataFrame:
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    """
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    US ISM Manufacturing PMI
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    Returns:
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        pd.DataFrame: Manufacturing PMI data
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    """
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# Get US manufacturing PMI
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us_pmi_df = ak.macro_usa_ism_pmi()
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```
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#### Industrial Production
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```python { .api }
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import akshare as ak
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def macro_usa_industrial_production() -> pd.DataFrame:
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    """
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    US industrial production index
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    Returns:
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        pd.DataFrame: Industrial production data
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    """
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# Get US industrial production
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us_industrial_df = ak.macro_usa_industrial_production()
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```
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## European Economic Data (16+ functions)
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### Eurozone Indicators
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#### European Central Bank Policy
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```python { .api }
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import akshare as ak
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def macro_euro_interest_rate() -> pd.DataFrame:
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    """
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    ECB interest rate decisions
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    Returns:
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        pd.DataFrame: ECB policy rate history
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    """
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# Get ECB rates
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ecb_rates = ak.macro_euro_interest_rate()
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```
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#### Eurozone CPI
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```python { .api }
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import akshare as ak
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def macro_euro_cpi() -> pd.DataFrame:
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    """
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    Eurozone Consumer Price Index
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    Returns:
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        pd.DataFrame: Eurozone inflation data
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    """
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# Get Eurozone CPI
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euro_cpi = ak.macro_euro_cpi()
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```
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#### Eurozone GDP
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```python { .api }
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import akshare as ak
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def macro_euro_gdp() -> pd.DataFrame:
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    """
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    Eurozone GDP quarterly data
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    Returns:
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        pd.DataFrame: Eurozone economic growth
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    """
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# Get Eurozone GDP
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euro_gdp = ak.macro_euro_gdp()
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```
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## Other Major Economies
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### Japan Economic Data
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```python { .api }
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import akshare as ak
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def macro_japan_cpi() -> pd.DataFrame:
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    """Japanese Consumer Price Index"""
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def macro_japan_gdp() -> pd.DataFrame:
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    """Japanese GDP data"""
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def macro_japan_interest_rate() -> pd.DataFrame:
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    """Bank of Japan policy rates"""
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# Get Japanese economic indicators
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japan_cpi = ak.macro_japan_cpi()
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japan_gdp = ak.macro_japan_gdp()
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japan_rates = ak.macro_japan_interest_rate()
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```
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### Australia Economic Data
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```python { .api }
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import akshare as ak
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def macro_australia_cpi() -> pd.DataFrame:
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    """Australian Consumer Price Index"""
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def macro_australia_gdp() -> pd.DataFrame:
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    """Australian GDP data"""
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def macro_australia_employment() -> pd.DataFrame:
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    """Australian employment statistics"""
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# Get Australian economic data
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aus_cpi = ak.macro_australia_cpi()
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aus_gdp = ak.macro_australia_gdp()
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aus_employment = ak.macro_australia_employment()
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```
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### Canada Economic Data
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```python { .api }
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import akshare as ak
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def macro_canada_cpi() -> pd.DataFrame:
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    """Canadian Consumer Price Index"""
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def macro_canada_gdp() -> pd.DataFrame:
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    """Canadian GDP data"""
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def macro_canada_employment() -> pd.DataFrame:
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    """Canadian employment data"""
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# Get Canadian economic indicators
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canada_cpi = ak.macro_canada_cpi()
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canada_gdp = ak.macro_canada_gdp()
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canada_employment = ak.macro_canada_employment()
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```
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### United Kingdom Economic Data
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```python { .api }
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import akshare as ak
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def macro_uk_cpi() -> pd.DataFrame:
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    """UK Consumer Price Index"""
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def macro_uk_gdp() -> pd.DataFrame:
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    """UK GDP quarterly data"""
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def macro_uk_employment() -> pd.DataFrame:
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    """UK employment statistics"""
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# Get UK economic data
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uk_cpi = ak.macro_uk_cpi()
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uk_gdp = ak.macro_uk_gdp()
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uk_employment = ak.macro_uk_employment()
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```
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## Economic Calendar and Events
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### Economic Event Calendar
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```python { .api }
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import akshare as ak
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def macro_economic_calendar() -> pd.DataFrame:
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    """
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    Economic event calendar with scheduled releases
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    Returns:
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        pd.DataFrame: Upcoming economic events and indicators
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    """
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# Get economic calendar
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calendar_df = ak.macro_economic_calendar()
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```
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## Data Integration Patterns
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### Cross-Country Analysis
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```python { .api }
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import akshare as ak
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import pandas as pd
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def get_global_cpi_comparison() -> pd.DataFrame:
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    """Compare CPI across major economies"""
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    # Get CPI data from major economies
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    china_cpi = ak.macro_china_cpi()
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    us_cpi = ak.macro_usa_cpi()
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    euro_cpi = ak.macro_euro_cpi()
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    japan_cpi = ak.macro_japan_cpi()
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    # Process and combine data for comparison
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    # (Implementation would involve data alignment and merging)
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    return combined_cpi_df
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def get_global_gdp_comparison() -> pd.DataFrame:
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    """Compare GDP growth across major economies"""
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    china_gdp = ak.macro_china_gdp()
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    us_gdp = ak.macro_usa_gdp()
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    euro_gdp = ak.macro_euro_gdp()
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    japan_gdp = ak.macro_japan_gdp()
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    return combined_gdp_df
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# Usage
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global_cpi = get_global_cpi_comparison()
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global_gdp = get_global_gdp_comparison()
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```
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### Economic Indicator Dashboard
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```python { .api }
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import akshare as ak
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def create_economic_dashboard(country: str = "china") -> dict:
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    """Create comprehensive economic indicator dashboard"""
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    dashboard = {}
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    if country.lower() == "china":
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        dashboard.update({
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            'cpi': ak.macro_china_cpi(),
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            'gdp': ak.macro_china_gdp(),
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            'pmi': ak.macro_china_pmi(),
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            'money_supply': ak.macro_china_money_supply(),
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            'trade': ak.macro_china_trade(),
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            'interest_rate': ak.macro_china_interest_rate()
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        })
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    elif country.lower() == "usa":
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        dashboard.update({
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            'cpi': ak.macro_usa_cpi(),
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            'gdp': ak.macro_usa_gdp(),
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            'employment': ak.macro_usa_non_farm(),
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            'unemployment': ak.macro_usa_unemployment_rate(),
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            'pmi': ak.macro_usa_ism_pmi(),
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            'industrial': ak.macro_usa_industrial_production()
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        })
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    return dashboard
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# Usage
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china_dashboard = create_economic_dashboard("china")
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us_dashboard = create_economic_dashboard("usa")
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```
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## Data Characteristics
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### Update Frequencies
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- **Monthly indicators**: CPI, PPI, Industrial Production, Trade Data
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- **Quarterly indicators**: GDP, Fixed Asset Investment 
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- **Weekly indicators**: Money Supply (some components)
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- **Daily indicators**: Interest Rates, Exchange Rates
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### Data Coverage
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- **Historical depth**: Most indicators cover 10+ years of history
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- **Geographic coverage**: 7 major economies/regions
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- **Indicator breadth**: 226 different economic metrics
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- **Release timeliness**: Data typically available within days of official release
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### Common Use Cases
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1. **Macroeconomic Research**: Cross-country economic analysis
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2. **Investment Strategy**: Economic cycle and policy analysis
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3. **Risk Management**: Economic indicator monitoring for market risk
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4. **Academic Research**: Economic data for empirical studies
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5. **Policy Analysis**: Central bank and government policy impact assessment
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The economic data capabilities in AKShare provide comprehensive coverage for macroeconomic analysis across major global economies, supporting both academic research and practical investment decision-making.