A股市场统计（营业收入增长率、净利润增长率及净利润比市值近十年的平均数、中位数）

    from WindPy import *
    import pandas as pd
    import numpy as np
    from datetime import datetime
    from WindCharts import *  
    
    w.start()
    hs300_stock_list=w.wset("sectorconstituent", "date=2009-05-02;windcode=000300.SH", usedf=True)
    zz800_stock_list=w.wset("sectorconstituent", "date=2009-05-02;windcode=000906.SH", usedf=True)
    zz800=list(zz800_stock_list[1]['wind_code'])
    hs300=list(hs300_stock_list[1]['wind_code'])
    data_df=pd.DataFrame()
    for i in range(11):
    #统计近十年的营收、净利润增长率及净利润比市值的中位数及平均数
    year=2009+i
    #选择股票标的的范围
    target_stocks=hs300
    data_df11=w.wss(target_stocks, "oper_rev,net_profit_is", "rptDate="+str(year)+"1231;rptType=1", usedf=True)[1]
    data_df11.rename(columns={'OPER_REV':str(year)+'-营收','NET_PROFIT_IS':str(year)+'-净利润'}, inplace = True)
    
    market_value=w.wss(target_stocks,"ev", "tradeDate="+str(year)+"1231", usedf=True)[1]
    market_value.rename(columns={'EV':str(year)+'-市值'}, inplace = True)    
    data_df=pd.concat([data_df11,market_value,data_df],axis=1)
     #输出格式为表格格式，可直接复制生成表格 
    print('年份|平均数数营业收入增长率|平均数净利润增长率')
    print('-------- | -----|-----')
    for i in range(10):
    year=2009+i
    data_df[str(year+1)+'营收增长率']=data_df.apply(lambda x:(x[str(year+1)+'-营收']-x[str(year)+'-营收'])/x[str(year)+'-营收'],axis=1)
    data_df[str(year+1)+'净利增长率']=data_df.apply(lambda x:(x[str(year+1)+'-净利润']-x[str(year)+'-净利润'])/x[str(year)+'-净利润'],axis=1)
    mean_revenue=data_df[str(year+1)+'营收增长率'].mean()
    mean_net_profit=data_df[str(year+1)+'净利增长率'].mean()
    print(year+1,'|','%.2f%%'% (mean_revenue*100),'|','%.2f%%' % (mean_net_profit*100))
    
    
    
    
    print('年份|中位数营业收入增长率|中位数净利润增长率')
    print('-------- | -----|-----')
    for i in range(10):
    year=2009+i
    data_df[str(year+1)+'营收增长率']=data_df.apply(lambda x:(x[str(year+1)+'-营收']-x[str(year)+'-营收'])/x[str(year)+'-营收'],axis=1)
    data_df[str(year+1)+'净利增长率']=data_df.apply(lambda x:(x[str(year+1)+'-净利润']-x[str(year)+'-净利润'])/x[str(year)+'-净利润'],axis=1)
    median_revenue=data_df[str(year+1)+'营收增长率'].median()
    median_net_profit=data_df[str(year+1)+'净利增长率'].median()
    print(year+1,'|','%.2f%%'% (median_revenue*100),'|','%.2f%%' % (median_net_profit*100))
    
    print('年份|净利润比市值平均数|净利润比市值中位数')    
    
    print('-------- | -----|-----')
    for i in range(10):
    year=2009+i
    
    
    data_df[str(year+1)+'净利润/股价']=data_df.apply(lambda x:(x[str(year+1)+'-净利润']/x[str(year)+'-市值']),axis=1)
    mean_ni_ev=data_df[str(year+1)+'净利润/股价'].mean()
    median_ni_ev=data_df[str(year+1)+'净利润/股价'].median()  
    print(year+1,'|','%.2f%%'% (mean_ni_ev*100),'|','%.2f%%' % (median_ni_ev*100))
    
    
    
    
    python
    
    
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