技術標籤：python資料處理技巧python

掘金成分股的原始資料格式

    constituents                                                    trade_date
{'SHSE.600527': 0.009999999776482582, 'SHSE.600461': 0.019999999552965164,···}     2017-07-31 00:00:00
{'SHSE.603966': 0.009999999776482582, 'SHSE.603960': 0.009999999776482582,···}
 
     2017-08-31 00:00:00
···

我想要的資料格式

              stockcode
2016-01-29  SZSE.300002
2016-01-29  SZSE.300003
2016-01-29  SZSE.000568
2016-01-29  SZSE.000623
2016-01-29  SHSE.600398
                ...
2020-11-30  SHSE.600297
2020-11-30  SHSE.600352
2020-11-30  SHSE.600299
2020-11-30  SHSE.600406
2020-11-30  SHSE.600362

[17700 rows x 1 columns]
 

弄了好一會，記錄一下，方便下次用到

    stock300 = get_history_constituents(index='SHSE.000300', start_date='2016-01-01', end_date='2020-12-01')#[0]['constituents']
    df300=pd.DataFrame(stock300)
    df_300=df300.constituents.apply(lambda x:list(x.keys()))#.iloc[:,1]
    df_300.index=df300.trade_date
    #列表展開 然後每個列表再做成df,
    list300=df_300.
 
values.flatten()
    indexs=list(map(lambda x:x.date(),df_300.index))
    #時間索引為成分股數量的長度
    listdf_300=list(map(lambda x,y:pd.DataFrame(x,index=[y]*len(x),columns=['stockcode']),list300,indexs))
    #list巢狀df的合併
    df_=reduce(lambda x,y:pd.concat([x,y]),listdf_300)
    print(df_)

    df_.to_csv('stock300.csv')#,index=False