下午所有，我有三個大的按結果分組。簡化的 df 如下所示。第一個 df 是所有客戶的總 RFQ 和總 RFQ 數量，沒有通過產品和貨幣進行細分。df1 = [('Year_Month', ['2017-11', '2017-12', '2018-01', '2018-02', '2018-05', '2018-06', '2018-07', '2018-08',]),('Client', ['RBMI', 'RBMI', 'RBMI', 'RBMI', 'QCBO', 'QCBO', 'QCBO', 'QCBO',]),('Total_RFQ_per_Client', [1, 2, 3, 4, 10, 20, 30, 40,]),('Total_RFQ_Volume_per_Client', ['1000', '2000', '3000', '4000', '10000', '20000', '30000', '40000',]), ]# create pandas dfdf1 = pd.DataFrame.from_items(df1)df1['Total_RFQ_per_Client']=df1.Total_RFQ_per_Client.astype('int64')df1['Total_RFQ_Volume_per_Client']=df1.Total_RFQ_Volume_per_Client.astype('int64')print(df1)# df1.info()print("") Year_Month Client Total_RFQ_per_Client Total_RFQ_Volume_per_Client0 2017-11 RBMI 1 10001 2017-12 RBMI 2 20002 2018-01 RBMI 3 30003 2018-02 RBMI 4 40004 2018-05 QCBO 10 100005 2018-06 QCBO 20 200006 2018-07 QCBO 30 300007 2018-08 QCBO 40 40000第二個 df 是已完成并添加了產品和貨幣列的所有客戶的詢價和詢價量。print("All Clients - Done RFQ's - Done RFQ Volume - Broken down into Product and Currency", end='\n') df2 = [('Year_Month', ['2017-11', '2018-01', '2018-01', '2018-02', '2018-05', '2018-07', '2018-08',]),('Client', ['RBMI', 'RBMI', 'RBMI', 'RBMI', 'QCBO', 'QCBO', 'QCBO',]),('Product', ['GOVT', 'GOVT', 'CORP', 'GOVT', 'GOVT', 'GOVT', 'GOVT',]),('currency_str', ['USD', 'USD', 'GBP', 'USD', 'USD', 'USD', 'USD',]),('Done_RFQ', [1, 1, 1, 1, 10, 20, 20,]),('Done_RFQ_Volume', [1000, 500, 500, 1000, 10000, 20000, 20000,]), ]
2 回答
MYYA
TA貢獻1868條經驗 獲得超4個贊
我認為“加入”語句在這里會做得更好：
import pandas as pd
df_joined = df1.set_index('Year_Month ').join(df2.set_index('Year_Month '))
df_joined = df_joined.set_index('Year_Month ').join(df3.set_index('Year_Month '))
如果您希望索引再次作為增量編號：
df_joined = df_joined.reset_index()
希望能幫助到你
侃侃爾雅
TA貢獻1801條經驗 獲得超16個贊
以下合并似乎有效：
dfTemp = pd.merge(df2, df3, how='outer', on=['Year_Month','Client','Product','currency_str'])
dfTemp = dfTemp.fillna(0)
dfTemp['Total_RFQ'] = dfTemp['Done_RFQ'] + dfTemp['Not_Done_RFQ']
dfTemp['Total_RFQ_Volume'] = (dfTemp['Done_RFQ_Volume']) + dfTemp['Not_Done_RFQ_Volume']
df_Client_Product_Ccy_Hit_Rate_Volumes = pd.merge(dfTemp, df1, how='inner', left_on=['Year_Month','Client'], right_on = ['Year_Month','Client'])
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