请考虑下面生成的数据框df:
import pandas as pd
def creatingDataFrame():
raw_data = {'code': [1, 2, 3, 2 , 3, 3],
'var1': [10, 20, 30, 20 , 30, 30],
'var2': [2,4,6,4,6,6],
'price': [20, 30, 40 , 50, 10, 20],
'sells': [3, 4 , 5, 1, 2, 3]}
df = pd.DataFrame(raw_data, columns = ['code', 'var1','var2', 'price', 'sells'])
return df
if __name__=="__main__":
df=creatingDataFrame()
setCode=set(df['code'])
listDF=[]
for code in setCode:
dfCode=df[df['code'] == code].copy()
print(dfCode)
lenDfCode=len(dfCode)
if(lenDfCode==1):
theData={'code': [dfCode['code'].iloc[0]],
'var1': [dfCode['var1'].iloc[0]],
'var2': [dfCode['var2'].iloc[0]],
'averagePrice': [dfCode['price'].iloc[0]],
'totalSells': [dfCode['sells'].iloc[0]]
}
else:
dfCode['price*sells']=dfCode['price']*dfCode['sells']
sumSells=np.sum(dfCode['sells'])
sumProducts=np.sum(dfCode['price*sells'])
dfCode['totalSells']=sumSells
av=sumProducts/sumSells
dfCode['averagePrice']=av
theData={'code': [dfCode['code'].iloc[0]],
'var1': [dfCode['var1'].iloc[0]],
'var2': [dfCode['var2'].iloc[0]],
'averagePrice': [dfCode['averagePrice'].iloc[0]],
'totalSells': [dfCode['totalSells'].iloc[0]]
}
dfPart=pd.DataFrame(theData, columns = ['code', 'var1','var2', 'averagePrice','totalSells'])
listDF.append(dfPart)
newDF = pd.concat(listDF)
print(newDF)
我有这个数据框
code var1 var2 price sells
0 1 10 2 20 3
1 2 20 4 30 4
2 3 30 6 40 5
3 2 20 4 50 1
4 3 30 6 10 2
5 3 30 6 20 3
我想生成以下数据框:
code var1 var2 averagePrice totalSells
0 1 10 2 20.0 3
0 2 20 4 34.0 5
0 3 30 6 28.0 10
请注意,此数据框是通过评估每个代码的平均价格和总销售额来创建的。此外,var1和var2对于每个代码都是相同的。上面的python代码可以做到这一点,但我知道它效率低下。我相信可以使用groupby完成所需的解决方案,但我无法生成它。
答案 0 :(得分:2)
情况不同,apply
与pd.Series
df.groupby(['code','var1','var2']).apply(lambda x : pd.Series({'averagePrice': sum(x['sells']*x['price'])/sum(x['sells']),'totalSells':sum(x['sells'])})).reset_index()
Out[366]:
code var1 var2 averagePrice totalSells
0 1 10 2 20.0 3.0
1 2 20 4 34.0 5.0
2 3 30 6 28.0 10.0