Question

我有一个数据框-df，如下所示：

Stud_id card    Nation  Gender  Age  Code   Amount  yearmonth
111     1       India   M      Adult 543    100     201601
111     1       India   M      Adult 543    100     201601
111     1       India   M      Adult 543    150     201602
111     1       India   M      Adult 612    100     201602
111     1       India   M      Adult 715    200     201603
222     2       India   M      Adult 715    200     201601
222     2       India   M      Adult 543    100     201604
222     2       India   M      Adult 543    100     201603
333     3       India   M      Adult 543    100     201601
333     3       India   M      Adult 543    100     201601
333     4       India   M      Adult 543    150     201602
333     4       India   M      Adult 612    100     201607

现在，我想要两个数据框，如下所示：

df_1

card    Code    Total_Amount    Avg_Amount
1       543     350             175
2       543     200             100
3       543     200             200
4       543     150             150
1       612     100             100
4       612     100             100
1       715     200             200
2       715     200             200

df_1的逻辑：

1.  Total_Amount : For each unique card and unique Code get the sum of amount ( For eg : card : 1 , Code : 543 = 350 )
2.  Avg_Amount: Divide the Total amount by no.of unique yearmonth for each unique card and unique Code ( For eg : Total_Amount = 350, No. Of unique yearmonth is 2 = 175

df_2：

Code    Avg_Amount
543     156.25
612     100
715     200

df_2的逻辑：

1.  Avg_Amount: Sum of Avg_Amount of each Code in df_1 (For eg. Code:543 the Sum of Avg_Amount is 175+100+200+150 = 625. Divide it by no.of rows - 4. So 625/4 = 156.25

创建数据框的代码-df：

df=pd.DataFrame({'Cus_id': (111,111,111,111,111,222,222,222,333,333,333,333), 
                     'Card': (1,1,1,1,1,2,2,2,3,3,4,4), 
                     'Nation':('India','India','India','India','India','India','India','India','India','India','India','India'), 
                     'Gender': ('M','M','M','M','M','M','M','M','M','M','M','M'),
                     'Age':('Adult','Adult','Adult','Adult','Adult','Adult','Adult','Adult','Adult','Adult','Adult','Adult'),
                     'Code':(543,543,543,612,715,715,543,543,543,543,543,612),
                     'Amount': (100,100,150,100,200,200,100,100,100,100,150,100),
                     'yearmonth':(201601,201601,201602,201602,201603,201601,201604,201603,201601,201601,201602,201607)})

获得所需df_2的代码：

 df1 = df_toy.groupby(['Card','Code'])['yearmonth','Amount'].apply(
                         lambda x: [sum(x.Amount),sum(x.Amount)/len(set(x.yearmonth))]).apply(
                         pd.Series).reset_index()
    df1.columns= ['Card','Code','Total_Amount','Avg_Amount']
    df2 = df1.groupby('Code')['Avg_Amount'].apply(lambda x: sum(x)/len(x)).reset_index(
                              name='Avg_Amount')

尽管代码可以正常工作，但由于我的数据集非常耗时。我在寻找优化的代码？我认为申请职能需要时间吗？是否有更好的优化代码pls？

Answer 1

对于DataFrame 1，您可以执行以下操作：

tmp = df.groupby(['Card', 'Code'], as_index=False) \
        .agg({'Amount': 'sum', 'yearmonth': pd.Series.nunique})

df1 = tmp.assign(Avg_Amount=tmp.Amount / tmp.yearmonth) \
         .drop(columns=['yearmonth'])

   Card  Code  Amount  Avg_Amount
0     1   543     350       175.0
1     1   612     100       100.0
2     1   715     200       200.0
3     2   543     200       100.0
4     2   715     200       200.0
5     3   543     200       200.0
6     4   543     150       150.0
7     4   612     100       100.0

对于DataFrame 2，您可以执行以下操作：

df1.groupby('Code', as_index=False) \
   .agg({'Avg_Amount': 'mean'})

   Code  Avg_Amount
0   543      156.25
1   612      100.00
2   715      200.00

有没有一种方法可以优化groupby期间的熊猫应用功能？

1 个答案: