我有一个数据框架，其中包含通过客户分析得出的不同项目的销售时间序列。对于每个项目和给定的一天，我要计算： -最近2天总销售额中我最好的客户的份额 -最近2天总销量中我的主要客户（从列表中）所占的份额
我尝试了此处提供的解决方案： -对于总和： Pandas sum over a date range for each category separately -对于条件分组依据：Pandas groupby with identification of an element with max value in another column
可以通过以下方式生成示例数据框：
import pandas as pd
from datetime import timedelta
df_1 = pd.DataFrame()
df_2 = pd.DataFrame()
df_3 = pd.DataFrame()
# Create datetimes and data
df_1['item'] = [1, 1, 1, 2, 2, 2, 2]
df_1['date'] = pd.date_range('1/1/2018', periods=7, freq='D')
df_1['customer'] = ['a', 'b', 'c', 'a', 'b', 'b', 'c']
df_1['sales']= [2, 4, 1, 5, 7, 2, 3]
df_2['item'] = [1, 1, 1, 2, 2, 2, 2]
df_2['date'] = pd.date_range('1/1/2018', periods=7, freq='D')
df_2['customer'] = ['b', 'b', 'c', 'a', 'a','c', 'a']
df_2['sales']= [2, 3, 4, 2, 3, 5, 6]
df_3['item'] = [1, 1, 1, 2, 2, 2, 2]
df_3['date'] = pd.date_range('1/1/2018', periods=7, freq='D')
df_3['customer'] = ['b', 'c', 'a', 'c', 'b', 'a', 'b']
df_3['sales']= [6, 5, 2, 3, 4, 5, 6]
df = pd.concat([df_1, df_2, df_3])
df = df.sort_values(['item', 'date'])
df.reset_index(drop=True)
df
看起来像这样：
item date customer sales
0 1 2018-01-01 a 2
0 1 2018-01-01 b 2
0 1 2018-01-01 b 6
1 1 2018-01-02 b 4
1 1 2018-01-02 b 3
1 1 2018-01-02 c 5
2 1 2018-01-03 c 1
2 1 2018-01-03 c 4
2 1 2018-01-03 a 2
3 2 2018-01-04 a 5
3 2 2018-01-04 a 2
3 2 2018-01-04 c 3
4 2 2018-01-05 b 7
4 2 2018-01-05 a 3
4 2 2018-01-05 b 4
5 2 2018-01-06 b 2
5 2 2018-01-06 c 5
5 2 2018-01-06 a 5
6 2 2018-01-07 c 3
6 2 2018-01-07 a 6
6 2 2018-01-07 b 6
我希望得到以下结果：
item date sales_at_day sales_last_2_days a_share top_share
1 2018-01-01 10 NaN NaN NaN
1 2018-01-02 12 10 0.20 0.20
1 2018-01-03 7 22 0.09 0.09
2 2018-01-04 10 NaN NaN NaN
2 2018-01-05 14 10 0.70 1.00
2 2018-01-06 12 24 0.29 0.42
2 2018-01-07 15 26 0.31 0.50
其中：
“ a_share”：最近2天（不包括当日）的客户“ a”的销售额占总销售额的比例 'top_share'：
中的客户销售份额top_cust = ['a', 'c']
列出最近2天（不包括当日）的总销售额
有什么想法吗？预先非常感谢：）
安迪
答案 0 :(得分：2)
使用：
#custom rolling with shift first day
f = lambda x: x.rolling(2, min_periods=1).sum().shift()
#aggregate sum
df1 = df.groupby(['item','date'], as_index=False)['sales'].sum()
#apply custom rolling per groups
df1['sales_last_2_days'] = df1.groupby('item')['sales'].apply(f).reset_index(drop=True, level=0)
#filter customer a and aggregate sum
a = df[df['customer'].eq('a')].groupby(['item','date'])['sales'].sum().rename('a_share')
#add new column to original
df1 = df1.join(a, on=['item','date'])
#applt custom rolling per groups and divide
df1['a_share'] = df1.groupby('item')['a_share'].apply(f).reset_index(drop=True, level=0) / df1['sales_last_2_days']
#verys similar like before, only test membership by isin
top_cust = ['a', 'c']
a = df[df['customer'].isin(top_cust)].groupby(['item','date'])['sales'].sum().rename('top_share')
df1 = df1.join(a, on=['item','date'])
df1['top_share'] = df1.groupby('item')['top_share'].apply(f).reset_index(drop=True, level=0) / df1['sales_last_2_days']
print (df1)
item date sales sales_last_2_days a_share top_share
0 1 2018-01-01 10 NaN NaN NaN
1 1 2018-01-02 12 10.0 0.200000 0.200000
2 1 2018-01-03 7 22.0 0.090909 0.318182
3 2 2018-01-04 10 NaN NaN NaN
4 2 2018-01-05 14 10.0 0.700000 1.000000
5 2 2018-01-06 12 24.0 0.416667 0.541667
6 2 2018-01-07 15 26.0 0.307692 0.500000
如果要在几天内使用rolling，则会更加复杂：
df1 = df.groupby(['item','date'], as_index=False)['sales'].sum()
sales1 = (df1.set_index('date')
.groupby('item')['sales']
.rolling('2D', min_periods=1)
.sum()
.groupby('item')
.shift()
.rename('sales_last_2_days')
)
df1 = df1.join(sales1, on=['item','date'])
df2 = df[df['customer'].eq('a')].groupby(['item','date'], as_index=False)['sales'].sum()
a = (df2.set_index('date')
.groupby('item')[['sales']]
.apply(lambda x: x.asfreq('D'))
.reset_index(level=0)
.groupby('item')['sales']
.rolling('2D', min_periods=1)
.sum()
.groupby('item')
.shift()
.rename('a_share')
)
print (a)
df1 = df1.join(a, on=['item','date'])
df1['a_share'] /= df1['sales_last_2_days']
top_cust = ['a', 'c']
df3 = df[df['customer'].isin(top_cust)].groupby(['item','date'], as_index=False)['sales'].sum()
b = (df3.set_index('date')
.groupby('item')[['sales']]
.apply(lambda x: x.asfreq('D'))
.reset_index(level=0)
.groupby('item')['sales']
.rolling('2D', min_periods=1)
.sum()
.groupby('item')
.shift()
.rename('top_share')
)
df1 = df1.join(b, on=['item','date'])
df1['top_share'] /= df1['sales_last_2_days']
print (df1)
item date sales sales_last_2_days a_share top_share
0 1 2018-01-01 10 NaN NaN NaN
1 1 2018-01-02 12 10.0 0.200000 0.200000
2 1 2018-01-03 7 22.0 0.090909 0.318182
3 2 2018-01-04 10 NaN NaN NaN
4 2 2018-01-05 14 10.0 0.700000 1.000000
5 2 2018-01-06 12 24.0 0.416667 0.541667
6 2 2018-01-07 15 26.0 0.307692 0.500000