熊猫翻滚几天并得到总和答案

【问题标题】：Pandas rolling over days and getting sum熊猫翻滚几天并得到总和
【发布时间】：2020-07-25 09:35:35
【问题描述】：

这是我的数据框

d= {'dates': ['2020-07-16','2020-07-15','2020-07-14','2020-07-13','2020-07-16','2020-07-15','2020-07-14','2020-07-13'], 
    'location':['Paris','Paris','Paris','Paris','NY','NY','NY','NY'],'T':[100,200,300,400,10,20,30,40]} 
df = pandas.DataFrame(data=d)
df['dates']=pandas.to_datetime(df['dates'])
df

    dates   location    T
0   2020-07-16  Paris   100
1   2020-07-15  Paris   200
2   2020-07-14  Paris   300
3   2020-07-13  Paris   400
4   2020-07-16  NY       10
5   2020-07-15  NY       20
6   2020-07-14  NY       30
7   2020-07-13  NY       40

我想为过去 2 天（包括当前日期）滚动的给定位置获取一些 T 值。这是我想要的熊猫：

    dates   location    T     SUM2D
0   2020-07-16  Paris   100     300
1   2020-07-15  Paris   200     500
2   2020-07-14  Paris   300     700
3   2020-07-13  Paris   400     NaN
4   2020-07-16  NY       10      30
5   2020-07-15  NY       20      50
6   2020-07-14  NY       30      70
7   2020-07-13  NY       4      NaN

我试过玩这句话没有成功：

df['SUM2D'] = df.set_index('dates').groupby('location').rolling(window=2, freq='D').sum()['T'].values

【问题讨论】：

标签： python pandas dataframe pandas-groupby

【解决方案1】：

尝试在索引之前对数据帧进行排序：

df = df.sort_values(['location','dates']).set_index('dates')
df['SUM2D'] = df.groupby('location')['T'].rolling(window=2, freq='D').sum().values

df[::-1]

结果集：

           location    T  SUM2D
dates                          
2020-07-16    Paris  100  300.0
2020-07-15    Paris  200  500.0
2020-07-14    Paris  300  700.0
2020-07-13    Paris  400    NaN
2020-07-16       NY   10   30.0
2020-07-15       NY   20   50.0
2020-07-14       NY   30   70.0
2020-07-13       NY   40    NaN

更简洁优雅的解决方案是使用transform:

df['SUM2D'] = df.sort_values(['dates']).groupby('location')['T'].transform(lambda x: x.rolling(2, 2).sum())

现在的结果是：

       dates location    T  SUM2D
0 2020-07-16    Paris  100  300.0
1 2020-07-15    Paris  200  500.0
2 2020-07-14    Paris  300  700.0
3 2020-07-13    Paris  400    NaN
4 2020-07-16       NY   10   30.0
5 2020-07-15       NY   20   50.0
6 2020-07-14       NY   30   70.0
7 2020-07-13       NY   40    NaN

【讨论】：

只需将 df[::-1] 添加到您的第一个解决方案中即可重新排序 date 。谢谢！
刚刚编辑 - 重新排序了日期。如果解决方案没问题，请考虑接受它作为答案。