在python中重采樣和切片數據

Question

數據如下所示：

    High    Low Open    Close   Volume  Adj Close
Date                        
1999-12-31  1472.420044 1458.189941 1464.469971 1469.250000 374050000   1469.250000
2000-01-03  1478.000000 1438.359985 1469.250000 1455.219971 931800000   1455.219971
2000-01-04  1455.219971 1397.430054 1455.219971 1399.420044 1009000000  1399.420044
2000-01-05  1413.270020 1377.680054 1399.420044 1402.109985 1085500000  1402.109985
2000-01-06  1411.900024 1392.099976 1402.109985 1403.449951 1092300000  1403.449951
... ... ... ... ... ... ...
2020-01-06  3246.840088 3214.639893 3217.550049 3246.280029 3674070000  3246.280029
2020-01-07  3244.909912 3232.429932 3241.860107 3237.179932 3420380000  3237.179932
2020-01-08  3267.070068 3236.669922 3238.590088 3253.050049 3720890000  3253.050049
2020-01-09  3275.580078 3263.669922 3266.030029 3274.699951 3638390000  3274.699951
2020-01-10  3282.989990 3268.010010 3281.810059 3273.739990 920449258   3273.739990
5039 rows × 6 columns

由於這是每日數據，因此我對其進行了重新采樣：

weekly_resample = data.High.resample('M')這將生成一個 DatetimeIndexResampler 對象文件。 現在我想對這些數據進行切片以僅查看過去 10 周，為此我已經這樣做了：

weekly_resample = data.High.resample('M')[-1:10]

但這會產生錯誤：

KeyError: 'Column not found: slice(-1, 10, None)'

我如何切片過去 10 周？

Answer 1

將DataFrame.groupby與Grouper DataFrame.groupby使用，因此可以將GroupBy.tail用於最后 10 行：

weekly_resample = data.High.groupby(pd.Grouper(freq='M')).tail(10)
print (weekly_resample)
Date
1999-12-31    1472.420044
2000-01-03    1478.000000
2000-01-04    1455.219971
2000-01-05    1413.270020
2000-01-06    1411.900024
2020-01-06    3246.840088
2020-01-07    3244.909912
2020-01-08    3267.070068
2020-01-09    3275.580078
2020-01-10    3282.989990
Name: High, dtype: float64

使用resample解決方案是可能的，只需要Resampler.transform ：

weekly_resample = data.High.resample('M').transform(lambda x: x.iloc[-10:])
#alternative
#weekly_resample = data.High.resample('M').transform(lambda x: x.tail(10))
print (weekly_resample)
Date
1999-12-31    1472.420044
2000-01-03    1478.000000
2000-01-04    1455.219971
2000-01-05    1413.270020
2000-01-06    1411.900024
2020-01-06    3246.840088
2020-01-07    3244.909912
2020-01-08    3267.070068
2020-01-09    3275.580078
2020-01-10    3282.989990
Name: High, dtype: float64