PANDA将功能应用于按白天分组的数据

Question

我有一个看起来像这样的数据集：

date,value1,value2
2016-01-01 00:00:00,3,0
2016-01-01 01:00:00,0,0
2016-01-01 02:00:00,0,0
2016-01-01 03:00:00,0,0
2016-01-01 04:00:00,0,0
2016-01-01 05:00:00,0,0
2016-01-01 06:00:00,0,0
2016-01-01 07:00:00,0,2
2016-01-01 08:00:00,3,11
2016-01-01 09:00:00,14,14
2016-01-01 10:00:00,12,13
2016-01-01 11:00:00,11,13
2016-01-01 12:00:00,11,9
2016-01-01 13:00:00,17,21
2016-01-01 14:00:00,9,22
2016-01-01 15:00:00,10,9
2016-01-01 16:00:00,11,9
2016-01-01 17:00:00,8,8
2016-01-01 18:00:00,4,2
2016-01-01 19:00:00,5,7
2016-01-01 20:00:00,5,5
2016-01-01 21:00:00,3,4
2016-01-01 22:00:00,2,4
2016-01-01 23:00:00,2,4
2016-01-02 00:00:00,0,0
2016-01-02 01:00:00,0,0
2016-01-02 02:00:00,0,0
2016-01-02 03:00:00,0,0
2016-01-02 04:00:00,0,0
2016-01-02 05:00:00,0,0
2016-01-02 06:00:00,1,0
2016-01-02 07:00:00,0,0
2016-01-02 08:00:00,0,0
2016-01-02 09:00:00,0,0
2016-01-02 10:00:00,0,0
2016-01-02 11:00:00,0,0
2016-01-02 12:00:00,0,0
2016-01-02 13:00:00,1,0
2016-01-02 14:00:00,0,0
2016-01-02 15:00:00,0,0
2016-01-02 16:00:00,0,0
2016-01-02 17:00:00,0,0
2016-01-02 18:00:00,0,0
2016-01-02 19:00:00,0,0
2016-01-02 20:00:00,1,0
2016-01-02 21:00:00,0,0
2016-01-02 22:00:00,0,0
2016-01-02 23:00:00,0,0

我要做的是计算每天Value1和Value2之间的RMSE。因此，基本上，我想运行31次功能(每天一次(，输入将是当天的24个条目(每小时1个(我尝试使用

rmse(df.groupby([df.index.day]).mean().value1, 
    df.groupby([df.index.day]).mean().value2)

，但它给了我一个值，我想要的是每天带有RMSE的列表，例如

daily_rmse = [rmse01_01, rmse01_02, ..., rmse01_31]

Answer 1

使用 sklearn s mean_squared_error

from sklearn.metrics import mean_squared_error
df.groupby(df.date.dt.date).apply(
    lambda x: mean_squared_error(x.value1, x.value2) ** .5)
date
2016-01-01    3.494043
2016-01-02    0.377964
dtype: float64

Answer 2

您不需要继续重做groupby，并且需要在其每个元素上计算rmse，而不是按照均值顺序：

gb = df.groupby(df.index.date)
mean_by_day = gb.mean()
rmse_by_day = gb.std(ddof=0)

我怀疑您所应用的RMSE公式完全等于由元素数量归一化的标准偏差(不是元素的数量-1，pandas中的默认值为默认值(。

您现在应该能够访问mean_by_day.value1和std_by_day.value1以获取所需的值。

我获得的mean_by_day的值是

              value1    value2
2016-01-01  5.416667  6.541667
2016-01-02  0.125000  0.000000

同样，对于rmse_by_day，我得到

              value1    value2
2016-01-01  5.139039  6.422481
2016-01-02  0.330719  0.000000

请注意，使用索引的date字段而不是day，如果您的数据持续了多个月，则可以重复。