Question

我有一个数据框，其中包含一个房子（ID）的每小时kWh能耗（消耗），持续几个月，例如：

                      ID  Consumption
DateTime                              
2016-07-01 01:00:00  1642     0.703400
2016-07-01 02:00:00  1642     0.724033
2016-07-01 03:00:00  1642     0.747300
2016-07-01 04:00:00  1642     0.830450
2016-07-01 05:00:00  1642     0.704917
2016-07-01 06:00:00  1642     0.708467
2016-07-01 07:00:00  1642     0.806533
2016-07-01 08:00:00  1642     0.774483
2016-07-01 09:00:00  1642     0.724833
2016-07-01 10:00:00  1642     0.721900
2016-07-01 11:00:00  1642     0.729450
2016-07-01 12:00:00  1642     0.757233
2016-07-01 13:00:00  1642     0.744667

这里DateTime是类型的索引。我的目标是找到一周内每小时的平均消费和差异，即（24 * 7 = 168小时）

HourOfWeek    Consumption
1             0.703400
2             0.724033
...
168           0.876923

我试过了

print (df.groupby(df.index.week)['Consumption'].transform('mean'))

然而，这并没有给出正确的结果，如何在大熊猫中完成？任何帮助将不胜感激。

Answer 1

我认为groupby和dayofweek需要1，但需要添加0，因为两者中的第一个值都是mean。然后汇总df1 = (df.groupby((df.index.dayofweek + 1) * (df.index.hour + 1))['Consumption'].mean() .rename_axis('HourOfWeek') .reset_index()) print (df1) HourOfWeek Consumption 0 10 0.703400 1 15 0.724033 2 20 0.747300 3 25 0.830450 4 30 0.704917 5 35 0.708467 6 40 0.806533 7 45 0.774483 8 50 0.724833 9 55 0.721900 10 60 0.729450 11 65 0.757233 12 70 0.744667：

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Answer 2

即使迟到：我也遇到了类似的问题，我认为以上答案不正确，应该是

df.groupby((df.index.dayofweek) * 24 + (df.index.hour)).mean().rename_axis('HourOfWeek')

在上述答案中，由于分配的组不是唯一的（例如，星期一2pm和星期二1 am依此类推，依此类推

Pandas Dataframe group by week of week

2 个答案: