用 group by 的平均值填充 NaN

Question

我的数据集看起来像这样

Month DayOfWeek  Class A1  A2 ... A999
July  Monday     Bata  7   9  ... 5
July  Tuesay     Bata  3   1  ... 2
July  Sunday     Bata  4   5  ... 6
July  Monday     Adid  9   8  ... 5
July  Sunday     Adid  4   0  ... 4
Sept  Monday     Nike  7   5  ... 7
Sept  Sunday     Nike  8   3  ... 7
Sept  Satday     Adid  2   7  ... 7
Sept  Monday     Bata  8   9  ... 4
Oct   Monday     Nike  4   2  ... 5
Oct   Sunday     Bata  8   6  ... 3
July  Monday     Nike  NaN NaN    NaN
Sept  Sunday     Nike  NaN NaN    NaN
Oct   Satday     Nike  NaN NaN    NaN
Sept  Monday     Bata  NaN NaN    NaN

我想用之前记录的平均值填充NaNs

我知道我可以使用

df['A1'] = df['A1'].fillna((df['A1'].mean()))

但这是一个糟糕的方法，因为我有超过 1000 列，以后它们可能会增加

加上

我想根据 Month 和 DayOfWeek 求平均值

此记录

July  Monday     Nike  NaN NaN    NaN

因此，平均值将只是 月份 = 七月 & DayOfWeek = 星期一

的记录的平均值

我该怎么做？

Answer 1

给你：

df['A1'] = df.groupby(['Month','DayOfWeek'])['A1'].transform(lambda x: x.fillna(x.mean()))

上面仍然会给出一个空值，因为"Month = Oct & DayOfWeek = Monday"没有值。在这种情况下，您可能需要编写第二个代码来填充该月的平均值或 DayOfWeek 的平均值。下面的代码片段用具有空值的记录的月份平均值填充空值：

df['A1'] = df.groupby('Month')['A1'].transform(lambda x: x.fillna(x.mean()))

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用 group by 的平均值填充 NaN

Fill NaN with mean value with group by

python

nan

dataframe