如何使用Python计算最近三个非NaN值的平均值

Question

我有一个数据帧df，如下所示。我想计算最后3个非nan列的平均值。如果非缺失列少于三列，则缺少平均值。

name day1 day2 day3 day4  day5 day6 day7
A    1     1   nan   2    3    0   3
B    nan   nan nan   nan  nan  nan 3
C    1     1   0     1    1    1   1
D    1     1   0     1    nan  1   4

expect输出应如下所示

name day1 day2 day3 day4  day5 day6 day7    expected 
A    1     1   nan   2    3    0   3        2     <-  1/3*(day5 + day6 + day7)
B    nan   nan nan   nan  nan  nan 3        nan   <-  less than 3 non-missing
C    1     1   0     1    1    1   1        1     <-  1/3*(day5 + day6 + day7)
D    1     1   0     1    nan  1   4        2    <-  1/3 *(day4 + day6 + day7)

我知道如何计算最后三列的平均值，并计算有多少未丢失的观察值。df.iloc[:, 5:7].count(axis=1) average of the last three column df.iloc[:, 5:7].count(axis=1) number of non-nan in the last three column

如果有少于3个未丢失的观察值，我知道如何使用df.iloc[:, 1:7].count(axis=1) <= 3将平均值设置为丢失。

但我正在努力寻找一种方法来计算最后三个未丢失列的平均值。有人能教我怎么解决这个问题吗？

Answer 1

使用justify的矢量化模型-

N = 3 # last N entries for averaging
avg = np.mean(justify(df.values,invalid_val=np.nan,axis=1, side='right')[:,-N:],1)
df['expected'] = avg

Answer 2

您可以通过自定义函数使用pd.DataFrame.apply。这只是部分矢量化的。

def mean_calculator(row):
    non_nulls = row.notnull()
    if non_nulls.sum() < 3:
        return np.nan
    return row[non_nulls].values[-3:].mean()

df['expected'] = df.iloc[:, 1:].apply(mean_calculator, axis=1)

print(df)

  name  day1  day2  day3  day4  day5  day6  day7  expected
0    A   1.0   1.0   NaN   2.0   3.0   0.0     3       2.0
1    B   NaN   NaN   NaN   NaN   NaN   NaN     3       NaN
2    C   1.0   1.0   0.0   1.0   1.0   1.0     1       1.0
3    D   1.0   1.0   0.0   1.0   NaN   1.0     4       2.0

Answer 3

您可以使用以下函数从计算expected列开始：

expected = df.apply(lambda x: x[~x.isnull()][-3:].mean(), axis = 1)

并将这些值插入到至少具有3有效值的列中：

m = df.isnull().sum(axis=1) > 3
df.loc[~m,'expected'] = expected.mask(m)

       day1  day2  day3  day4  day5  day6  day7  expected
name                                                    
A      1.0   1.0   NaN   2.0   3.0   0.0     3       2.0
B      NaN   NaN   NaN   NaN   NaN   NaN     3       NaN
C      1.0   1.0   0.0   1.0   1.0   1.0     1       1.0
D      1.0   1.0   0.0   1.0   NaN   1.0     4       2.0