创建一列，该列是数据框中多个列的平均值

Question

因此，我研究了多种可能的解决方案，但似乎都不起作用

基本上，我想在我的数据框中创建一个新列，它是多个其他列的平均值。我希望此平均值排除NaN值，但即使行中有NaN值，也要计算平均值

我有一个看起来像这样的数据帧（但实际上是Q222-229）：

ID   Q1   Q2   Q3   Q4   Q5
1    4    NaN  NaN  NaN  NaN
2    5    7    8    NaN  NaN
3    7    1    2    NaN  NaN
4    2    2    3    4    1
5    1    3    NaN  NaN  NaN

我想创建一个列，它是Q1、Q2、Q3、Q4、Q5的平均值，即：

ID   Q1   Q2   Q3   Q4   Q5   avg_age
1    4    NaN  NaN  NaN  NaN  4
2    5    7    8    NaN  NaN  5.5
3    7    1    2    NaN  NaN  3.5
4    2    2    3    4    1    2
5    1    3    NaN  NaN  NaN  2

（忽略值）

但是，我尝试过的每个方法都会在avg_age列中返回NaN值，这让我认为，当忽略NaN值时，pandas会忽略整行。但我不希望发生这种情况，而是希望返回的平均值忽略NaN值

以下是我迄今为止所尝试的：

1.
    avg_age = s.loc[: , "Q222":"Q229"]
    avg_age = avg_age.mean(axis=1)
    s = pd.concat([s, avg_age], axis=1)

2.
    s['avg_age'] = s[['Q222', 'Q223', 'Q224', 'Q225', 'Q226', 'Q227', 'Q228', 'Q229']].mean(axis=1)

3.

    avg_age = ['Q222', 'Q223', 'Q224', 'Q225', 'Q226', 'Q227', 'Q228', 'Q229']
    s.loc[:, 'avg_age'] = s[avg_age].mean(axis=1)

我不确定我最初对值进行编码的方式是否有问题，因此以下是我的代码供参考：

#改变年龄变量输入

s['Q222'] = s['Q222'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                              ['2','3','4','5', '6', '7', '8', np.NaN])
s['Q223'] = s['Q223'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                              ['2','3','4','5', '6', '7', '8', np.NaN])
s['Q224'] = s['Q224'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                              ['2','3','4','5', '6', '7', '8', np.NaN])
s['Q225'] = s['Q225'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                              ['2','3','4','5', '6', '7', '8', np.NaN])
s['Q226'] = s['Q226'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                              ['2','3','4','5', '6', '7', '8', np.NaN])
s['Q227'] = s['Q227'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                              ['2','3','4','5', '6', '7', '8', np.NaN])
s['Q228'] = s['Q228'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                              ['2','3','4','5', '6', '7', '8', np.NaN])
s['Q229'] = s['Q229'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                              ['2','3','4','5', '6', '7', '8', np.NaN])

s['Q222'] = s['Q222'].replace(['0-4', '05-11', '12-15', '16-17'], '1')
s['Q223'] = s['Q223'].replace(['0-4', '05-11', '12-15', '16-17'], '1')
s['Q224'] = s['Q224'].replace(['0-4', '05-11', '12-15', '16-17'], '1')
s['Q225'] = s['Q225'].replace(['0-4', '05-11', '12-15', '16-17'], '1')
s['Q226'] = s['Q226'].replace(['0-4', '05-11', '12-15', '16-17'], '1')
s['Q227'] = s['Q227'].replace(['0-4', '05-11', '12-15', '16-17'], '1')
s['Q228'] = s['Q228'].replace(['0-4', '05-11', '12-15', '16-17'], '1')
s['Q229'] = s['Q229'].replace(['0-4', '05-11', '12-15', '16-17'], '1')

提前感谢任何能够提供帮助的人

Answer 1

skipna=True

可以使用list comprehension获得平均值，使用mean()获得平均值：

df['ave_age'] = df[[col for col in df.columns if 'Q' in col]].mean(axis = 1,skipna = True)

Answer 2

DataFrame.mean()的默认行为应该满足您的要求

下面是一个示例，显示在列的子集上取平均值并将其放置在新创建的列中：

In[19]: tmp
Out[19]: 
   a  b    c
0  1  2  5.0
1  2  3  6.0
2  3  4  NaN

In[24]: tmp['mean'] = tmp[['b', 'c']].mean(axis=1)

In[25]: tmp
Out[25]: 
   a  b    c  mean
0  1  2  5.0   3.5
1  2  3  6.0   4.5
2  3  4  NaN   4.0

至于你的代码出了什么问题：

s['Q222'] = s['Q222'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                         ['2','3','4','5', '6', '7', '8', np.NaN])

数据帧中没有数值（即2、3、4），而是字符串（'2'、'3'和'4'）。DataFrame.mean()函数将这些字符串视为NaN，因此您将得到NaN作为所有平均值计算的结果

尝试用数字填充你的框架，如下所示：

 s['Q222'] = s['Q222'].replace(['18-24', '25-34','35-44', '45-54','55-64', '65-74', '75 or older', "Don't know"],
                          [2, 3, 4, 5, 6, 7, 8, np.NaN])