大佬教程收集整理的这篇文章主要介绍了创建一个列，它是 Pandas 中数据框中多列的平均值，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。
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
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
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','Q229']
s.loc[:,'avg_age'] = s[avg_age].mean(axis=1)
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',np.NaN])
s['Q224'] = s['Q224'].replace(['18-24',np.NaN])
s['Q225'] = s['Q225'].replace(['18-24',np.NaN])
s['Q226'] = s['Q226'].replace(['18-24',np.NaN])
s['Q227'] = s['Q227'].replace(['18-24',np.NaN])
s['Q228'] = s['Q228'].replace(['18-24',np.NaN])
s['Q229'] = s['Q229'].replace(['18-24',np.NaN])
s['Q222'] = s['Q222'].replace(['0-4','05-11','12-15','16-17'],'1')
s['Q223'] = s['Q223'].replace(['0-4','1')
s['Q224'] = s['Q224'].replace(['0-4','1')
s['Q225'] = s['Q225'].replace(['0-4','1')
s['Q226'] = s['Q226'].replace(['0-4','1')
s['Q227'] = s['Q227'].replace(['0-4','1')
s['Q228'] = s['Q228'].replace(['0-4','1')
s['Q229'] = s['Q229'].replace(['0-4','1')
skipna=True
@H_675_2@可以使用 list comprehension 获取列的平均值，并使用 @H_113_9@mean() 获取：
df['ave_age'] = df[[col for col in df.columns if 'Q' in col]].mean(axis = 1,skipna = TruE)
DataFrame.mean() 的默认行为应该按照您的意愿行事。
@H_675_2@以下示例显示对列的子集取平均值并将其放入新创建的列中：
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
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])
DataFrame.mean() 函数将这些字符串视为 NaN，因此您将获得 NaN 作为所有均值计算的结果。
@H_675_2@尝试用数字填充您的框架，如下所示：
s['Q222'] = s['Q222'].replace(['18-24',[2,3,4,5,6,7,8,np.NaN])
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