[英]don't know how to coerce int64 and float64
I have a list which is filled form excel file (import to pandas)我有一个由 excel 文件填写的列表(导入到 Pandas)
a=[df.math[0],df.bio[0],df.chemistry[0]]
when I pass mean(a) it gives me the following error:当我通过 mean(a) 时,它给了我以下错误:
don't know how to coerce int64 and float64
How to fix it?如何解决? I tried
a=[float(df.math[0]),float(df.bio[0]),float(df.chemistry[0])]
- but still does not work我试过
a=[float(df.math[0]),float(df.bio[0]),float(df.chemistry[0])]
- 但仍然不起作用
What do you think is the problem ?你认为有什么问题?
I think here the simpliest is use numpy.mean
:我认为这里最简单的是使用
numpy.mean
:
x = np.mean(a)
Sample :样品:
df = pd.DataFrame({
'A':list('abcdef'),
'math':[4,5,4,5,5,4],
'chemistry':[7.3434,8,9,4,2,3],
'bio':[1,3,5,7,1,0],
'E':[5,3,6,9,2,4],
'F':list('aaabbb')
})
x = np.mean(a)
print (x)
4.114466666666666
Pandas solution:熊猫解决方案:
x = df.loc[0, ['math','bio','chemistry']].mean()
print (x)
4.114466666666666
Also for me working converting all values to floats:同样对我来说,将所有值转换为浮点数:
import statistics
a = [float(df.math[0]),float(df.bio[0]),float(df.chemistry[0])]
x = statistics.mean(a)
print (x)
4.114466666666667
import statistics
a = [float(x) for x in a]
x = statistics.mean(a)
print (x)
4.114466666666667
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