pandas 如何计算数据帧行的标准偏差？
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How I can calculate standard deviation for rows of a dataframe?
提问by NamAshena
df:
name group S1 S2 S3
A mn 1 2 8
B mn 4 3 5
C kl 5 8 2
D kl 6 5 5
E fh 7 1 3
output:
std (S1,S2,S3)
3.78
1
3
0.57
3.05
This is working for getting std for a column:
这适用于获取列的 std：
numpy.std(df['A'])
I want to do the same for rows
我想对行做同样的事情
回答by jezrael
You can use DataFrame.std, which omit non numeric columns:
您可以使用DataFrame.std，它省略了非数字列：
print (df.std())
S1 2.302173
S2 2.774887
S3 2.302173
dtype: float64
If need stdby columns:
如果需要std按列：
print (df.std(axis=1))
0 3.785939
1 1.000000
2 3.000000
3 0.577350
4 3.055050
dtype: float64
If need select only some numeric columns, use subset:
如果只需要选择一些数字列，使用子集：
print (df[['S1','S2']].std())
S1 2.302173
S2 2.774887
dtype: float64
There is different with numpy.stdby default parameter ddof(Delta Degrees of Freedom):
与numpy.std默认参数ddof（Delta degree of Freedom）不同：
- pandas by default
ddof=1 - numpy by default
ddof=0
- 默认Pandas
ddof=1 - 默认为 numpy
ddof=0
So there are different outputs:
所以有不同的输出：
#ddof=1
print (df.std(axis=1))
0 3.785939
1 1.000000
2 3.000000
3 0.577350
4 3.055050
dtype: float64
#ddof=0
print (np.std(df, axis=1))
0 3.091206
1 0.816497
2 2.449490
3 0.471405
4 2.494438
dtype: float64
But you can change it very easy:
但是你可以很容易地改变它：
#same output as pandas function
print (np.std(df, ddof=1, axis=1))
0 3.785939
1 1.000000
2 3.000000
3 0.577350
4 3.055050
dtype: float64
#same output as numpy function
print (df.std(ddof=0, axis=1))
0 3.091206
1 0.816497
2 2.449490
3 0.471405
4 2.494438
dtype: float64
回答by Stefano Fedele
When you can not do on rows whatever you can do on column you may use "transpose"
当你不能在行上做任何你可以在列上做的事情时，你可以使用“转置”
np.std( df.transpose()['S1'] )
