How I can calculate standard deviation for rows of a dataframe?

Question

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:

numpy.std(df['A'])

I want to do the same for rows

Answer 1

You can use DataFrame.std, which omit non numeric columns:

print (df.std())
S1    2.302173
S2    2.774887
S3    2.302173
dtype: float64

If need std by columns:

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:

print (df[['S1','S2']].std())
S1    2.302173
S2    2.774887
dtype: float64

There is different with numpy.std by default parameter ddof (Delta Degrees of Freedom):

• pandas by default ddof=1
• numpy by default ddof=0

So there are different outputs:

#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:

#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