You can use .std(axis=1) [pandas-doc] instead, this will result in a Series with as indices the indices of your dataframe, and as values, the standard deviation of the two values in the corresponding columns:

>>> df.std(axis=1)
0    1.414214
1    2.687006
2    1.626346
3    1.223295
4    1.025305
5    1.732412
6    1.965757
dtype: float64

Use std by row (axis=1):

df['stdDev'] = df[['A', 'B', 'C', 'D']].std(axis=1)

output:

  Key  A  B   C   D    stdDev
0   X  1  2   3   4  1.290994
1   y  4  5   6   7  1.290994
2   z  8  9  10  11  1.290994

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