Standardizing features by one specific feature I am working on a project with a dataset that looks something like the following:

      velocity   accel_amp         f_vert   tau_vert      f_pitch_filt   tau
0    3.778595      -5.796777     2.400000  32.753227      1.600000   27.844535
1    1.970611      -6.087134     2.272727  32.638705      1.704545   30.639998
2    3.581163      -6.241817     2.400000  32.850969      1.600000   30.449256
3    4.735210      -6.109532     1.400000  28.809865      1.000000  127.749313
4    5.340568      -6.614317     1.400000  20.249699      1.000000  124.549628


I was suggested to standardize the last 5 features by velocity in order to improve my PCA.
Does this simply mean to take each element in these last 5 columns, subtract the mean of the velocity column, and then divide by the standard deviation of the velocity column? 

That is how I interpreted this suggestion. Is there a functionality in Python for doing this? Any suggestions or clarification would be appreciated.

Thanks.
If this is a pandas DataFrame:

vel_mean = df.velocity.mean()
 vel_std = df.velocity.std()
 df = df.apply(lambda x: (x - vel_mean) / vel_std)


In case it is a numpy array:

data = (data - np.mean(data, axis=0)[0]) / np.std(data, axis=0)[0]