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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.

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  • $\begingroup$ Although the suggested answer does what you described, but I wonder what this kind of standardization mean? Maybe other features are directly correlation to velocity, what about their distribution, normally distributed? I am not sure I understand what goes on here. You may want to ask this question at stats.stackexchange.com $\endgroup$
    – TwinPenguins
    Apr 3, 2020 at 7:14
  • $\begingroup$ 1) it’s not clear why it was suggested to standardize by velocity, I guess it was some misunderstanding. Maybe you could clarify with the person who suggested it? 2) what does it mean „improve PCA“? 3) in general, it is advisable to standardize the data before PCA if the columns have significantly different scale. However, one takes the mean and standard deviation of particular column. See the following question and the questions/ answers linked therein. stats.stackexchange.com/questions/69157/… $\endgroup$
    – aivanov
    Jan 10, 2021 at 21:21

1 Answer 1

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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]

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