Standardize or Normalize Categorical values

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I am fairly new to data science (I'm using python) and found that it's better for us to standardize or normalize our data before we go further. My questions are :

  1. What if there are categorical values (binary and using one hot encoding, 0 or 1) such as male or female, do we need to standardize or normalize this kind of data?
  2. What if the categorical data is non-binary, for example, measurement of your health (1= poor, 2=quite healthy, 3=healthy, 4=fit, 5=very fit). Do we still need to do the standardize or normalize these kind of data?
standardized data-science normalize python
1 Answers

If you have more than 2 categorical value, better convert them one hot encoding. Categorical values should not have a mathematical relationship. If you can not explain a mathematical order between your categorical data (e.g. fit > healthy? ) You should create one hot vectors and represent it as features :

                 Old Version      New version
                              1th 2th  3th  4th  5th  6th
poor              1           0   0    0    0    0     1
quite             2           0   0    0    0    1     0
healthy           3           0   0    0    1    0     0
healthy           4           0   0    1    0    0     0
fit               5           0   1    0    0    0     0
very fit          6           1   0    0    0    0     0

Basically you have 6 new features and each of them represent your one category.

Note: There is no need to apply normalization or standartization to binary data because it's already in [0,1]

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