Often times its useful to normalize (z-score) you features before plotting, so that they are on the same scale. Otherwise, some features will be weighted more heavily than others when doing PCA, and that may or may not be what you want. The normalize kwarg can be passed to the plot function. If normalize is set to ‘across’, the zscore will be computed for the column across all of the lists passed. Conversely, if normalize is set to ‘within’, the z-score will be computed separately for each column in each list. Finally, if normalize is set to ‘row’, each row of the matrix will be zscored. Alternatively, you can use the normalize function found in tools (see the third example).

```
# Code source: Andrew Heusser
# License: MIT
# import
import hypertools as hyp
import numpy as np
import matplotlib.pyplot as plt
# simulate data
cluster1 = np.random.multivariate_normal(np.zeros(3), np.eye(3), size=100)
cluster2 = np.random.multivariate_normal(np.zeros(3)+10, np.eye(3), size=100)
data = [cluster1, cluster2]
# plot normalized across lists
hyp.plot(data, '.', normalize='across', title='Normalized across datasets')
# plot normalized within list
hyp.plot(data, '.', normalize='within', title='Normalized within dataset')
# normalize by row
normalized_row = hyp.normalize(data, normalize='row')
# plot normalized by row
hyp.plot(normalized_row, '.', title='Normalized across row')
```

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