<jupyter_start><jupyter_text>Markdown line<jupyter_code>import numpy as np
X = np.array([[1,2,3],[4,5,6],[7,8,9]])
X.shape
np.mean(X)
np.sum(X)
np.std(X)
X
X = np.array([[1,2,3],[4,5,6],[7,8,9],[7,2,1],[5,9,3]])
X
X.shape
X[:,1].max()<jupyter_output><empty_output><jupyter_text>Column normalization gets rid of scale.  Having variables in different scales could be dangerous.Through normalization, each feature is reduced to lie between 0 and 1.  Hence all points now lie in the unit hyper-cube next to the origin<jupyter_code>import pandas as pd
df = pd.DataFrame(X)
df
Y = np.array(df[[0,1]])
Y
Z = np.cov(np.array(df[0]),np.array(df[1]))
Z
Z.shape
Z
np.array(df[0]).shape
np.cov(X).shape
X
df1<jupyter_output><empty_output>
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