How to convert element to Z value using mean, std for each column?

I want to know how to convert each column’s value to some value using pandas and numpy.

import pandas as pd
import numpy as np

matrix = [[1,2,3], [4,5,6], [7,8,9]]

Original Matrix

Output Matrix

For examples, column ‘a’ is [1, 4, 7].
‘a”s mean is 4 and ‘a”s std is 2.45
So, Output is [(1-4) / 2.45, (4-4) / 2.45, (7-4) / 2.45]

How to convert each values using pandas and numpy?

>Solution :

Use:

matrix = [[1,2,3], [4,5,6], [7,8,9]]
df = pd.DataFrame(matrix, columns=list('abc'))

If need processing all columns subtract all values by means and divide by DataFrame.std with set ddof=0, because default is ddof=1.

df = df.sub(df.mean()).div(df.std(ddof=0))
print (df)
          a         b         c
0 -1.224745 -1.224745 -1.224745
1  0.000000  0.000000  0.000000
2  1.224745  1.224745  1.224745

If need processing only one column:

df['a'] = df['a'].sub(df['a'].mean()).div(df['a'].std(ddof=0))
print (df)
          a  b  c
0 -1.224745  2  3
1  0.000000  5  6
2  1.224745  8  9