Are you looking for something like this say for image feature vectors?
[jalal@goku example]$ python stat_all_data.py 
std is:  [0.2603135  0.72011209 0.4863223  ... 0.18916588 0.30144495 0.22394807] 2
mean is:  [0.2792807  1.14619135 0.63354225 ... 0.2145517  0.32491094 0.2647783 ] 2
[[0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]]
[[0.27167743 1.03813016 0.59986466 ... 0.31728339 0.06514341 0.12442386]
 [0.13293175 2.18057656 0.36396137 ... 0.27578598 0.1110082  0.05481818]
 [0.13702488 1.23713005 0.26817697 ... 0.06472887 0.00385979 0.00547262]
 ...
 [0.36607069 1.04307044 0.42863995 ... 0.36594653 0.22864909 0.10765515]
 [0.05682189 0.90699673 0.3805581  ... 0.06757039 0.04256344 0.01031811]
 [0.13570487 0.58878648 0.37142602 ... 0.32595682 0.0348542  0.06269506]]

The code is:
import numpy as np
from numpy import linalg as LA

infile1 = np.loadtxt('entire_dataset__resnet50_feature_vectors.txt')
std1 = np.std(infile1, 0)
mu1 = np.mean(infile1, 0)

print("std is: ", std1, 2)
print("mean is: ", mu1, 2)

print(np.minimum(infile1,0))

print(np.maximum(infile1,0))