numpy knowledge points

1、nonzero

      For one-dimensional data, the eligible subscript will be returned

>>> b1 = np.array([True, False, True, False])
>>> np.nonzero(b1)
    (array([0, 2]),)

     For two-dimensional data, a two-dimensional tuple is returned, the first dimension is the index of the eligible x, and the second dimension is the index of the eligible y

>>> b2 = np.array([[True, False, True], [True, False, False]])
>>> np.nonzero(b2)
    (array([0, 0, 1]), array([0, 2, 0]))

 2 、 var, std, cov

   var is the variance, std is the standard deviation, cov is the covariance, and the denominator is n-1

import numpy as np

# Build the test data with a mean of 10
sc = [9.7, 10, 10.3, 9.7, 10, 10.3, 9.7, 10, 10.3]

# output mean 10.0
print(np.mean(sc))

# output var, that is (0.09 + 0 + 0.09 + 0.09 + 0 + 0.09 + 0.09 + 0 + 0.09) = 0.54, then 0.54 / 9=0.06, output 0.06
print (np.var (sc))

# Equivalent to 0.06 root
print(np.std(sc))

# 0.54 / 8 = 0.0675
print (np.cov (sc))