I have a code snippet:

import numpy as np
x1 = [[1,4,2,1],       
      [1,1,4,5],
      [0.5,0.3, 1,6],
      [0.8,0.2,0.7,1]]

x2 = [[7,0,2,3],       
      [8,0,4,5],
      [0.1,0, 2,6],
      [0.1,0,0.16666667,6]]

np.true_divide(x1, x2)

The output is:

array([[0.14285714,        inf, 1.        , 0.33333333],
       [0.125     ,        inf, 1.        , 1.        ],
       [5.        ,        inf, 0.5       , 1.        ],
       [8.        ,        inf, 4.19999992, 0.16666667]])

I am aware that some elements will have zerodivision error which can be seen as 'inf'.

How can I use 'try and except' to change all these 'inf' results into 0? Or is there a better method to convert all those 'inf's into 0?

CodePudding user response：

You can use numpy.where to select the values for which the division result or the original values be retained:

import numpy as np
x1 = np.array([[1,4,2,1],       
               [1,1,4,5],
               [0.5,0.3, 1,6],
               [0.8,0.2,0.7,1]])
x2 = np.array([[7,0,2,3],       
               [8,0,4,5],
               [0.1,0, 2,6],
               [0.1,0,0.16666667,6]])

np.where(x2==0, 0, x1/x2)
# or
# np.where(x2==0, x2, np.true_divide(x1, x2))

Output:

array([[0.14285714, 0.        , 1.        , 0.33333333],
       [0.125     , 0.        , 1.        , 1.        ],
       [5.        , 0.        , 0.5       , 1.        ],
       [8.        , 0.        , 4.19999992, 0.16666667]])

CodePudding user response：

0/0 can handle by adding invalid='ignore' to numpy.errstate() introducing numpy.nan_to_num() to convert np.nan to 0.

with np.errstate(divide='ignore', invalid='ignore'):
    c = np.true_divide(x1,x2)
    c[c == np.inf] = 0
    c = np.nan_to_num(c)
print(c)

Output

[[0.14285714 0.         1.         0.33333333]
 [0.125      0.         1.         1.        ]
 [5.         0.         0.5        1.        ]
 [8.         0.         4.19999992 0.16666667]]