printing a two dimensional array in python

Question

I have to print this python code in a 5x5 array the array should look like this :

0 1 4 (infinity) 3
1 0 2 (infinity) 4
4 2 0  1         5
(inf)(inf) 1 0   3
3 4 5   3        0

can anyone help me print this table? using indices.

for k in range(n):
        for i in range(n):
            for j in range(n):
                if A[i][k]+A[k][j]<A[i][j]:
                    A[i][j]=A[i][k]+A[k][j]

Answer 1

A combination of list comprehensions and str joins can do the job:

inf = float('inf')
A = [[0,1,4,inf,3],
     [1,0,2,inf,4],
     [4,2,0,1,5],
     [inf,inf,1,0,3],
     [3,4,5,3,0]]

print('\n'.join([''.join(['{:4}'.format(item) for item in row]) 
      for row in A]))

yields

   0   1   4 inf   3
   1   0   2 inf   4
   4   2   0   1   5
 inf inf   1   0   3
   3   4   5   3   0

---

Using for-loops with indices is usually avoidable in Python, and is not considered "Pythonic" because it is less readable than its Pythonic cousin (see below). However, you could do this:

for i in range(n):
    for j in range(n):
        print '{:4}'.format(A[i][j]),
    print

The more Pythonic cousin would be:

for row in A:
    for val in row:
        print '{:4}'.format(val),
    print

However, this uses 30 print statements, whereas my original answer uses just one.

Answer 2

There is always the easy way.

import numpy as np
print(np.matrix(A))

Answer 3

I used numpy to generate the array, but list of lists array should work similarly.

import numpy as np
def printArray(args):
    print "\t".join(args)

n = 10

Array = np.zeros(shape=(n,n)).astype('int')

for row in Array:
    printArray([str(x) for x in row])

If you want to only print certain indices:

import numpy as np
def printArray(args):
    print "\t".join(args)

n = 10

Array = np.zeros(shape=(n,n)).astype('int')

i_indices = [1,2,3]
j_indices = [2,3,4]

for i in i_indices:printArray([str(Array[i][j]) for j in j_indices])

Answer 4

print(mat.__str__())

其中 mat 是指您的矩阵对象的变量

Answer 5

for i in A:
    print('\t'.join(map(str, i)))

Answer 6

using indices, for loops and formatting:

import numpy as np

def printMatrix(a):
   print "Matrix["+("%d" %a.shape[0])+"]["+("%d" %a.shape[1])+"]"
   rows = a.shape[0]
   cols = a.shape[1]
   for i in range(0,rows):
      for j in range(0,cols):
         print "%6.f" %a[i,j],
      print
   print      


def printMatrixE(a):
   print "Matrix["+("%d" %a.shape[0])+"]["+("%d" %a.shape[1])+"]"
   rows = a.shape[0]
   cols = a.shape[1]
   for i in range(0,rows):
      for j in range(0,cols):
         print("%6.3f" %a[i,j]),
      print
   print      


inf = float('inf')
A = np.array( [[0,1.,4.,inf,3],
     [1,0,2,inf,4],
     [4,2,0,1,5],
     [inf,inf,1,0,3],
     [3,4,5,3,0]])

printMatrix(A)    
printMatrixE(A)    

which yields the output:

Matrix[5][5]
     0      1      4    inf      3
     1      0      2    inf      4
     4      2      0      1      5
   inf    inf      1      0      3
     3      4      5      3      0

Matrix[5][5]
 0.000  1.000  4.000    inf  3.000
 1.000  0.000  2.000    inf  4.000
 4.000  2.000  0.000  1.000  5.000
   inf    inf  1.000  0.000  3.000
 3.000  4.000  5.000  3.000  0.000

Answer 7

In addition to the simple print answer, you can actually customise the print output through the use of the numpy.set_printoptions function.

Prerequisites:

>>> import numpy as np
>>> inf = np.float('inf')
>>> A = np.array([[0,1,4,inf,3],[1,0,2,inf,4],[4,2,0,1,5],[inf,inf,1,0,3],[3,4,5,3,0]])

The following option:

>>> np.set_printoptions(infstr="(infinity)")

Results in:

>>> print(A)
[[        0.         1.         4. (infinity)         3.]
 [        1.         0.         2. (infinity)         4.]
 [        4.         2.         0.         1.         5.]
 [(infinity) (infinity)         1.         0.         3.]
 [        3.         4.         5.         3.         0.]]

The following option:

>>> np.set_printoptions(formatter={'float': "\t{: 0.0f}\t".format})

Results in:

>>> print(A)
[[   0       1       4       inf     3  ]
 [   1       0       2       inf     4  ]
 [   4       2       0       1       5  ]
 [   inf     inf     1       0       3  ]
 [   3       4       5       3       0  ]]

If you just want to have a specific string output for a specific array, the function numpy.array2string is also available.