Sort values of matrix in python

Question

What I have so far is:

dict={'A':[1,2,3], 'B':[2,5,4], 'C':[2,1,8]}
N=len(keys)
m=numpy.zeros(N,N)
for i in range(N):
    for j in range(N):
         m[i-1,j-1]=covariance(values[i-1],values[j-1])
         m[j-1,i-1]=covariance(values[j-1],values[i-1])
m=numpy.triu(m)

which gives me:

1   0.639  0.07
0     1    0.51
0     0      1

I dont have the column names or the row names yet. I would like something like this:

       A     B      C
A      1   0.639  0.07
B      0     1    0.51
C      0     0      1

Given this matrix, I would like to sort it in descending order by the value of the matrix so the output I would like is:

A & A: 1
B & B: 1
C & C: 1
A & B: 0.639
B & C: 0.51
A & C: 0.07
B & A: 0 #etc

From the output would like to save it into a csv file where the first column are the names and the second column are the corresponding scores

Thanks for reading.

You need to show what you have done before you ask a question. If you have no idea where to begin, here is a hint: read the data into a dictionary with the 'A & A' etc as the keys and then sort the dictioanry with the sorted() function using the key parameter. — ssm
– ssm, Commented Feb 21, 2014 at 0:58

Jaime · Accepted Answer · 2014-02-21 01:29:41Z

2

Call np.sort with the axis keyword argument set to None, then reverse it with slicing:

>>> a = np.array([[1, 0.639, 0.07], [0, 1, 0.51], [0, 0, 1]])
>>> a
array([[ 1.   ,  0.639,  0.07 ],
       [ 0.   ,  1.   ,  0.51 ],
       [ 0.   ,  0.   ,  1.   ]])
>>> np.sort(a, axis=None)[::-1]
array([ 1.   ,  1.   ,  1.   ,  0.639,  0.51 ,  0.07 ,  0.   ,  0.   ,  0.   ])

If you want to know where each value is coming from, then first use np.argsort, then unravel the flattened indices:

>>> idx = np.argsort(a, axis=None)[::-1]
>>> rows, cols = np.unravel_index(idx, a.shape)
>>> a_sorted = a[rows, cols]
>>> for r, c, v in zip(rows, cols, a_sorted):
...     print 'ABC'[r], '&', 'ABC'[c], ':', v
... 
C & C : 1.0
B & B : 1.0
A & A : 1.0
A & B : 0.639
B & C : 0.51
A & C : 0.07
C & B : 0.0
C & A : 0.0
B & A : 0.0

edited Feb 21, 2014 at 1:29

answered Feb 21, 2014 at 0:45

Jaime

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2 Comments

user3334418 Over a year ago

awesome. how would i go about pulling out the fact that 0.51 corresponds to "B&C"?

Aaron Hall Over a year ago

He wants the output he provided. :)

steinerkelvin · Accepted Answer · 2014-02-21 01:19:51Z

1

Starting from a numpy array, like this:

matrix = numpy.array( [ [ 1, 0.639, 0.07 ],
                        [ 0, 1,     0.51 ],
                        [ 0, 0,     1 ]  ] )

you can do like this:

indices = ["A", "B", "C", ]                     

values = []

for r,row in enumerate( matrix ):
    for c, cell in enumerate( row ):
        values.append( ("{} & {}".format( indices[r], indices[c] ), cell ) )

values.sort( key=lambda it: (-it[1], it[0]) )

for k,v in values:
    print "{}: {}".format(k,v)

OUTPUT:

A & A: 1.0
B & B: 1.0
C & C: 1.0
A & B: 0.639
B & C: 0.51
A & C: 0.07
B & A: 0.0
C & A: 0.0
C & B: 0.0

edited Feb 21, 2014 at 1:19

answered Feb 21, 2014 at 1:01

steinerkelvin

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3 Comments

user3334418 Over a year ago

Receiving an error: for k,v in values: ValueError:too many values to unpack

user3334418 Over a year ago

how would i go about taking the output and saving it into a csv file

steinerkelvin Over a year ago

@user3334418 csv module docs and the code.

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