Inserting rows in an empty array using Numpy

Question

Hello im currently trying to convert this array

test=np.array([[0,0],[0,1],[1,1],[3,0]])

and using Manhattan distance to convert this array into this shape

[0., 1., 2., 3.] 
[1., 0., 1., 4.]
[2., 1., 0., 3.,
[3., 4., 3., 0.]

the code goes like this

list_x=[]
newarray=np.array([])
length=len(test)
for i in range(length):
    for j in range(length):
        print('i=',i)
        print('j=',j)
        var=sum(abs(a-b) for a,b in zip(test[i],test[j]))
        list_x.append(var)
    newarray= np.append(newarray,list_x,axis = 0) 
    list_x=[]

but the outcome of the code keeps giving me this:

array([0., 1., 2., 3., 1., 0., 1., 4., 2., 1., 0., 3., 3., 4., 3., 0.])

is there a problem in my np.append() that prevent to convert it to 4*4 shap array ?

yatu · Accepted Answer · 2020-08-26 09:36:42Z

1

You could go with scikit-learn's manhattan_distances to obtain all pairwise manhattan distances simplifying the above to a single function call:

from sklearn.metrics.pairwise import manhattan_distances

manhattan_distances(test)
array([[0., 1., 2., 3.],
       [1., 0., 1., 4.],
       [2., 1., 0., 3.],
       [3., 4., 3., 0.]])

If you wanted to obtain the distance with a for loop, I'd suggest you to use python lists instead. In order to end up with a nested list, generate an inner list with the distances of a row to the others, and append it to an outer list on each iteration:

out=[]
for i in range(length):
    new_row = []
    for j in range(length):
        var=sum(abs(a-b) for a,b in zip(test[i],test[j]))
        new_row.append(var)
    out.append(new_row)

print(out)
# [[0, 1, 2, 3], [1, 0, 1, 4], [2, 1, 0, 3], [3, 4, 3, 0]]

answered Aug 26, 2020 at 9:36

yatu

88.6k12 gold badges93 silver badges148 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

NaN Over a year ago

or use your original ... z = np.array([0., 1., 2., 3., 1., 0., 1., 4., 2., 1., 0., 3., 3., 4., 3., 0.]) and z.reshape(4,4)

Ehsan · Accepted Answer · 2020-08-26 09:45:29Z

0

Another way of doing it is using Scipy:

from scipy.spatial.distance import cdist
cdist(test,test,'cityblock')

output:

[[0. 1. 2. 3.]
 [1. 0. 1. 4.]
 [2. 1. 0. 3.]
 [3. 4. 3. 0.]]

Comparison:

#@ehsan's solution
def m1(test):
  return cdist(test,test,'cityblock')

#@yatu's solution
def m2(test):
  return manhattan_distances(test)

in_ = [np.random.randint(0,10,(n,2)) for n in [10,100,1000,10000]]

For large arrays they seem to have similar performance, but for smaller array (around 1000 rows) m1 seems faster.

edited Aug 26, 2020 at 9:45

answered Aug 26, 2020 at 9:40

Ehsan

12.5k2 gold badges24 silver badges36 bronze badges

3 Comments

noob Over a year ago

I know the cdist function but the object of this task is to right the Manhattan distance by hand and I have a problem only in the append function

Ehsan Over a year ago

@RAbeeq Then I misunderstood the problem. Do you have to use loops? you can still implement it with numpy without loops. Looping an array is not advised in general and is slow. If you need loops, I think Yatu's answer covers it. If you do not have to use loops, I can help implementing it with arrays.

noob Over a year ago

no need but thank your for you help I really thank you

Collectives™ on Stack Overflow

Inserting rows in an empty array using Numpy

2 Answers 2

1 Comment

3 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

1 Comment

3 Comments

Your Answer

Sign up or log in

Post as a guest

Related