python convert 2d array to 1d array

Question

I am new to python and need to do the following thing:

I've given an 1d array of vectors (so pretty much 2d).

My task is to create an 1d array that contains the length of each vector.

array([[0.  , 0.  ],
   [1.  , 0.  ],
   [1.  , 1.  ],
   [1.  , 0.75],
   [0.75, 1.  ],
   [0.5 , 1.  ]
   ...

should be converted to

array([0,
   1,
   1.4142,
   ...

I could easily do this in theory but I am not familiar with the inbuild commands of python and I am very happy if someone could tell me some inbuild commands of python that can do this.

What do you mean by length of each vector? How does `[1,1]` turn into `1.4142`? — Karl, Aug 30 '18 at 15:43
well the length of the vector (1,1)^T is sqrt(2) = 1.4142 (mathematically) — Finn Eggers, Aug 30 '18 at 15:44

score 4 · Answer 1 · answered Aug 30 '18 at 15:50

Using norm from np.linalg.norm:

import numpy as np

a = np.array([[0., 0.],
              [1., 0.],
              [1., 1.],
              [1., 0.75],
              [0.75, 1.],
              [0.5, 1.]])

print(np.linalg.norm(a, axis=1))

Output

[0.         1.         1.41421356 1.25       1.25       1.11803399]

jpp · Accepted Answer · 2018-08-30T16:00:27.380

With NumPy you can use vectorised operations:

A = np.array([[0.  , 0.  ],
              [1.  , 0.  ],
              [1.  , 1.  ],
              [1.  , 0.75],
              [0.75, 1.  ],
              [0.5 , 1.  ]])

res = np.sqrt(np.square(A).sum(1))

array([ 0.        ,  1.        ,  1.41421356,  1.25      ,  1.25      ,
        1.11803399])

Alternatively, if you prefer a less functional solution:

res = (A**2).sum(1)**0.5

pfctgeorge · Answer 3 · 2018-08-30T15:46:43.267

1

You can use the list comprehension. In Python 2,

print [(x[0]*x[0]+x[1]*x[1])**0.5 for x in arr]

where arr is your input

edited Aug 30 '18 at 15:46

answered Aug 30 '18 at 15:43

pfctgeorge

698
3
9

This looks like it just flattens the array. The OP doesn't want to simply flatten the array. He effectively wants to find the magnitude of each vector. – Kevin Aug 30 '18 at 15:45
Sorry, I misunderstood the problem at first. You can refresh my answer. – pfctgeorge Aug 30 '18 at 15:47
thank you. That works :) – Finn Eggers Aug 30 '18 at 15:48

dim · Answer 4 · 2018-08-30T16:00:08.497

1

You can try this:

import math
b = []
for el in arr:
    b.append(math.sqrt(el[0]**2 + el[1]**2))

print b

or you can do it even shorter:

b = [math.sqrt(el[0]**2 + el[1]**2) for el in arr]

where arr is the your array.

Here is and one more example with lambda:

b = map(lambda el: (el[0]**2 + el[1]**2)**0.5, arr)

edited Aug 30 '18 at 16:00

answered Aug 30 '18 at 15:46

dim

992
11
26

thank you that works :) forgot about the "ugly" append method in python... – Finn Eggers Aug 30 '18 at 15:47
@FinnEggers, You can do this and without `append`. See my second example. – dim Aug 30 '18 at 15:51

score 1 · Answer 5 · answered Aug 30 '18 at 15:49

1

you can iterate over your array to find the vector length:

array=[[0,0],[0,1],[1,0],[1,1]]
empty=[]
for (x,y) in array:
    empty.append((x**2+y**2)**0.5)
print(empty)

answered Aug 30 '18 at 15:49

Newmsy

13
3

score 1 · Answer 6 · answered Aug 30 '18 at 15:51

1

You can achieve it with hypotenuse np.hypot

np.hypot(array[:, 0], array[:, 1])

answered Aug 30 '18 at 15:51

taras

6,566
10
39
50

does this also work for higher dimensional vectors? – Finn Eggers Aug 30 '18 at 15:52
No, it works just for 2d vectors. See Daniel Mesejo's or jpp's answer for n-dimensional approach. – taras Aug 30 '18 at 15:53
Yeah I've seen that. I am using math.sqrt(np.sum(np.square(el))) now :) – Finn Eggers Aug 30 '18 at 15:55
@FinnEggers, as a side note, you can actually use `expr**0.5` instead of `math.sqrt(expr)` – taras Aug 30 '18 at 15:56
I assume it is not as efficient? – Finn Eggers Aug 30 '18 at 15:57
You can see it compared [here](https://stackoverflow.com/questions/327002/which-is-faster-in-python-x-5-or-math-sqrtx) – taras Aug 30 '18 at 15:58

python convert 2d array to 1d array

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