Adding appending numpy arrays

Question

I'm currently trying to append multiple Numpy arrays together. Basically, what I want to do is to start from a (1 x m) matrix (technically a vector), and end up with a (n x m) matrix. So going from n (1 x m) matrices (vectors) to one (n x m) matrix (If that makes any sense). The ultimate goal with this is to write the matrix into a csv-file with the numpy.savetxt() function so I'll end up with a csv-file with n columns of m length.

The problem with this is that numpy.append() appends the vectors together into a (1 x 2m) vector. So let's say a1 and a2 are Numpy arrays with 10000 elements each. I'll append a2 into a1 by using the append function and simultaneously creating a new array called a, which contains both a1 and a2.

a=np.append(a1, a2, axis=0)
a.shape
>>(20000,)

What I want instead is for the shape to be of the form

>>(2, 10000)

or more generally

>>(n, m)

What should I do? Please note, that I want to continue adding the vectors into the array. Thanks for your time!

What's the source of a1, a2, etc? Do they all exist at the start of this operation, or do you HAVE to generate a3 after a2 has been created and appended? — hpaulj
– hpaulj, Commented Jun 9, 2015 at 16:23
I think you need to play around more with np.array([...]) and np.reshape, and np.concatenate. Those are building blocks for all the proposed variations (vstack, column_stack, append). Look at Python code where available. — hpaulj
– hpaulj, Commented Jun 9, 2015 at 16:29

tmdavison · Accepted Answer · 2015-06-09 12:07:19Z

2

you can use the transpose of numpy.column_stack

For example:

import numpy as np

a=np.array([1,2,3,4,5])
b=np.array([9,8,7,6,5])
c=np.column_stack((a,b)).T

print c
>>> array([[1, 2, 3, 4, 5],
           [9, 8, 7, 6, 5]])

print a.shape,b.shape,c.shape
>>> (5,) (5,) (2, 5)

EDIT:

you can keep adding columns like so:

d=np.array([2,2,2,2,2])
c=np.column_stack((c.T,d)).T
print c
>>> array([[1, 2, 3, 4, 5],
           [9, 8, 7, 6, 5],
           [2, 2, 2, 2, 2]])
print c.shape
>>> (3, 5)

edited Jun 9, 2015 at 12:07

answered Jun 9, 2015 at 11:58

tmdavison

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3 Comments

P4nd4b0b3r1n0 Over a year ago

Hmmm. The problem I'm running into with this is that I can't keep adding columns to c, because of the shape problem. How do I keep adding these (1 x m) vectors into the (n x m) matrix inside a for-loop? Because at the moment, if I for example want to add an array d (shape (1 x 5)) into c, I'll get the shape error. ValueError: all the input array dimensions except for the concatenation axis must match exactly

tmdavison Over a year ago

you can add a new column with c=np.column_stack((c.T,d)).T. See my edit

hpaulj Over a year ago

He doesn't need to transpose the column_stack if he wants to create a csv with n columns.

farhawa · Accepted Answer · 2015-06-09 12:03:32Z

0

This should work

a=np.append(a1, a2, axis=0).reshape(2,10000)
a.shape
>>(2,10000)

answered Jun 9, 2015 at 12:03

farhawa

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Comments

plonser · Accepted Answer · 2015-06-09 13:03:04Z

0

In order to merge arrays vertically I would use np.vstack

import numpy as np

np.vstack((a1,a2))

However, from my point of view, numpy.array shouldn't be created using for loops and appending the new array to the old one. Instead, either you create first the whole numpy.array (nxm) and you write the data from the for loop into that array,

data = np.zeros((n,m))

for i in range(n):
    data[i] = ...

or you first create your array as an ordinary python list using append which you can transform at the end into an numpy.array.

data = []

for i in range(n):
    data.append(...)

data = np.asarray(data)

answered Jun 9, 2015 at 13:03

plonser

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2 Comments

hpaulj Over a year ago

If a1, a2, etc are all 1d arrays, then np.array([a1,a2,a3,..]) works just as well as vstack. That's what your last example is doing. vstack would be better for the incremental build (where one array is 2d, the other(s) 1d).

plonser Over a year ago

@hpaulj : I'm not sure what you mean ... I am just saying that I would use np.vstack instead of the transposed np.column_stack in the for loop. In the other part of my answer I only want to emphasize that the approach in general is not efficient from my (numpy) point of view. Instead, I would use one of the two options I propose ...

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