Numpy list of 1D Arrays to 2D Array

Question

I have a large list files that contain 2D numpy arrays pickled through numpy.save. I am trying to read the first column of each file and create a new 2D array.

I currently read each column using numpy.load with a mmap. The 1D arrays are now in a list.

col_list = []
for f in file_list:
    Temp = np.load(f,mmap_mode='r')
    col_list.append(Temp[:,0])

How can I convert this into a 2D array?

C. Yduqoli · Accepted Answer · 2018-01-26 06:55:19Z

73

You can use

numpy.stack(arrays, axis=0)

if you have an array of arrays. You can specify the axis in case you want to stack columns and not rows.

answered Jan 26, 2018 at 6:55

C. Yduqoli

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1 Comment

Avision Over a year ago

This should be the selected answer, or at least ranked higher. The regular way (with nd.array) doesn't work with lists of ndarrays

gofr1 · Accepted Answer · 2016-07-08 04:58:35Z

28

The array may be recreated:

a = np.array(a.tolist())

edited Jul 8, 2016 at 4:58

gofr1

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answered Jul 8, 2016 at 2:39

splendor

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2 Comments

danodonovan Over a year ago

This answer needn't be downvoted, it works in instances when @senshin 's answer won't work.

tnarik Over a year ago

This answer is only useful when you have an array of arrays, which is not a list of arrays.

senshin · Accepted Answer · 2014-01-24 01:30:14Z

24

You can just call np.array on the list of 1D arrays.

>>> import numpy as np
>>> arrs = [np.array([1,2,3]), np.array([4,5,6]), np.array([7,8,9])]
>>> arrs
[array([1, 2, 3]), array([4, 5, 6]), array([7, 8, 9])]
>>> arr2d = np.array(arrs)
>>> arr2d.shape
(3, 3)
>>> arr2d
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

answered Jan 24, 2014 at 1:30

senshin

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3 Comments

David Folkner Over a year ago

Thanks this works great. Since I wanted columns instead of rows I did have to do some transposes and optimize the direction of the data collection, but it works great.

Pinkesh Badjatiya Over a year ago

THis does not work for multidimensional array. Any solution to that?

Avision Over a year ago

Note that this doesn't work for lists of ndarrays. For a cleaner solution see the answer below using np.stack

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