Convert List into Numpy 2D Array

Question

How to convert a list into a numpy 2D ndarray. For example:

lst = [20, 30, 40, 50, 60]

Expected result:

>> print(arr)
>> array([[20],
       [30],
       [40],
       [50],
       [60]])

>> print(arr.shape)
>> (5, 1)

[[i] for i in x] or np.reshape([20, 30, 40, 50, 60], (-1,1)) ? — sushanth
– sushanth, Commented Aug 14, 2020 at 14:58
@Yoshi thanks for the suggestion but that doesn't quite seem to help me :( — Vishruth Subramanian
– Vishruth Subramanian, Commented Aug 14, 2020 at 15:00
I just missreaded the question. @Sushanth answer is what I think you are looking for :) — Yoshi
– Yoshi, Commented Aug 14, 2020 at 15:00

Ehsan · Accepted Answer · 2020-08-14 15:03:29Z

4

Convert it to array and reshape:

x = np.array(x).reshape(-1,1)

reshape adds the column structure. The -1 in reshape takes care of the correct number of rows it requires to reshape.

output:

[[20]
 [30]
 [40]
 [50]
 [60]]

answered Aug 14, 2020 at 15:03

Ehsan

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Comments

mathfux · Accepted Answer · 2020-08-14 15:09:24Z

2

If you need your calculations more effective, use numpy arrays instead of list comprehensions. This is an alternative way using array broadcasting

x = [20, 30, 40, 50, 60]
x = np.array(x) #convert your list to numpy array
result = x[:, None] #use numpy broadcasting

if you still need a list type at the end, you can convert your result efficiently using result.tolist()

answered Aug 14, 2020 at 15:09

mathfux

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That's not broadcasting. It's just indexing. None is an alias for np.newaxis

wprazuch · Accepted Answer · 2020-08-14 15:01:03Z

0

You may use a list comprehension and then convert it to numpy array:

import numpy as np

x = [20, 30, 40, 50, 60]

x_nested = [[item] for item in x]

x_numpy = np.array(x_nested)

answered Aug 14, 2020 at 15:01

wprazuch

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This is a much less efficient technique. When dealing with numerical data, get it into numpy as fast as possible, then manipulate it.