Again, after searching for the problem of converting nested lists with N levels into an N-dimensional array I found nothing, so here's my way around it:
import numpy as np
new_array=np.array([[[coord for coord in xk] for xk in xj] for xj in xi], ndmin=3) #this case for N=3
It's as simple as:
>>> lists = [[1, 2], [3, 4]]
>>> np.array(lists)
array([[1, 2],
[3, 4]])
As this is the top search on Google for converting a list of lists into a Numpy array, I'll offer the following despite the question being 4 years old:
>>> x = [[1, 2], [1, 2, 3], [1]]
>>> y = numpy.hstack(x)
>>> print(y)
[1 2 1 2 3 1]
When I first thought of doing it this way, I was quite pleased with myself because it's soooo simple. However, after timing it with a larger list of lists, it is actually faster to do this:
>>> y = numpy.concatenate([numpy.array(i) for i in x])
>>> print(y)
[1 2 1 2 3 1]
Note that @Bastiaan's answer #1 doesn't make a single continuous list, hence I added the concatenate
.
Anyway...I prefer the hstack
approach for it's elegant use of Numpy.
If your list of lists contains lists with varying number of elements then the answer of Ignacio Vazquez-Abrams will not work. Instead there are at least 3 options:
1) Make an array of arrays:
x=[[1,2],[1,2,3],[1]]
y=numpy.array([numpy.array(xi) for xi in x])
type(y)
>>><type 'numpy.ndarray'>
type(y[0])
>>><type 'numpy.ndarray'>
2) Make an array of lists:
x=[[1,2],[1,2,3],[1]]
y=numpy.array(x)
type(y)
>>><type 'numpy.ndarray'>
type(y[0])
>>><type 'list'>
3) First make the lists equal in length:
x=[[1,2],[1,2,3],[1]]
length = max(map(len, x))
y=numpy.array([xi+[None]*(length-len(xi)) for xi in x])
y
>>>array([[1, 2, None],
>>> [1, 2, 3],
>>> [1, None, None]], dtype=object)
I had a list of lists of equal length. Even then Ignacio Vazquez-Abrams
's answer didn't work out for me. I got a 1-D numpy array whose elements are lists. If you faced the same problem, you can use the below method
Use numpy.vstack
import numpy as np
np_array = np.empty((0,4), dtype='float')
for i in range(10)
row_data = ... # get row_data as list
np_array = np.vstack((np_array, np.array(row_data)))
Just use pandas
list(pd.DataFrame(listofstuff).melt().values)
this only works for a list of lists
if you have a list of list of lists you might want to try something along the lines of
lists(pd.DataFrame(listofstuff).melt().apply(pd.Series).melt().values)
>>> numpy.array([[1, 2], [3, 4]])
array([[1, 2], [3, 4]])
Source: Stackoverflow.com