I also don't recommend this, but you could use a numpy.chararray for this:
import numpy as np
arr = np.chararray((100, 12, 31, 24, 60, 60), itemsize=100)
arr[52, 7, 12, 12, 44, 54] = 'year 1950+52, 7th month, 12th day, 12th hour, 44th minute, 54th second'
I'm not exactly sure what your desired structure is, but the string I inserted into the array should explain the structure I proposed, and you can change it however you need. Note that itemsize limits how many characters you can put in at any index.
Again, with a caveat that this is not necessarily the most efficient thing in the world to do, but if you wish to store lists of ints and/or floats in that array (as per your comment), one way to do it would be to convert that list to strings, and then when retrieving it, re-transform back to a list:
data_to_insert = [1,2,3,4.5]
# store as string
arr[52, 7, 12, 12, 44, 54] = ','.join(map(str, data_to_insert))
# retrieve
arr[52, 7, 12, 12, 44, 54].decode('utf-8').split(',')
This should be pretty fast
Answer from sacuL on Stack OverflowI also don't recommend this, but you could use a numpy.chararray for this:
import numpy as np
arr = np.chararray((100, 12, 31, 24, 60, 60), itemsize=100)
arr[52, 7, 12, 12, 44, 54] = 'year 1950+52, 7th month, 12th day, 12th hour, 44th minute, 54th second'
I'm not exactly sure what your desired structure is, but the string I inserted into the array should explain the structure I proposed, and you can change it however you need. Note that itemsize limits how many characters you can put in at any index.
Again, with a caveat that this is not necessarily the most efficient thing in the world to do, but if you wish to store lists of ints and/or floats in that array (as per your comment), one way to do it would be to convert that list to strings, and then when retrieving it, re-transform back to a list:
data_to_insert = [1,2,3,4.5]
# store as string
arr[52, 7, 12, 12, 44, 54] = ','.join(map(str, data_to_insert))
# retrieve
arr[52, 7, 12, 12, 44, 54].decode('utf-8').split(',')
This should be pretty fast
Though i won't recommend it, A multi dimentional empty list can be created by using list comprehension:
> >>> a = 4 #Width of elements
> >>> b = 6 #Width of main list container
>>>>> c = 4
>>>>> d = 3
> >>> l = [[[[0 for k in range(d) ] for z in range(c)] for x in range(a)] for y in range(b)]
> >>> [[[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]]
Keep replacing 0 with list comprehensions to add more dimentions.
example code:
lst = [[]]
for x in a:
if x != '\n':
lst[-1].append(x)
else:
lst.append([])
print(lst)
output:
[[1, 2, 3, 4, 5], [6, 7, 8, 9, 0], [3, 45, 6, 7, 2]]
Using itertools.groupby would do the job (grouping by not being a linefeed):
import itertools
a = [1,2,3,4,5,'\n',6,7,8,9,0,'\n',3,45,6,7,2]
new_list = [list(x) for k,x in itertools.groupby(a,key=lambda x : x!='\n') if k]
print(new_list)
We compare the key truth value to filter out the occurrences of \n
result:
[[1, 2, 3, 4, 5], [6, 7, 8, 9, 0], [3, 45, 6, 7, 2]]
I'd suggest using np.full_like to choose the fill-value directly...
x = np.full_like((3, 1), None, dtype=object)
... of course the dtype you chose kind of defines what you mean by "empty"
I am guessing that by empty, you mean an array filled with zeros.
Use np.zeros() to create an array with zeros. np.empty() just allocates the array, so the numbers in there are garbage. It is provided as a way to even reduce the cost of setting the values to zero. But it is generally safer to use np.zeros().
I think your list comprehension versions were very close to working. You don't need to do any list multiplication (which doesn't work with empty lists anyway). Here's a working version:
>>> y = [[[] for i in range(n)] for i in range(n)]
>>> print y
[[[], [], [], []], [[], [], [], []], [[], [], [], []], [[], [], [], []]]
looks like the most easiest way is as follows:
def create_empty_array_of_shape(shape):
if shape: return [create_empty_array_of_shape(shape[1:]) for i in xrange(shape[0])]
it's work for me