What is the best way to divide a list into roughly equal parts? For example, if the list has 7 elements and is split it into 2 parts, we want to get 3 elements in o
This one provides chunks of length <= n, >= 0
def
chunkify(lst, n):
num_chunks = int(math.ceil(len(lst) / float(n))) if n < len(lst) else 1
return [lst[n*i:n*(i+1)] for i in range(num_chunks)]
for example
>>> chunkify(range(11), 3)
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10]]
>>> chunkify(range(11), 8)
[[0, 1, 2, 3, 4, 5, 6, 7], [8, 9, 10]]
this code works for me (Python3-compatible):
def chunkify(tab, num):
return [tab[i*num: i*num+num] for i in range(len(tab)//num+(1 if len(tab)%num else 0))]
example (for bytearray type, but it works for lists as well):
b = bytearray(b'\x01\x02\x03\x04\x05\x06\x07\x08')
>>> chunkify(b,3)
[bytearray(b'\x01\x02\x03'), bytearray(b'\x04\x05\x06'), bytearray(b'\x07\x08')]
>>> chunkify(b,4)
[bytearray(b'\x01\x02\x03\x04'), bytearray(b'\x05\x06\x07\x08')]
Implementation using numpy.linspace method.
Just specify the number of parts you want the array to be divided in to.The divisions will be of nearly equal size.
Example :
import numpy as np
a=np.arange(10)
print "Input array:",a
parts=3
i=np.linspace(np.min(a),np.max(a)+1,parts+1)
i=np.array(i,dtype='uint16') # Indices should be floats
split_arr=[]
for ind in range(i.size-1):
split_arr.append(a[i[ind]:i[ind+1]]
print "Array split in to %d parts : "%(parts),split_arr
Gives :
Input array: [0 1 2 3 4 5 6 7 8 9]
Array split in to 3 parts : [array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8, 9])]
def evenly(l, n):
len_ = len(l)
split_size = len_ // n
split_size = n if not split_size else split_size
offsets = [i for i in range(0, len_, split_size)]
return [l[offset:offset + split_size] for offset in offsets]
Example:
l = [a for a in range(97)]
should be consist of 10 parts, each have 9 elements except the last one.
Output:
[[0, 1, 2, 3, 4, 5, 6, 7, 8],
[9, 10, 11, 12, 13, 14, 15, 16, 17],
[18, 19, 20, 21, 22, 23, 24, 25, 26],
[27, 28, 29, 30, 31, 32, 33, 34, 35],
[36, 37, 38, 39, 40, 41, 42, 43, 44],
[45, 46, 47, 48, 49, 50, 51, 52, 53],
[54, 55, 56, 57, 58, 59, 60, 61, 62],
[63, 64, 65, 66, 67, 68, 69, 70, 71],
[72, 73, 74, 75, 76, 77, 78, 79, 80],
[81, 82, 83, 84, 85, 86, 87, 88, 89],
[90, 91, 92, 93, 94, 95, 96]]
Changing the code to yield n
chunks rather than chunks of n
:
def chunks(l, n):
""" Yield n successive chunks from l.
"""
newn = int(len(l) / n)
for i in xrange(0, n-1):
yield l[i*newn:i*newn+newn]
yield l[n*newn-newn:]
l = range(56)
three_chunks = chunks (l, 3)
print three_chunks.next()
print three_chunks.next()
print three_chunks.next()
which gives:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
[18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
[36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]
This will assign the extra elements to the final group which is not perfect but well within your specification of "roughly N equal parts" :-) By that, I mean 56 elements would be better as (19,19,18) whereas this gives (18,18,20).
You can get the more balanced output with the following code:
#!/usr/bin/python
def chunks(l, n):
""" Yield n successive chunks from l.
"""
newn = int(1.0 * len(l) / n + 0.5)
for i in xrange(0, n-1):
yield l[i*newn:i*newn+newn]
yield l[n*newn-newn:]
l = range(56)
three_chunks = chunks (l, 3)
print three_chunks.next()
print three_chunks.next()
print three_chunks.next()
which outputs:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
[19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]
[38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]
1>
import numpy as np
data # your array
total_length = len(data)
separate = 10
sub_array_size = total_length // separate
safe_separate = sub_array_size * separate
splited_lists = np.split(np.array(data[:safe_separate]), separate)
splited_lists[separate - 1] = np.concatenate(splited_lists[separate - 1],
np.array(data[safe_separate:total_length]))
splited_lists # your output
2>
splited_lists = np.array_split(np.array(data), separate)